File: test_dataloader.py

package info (click to toggle)
pytorch 1.13.1%2Bdfsg-4
  • links: PTS, VCS
  • area: main
  • in suites: bookworm
  • size: 139,252 kB
  • sloc: cpp: 1,100,274; python: 706,454; ansic: 83,052; asm: 7,618; java: 3,273; sh: 2,841; javascript: 612; makefile: 323; xml: 269; ruby: 185; yacc: 144; objc: 68; lex: 44
file content (2839 lines) | stat: -rw-r--r-- 117,835 bytes parent folder | download
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
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
1594
1595
1596
1597
1598
1599
1600
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1612
1613
1614
1615
1616
1617
1618
1619
1620
1621
1622
1623
1624
1625
1626
1627
1628
1629
1630
1631
1632
1633
1634
1635
1636
1637
1638
1639
1640
1641
1642
1643
1644
1645
1646
1647
1648
1649
1650
1651
1652
1653
1654
1655
1656
1657
1658
1659
1660
1661
1662
1663
1664
1665
1666
1667
1668
1669
1670
1671
1672
1673
1674
1675
1676
1677
1678
1679
1680
1681
1682
1683
1684
1685
1686
1687
1688
1689
1690
1691
1692
1693
1694
1695
1696
1697
1698
1699
1700
1701
1702
1703
1704
1705
1706
1707
1708
1709
1710
1711
1712
1713
1714
1715
1716
1717
1718
1719
1720
1721
1722
1723
1724
1725
1726
1727
1728
1729
1730
1731
1732
1733
1734
1735
1736
1737
1738
1739
1740
1741
1742
1743
1744
1745
1746
1747
1748
1749
1750
1751
1752
1753
1754
1755
1756
1757
1758
1759
1760
1761
1762
1763
1764
1765
1766
1767
1768
1769
1770
1771
1772
1773
1774
1775
1776
1777
1778
1779
1780
1781
1782
1783
1784
1785
1786
1787
1788
1789
1790
1791
1792
1793
1794
1795
1796
1797
1798
1799
1800
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
1811
1812
1813
1814
1815
1816
1817
1818
1819
1820
1821
1822
1823
1824
1825
1826
1827
1828
1829
1830
1831
1832
1833
1834
1835
1836
1837
1838
1839
1840
1841
1842
1843
1844
1845
1846
1847
1848
1849
1850
1851
1852
1853
1854
1855
1856
1857
1858
1859
1860
1861
1862
1863
1864
1865
1866
1867
1868
1869
1870
1871
1872
1873
1874
1875
1876
1877
1878
1879
1880
1881
1882
1883
1884
1885
1886
1887
1888
1889
1890
1891
1892
1893
1894
1895
1896
1897
1898
1899
1900
1901
1902
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
1914
1915
1916
1917
1918
1919
1920
1921
1922
1923
1924
1925
1926
1927
1928
1929
1930
1931
1932
1933
1934
1935
1936
1937
1938
1939
1940
1941
1942
1943
1944
1945
1946
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
2063
2064
2065
2066
2067
2068
2069
2070
2071
2072
2073
2074
2075
2076
2077
2078
2079
2080
2081
2082
2083
2084
2085
2086
2087
2088
2089
2090
2091
2092
2093
2094
2095
2096
2097
2098
2099
2100
2101
2102
2103
2104
2105
2106
2107
2108
2109
2110
2111
2112
2113
2114
2115
2116
2117
2118
2119
2120
2121
2122
2123
2124
2125
2126
2127
2128
2129
2130
2131
2132
2133
2134
2135
2136
2137
2138
2139
2140
2141
2142
2143
2144
2145
2146
2147
2148
2149
2150
2151
2152
2153
2154
2155
2156
2157
2158
2159
2160
2161
2162
2163
2164
2165
2166
2167
2168
2169
2170
2171
2172
2173
2174
2175
2176
2177
2178
2179
2180
2181
2182
2183
2184
2185
2186
2187
2188
2189
2190
2191
2192
2193
2194
2195
2196
2197
2198
2199
2200
2201
2202
2203
2204
2205
2206
2207
2208
2209
2210
2211
2212
2213
2214
2215
2216
2217
2218
2219
2220
2221
2222
2223
2224
2225
2226
2227
2228
2229
2230
2231
2232
2233
2234
2235
2236
2237
2238
2239
2240
2241
2242
2243
2244
2245
2246
2247
2248
2249
2250
2251
2252
2253
2254
2255
2256
2257
2258
2259
2260
2261
2262
2263
2264
2265
2266
2267
2268
2269
2270
2271
2272
2273
2274
2275
2276
2277
2278
2279
2280
2281
2282
2283
2284
2285
2286
2287
2288
2289
2290
2291
2292
2293
2294
2295
2296
2297
2298
2299
2300
2301
2302
2303
2304
2305
2306
2307
2308
2309
2310
2311
2312
2313
2314
2315
2316
2317
2318
2319
2320
2321
2322
2323
2324
2325
2326
2327
2328
2329
2330
2331
2332
2333
2334
2335
2336
2337
2338
2339
2340
2341
2342
2343
2344
2345
2346
2347
2348
2349
2350
2351
2352
2353
2354
2355
2356
2357
2358
2359
2360
2361
2362
2363
2364
2365
2366
2367
2368
2369
2370
2371
2372
2373
2374
2375
2376
2377
2378
2379
2380
2381
2382
2383
2384
2385
2386
2387
2388
2389
2390
2391
2392
2393
2394
2395
2396
2397
2398
2399
2400
2401
2402
2403
2404
2405
2406
2407
2408
2409
2410
2411
2412
2413
2414
2415
2416
2417
2418
2419
2420
2421
2422
2423
2424
2425
2426
2427
2428
2429
2430
2431
2432
2433
2434
2435
2436
2437
2438
2439
2440
2441
2442
2443
2444
2445
2446
2447
2448
2449
2450
2451
2452
2453
2454
2455
2456
2457
2458
2459
2460
2461
2462
2463
2464
2465
2466
2467
2468
2469
2470
2471
2472
2473
2474
2475
2476
2477
2478
2479
2480
2481
2482
2483
2484
2485
2486
2487
2488
2489
2490
2491
2492
2493
2494
2495
2496
2497
2498
2499
2500
2501
2502
2503
2504
2505
2506
2507
2508
2509
2510
2511
2512
2513
2514
2515
2516
2517
2518
2519
2520
2521
2522
2523
2524
2525
2526
2527
2528
2529
2530
2531
2532
2533
2534
2535
2536
2537
2538
2539
2540
2541
2542
2543
2544
2545
2546
2547
2548
2549
2550
2551
2552
2553
2554
2555
2556
2557
2558
2559
2560
2561
2562
2563
2564
2565
2566
2567
2568
2569
2570
2571
2572
2573
2574
2575
2576
2577
2578
2579
2580
2581
2582
2583
2584
2585
2586
2587
2588
2589
2590
2591
2592
2593
2594
2595
2596
2597
2598
2599
2600
2601
2602
2603
2604
2605
2606
2607
2608
2609
2610
2611
2612
2613
2614
2615
2616
2617
2618
2619
2620
2621
2622
2623
2624
2625
2626
2627
2628
2629
2630
2631
2632
2633
2634
2635
2636
2637
2638
2639
2640
2641
2642
2643
2644
2645
2646
2647
2648
2649
2650
2651
2652
2653
2654
2655
2656
2657
2658
2659
2660
2661
2662
2663
2664
2665
2666
2667
2668
2669
2670
2671
2672
2673
2674
2675
2676
2677
2678
2679
2680
2681
2682
2683
2684
2685
2686
2687
2688
2689
2690
2691
2692
2693
2694
2695
2696
2697
2698
2699
2700
2701
2702
2703
2704
2705
2706
2707
2708
2709
2710
2711
2712
2713
2714
2715
2716
2717
2718
2719
2720
2721
2722
2723
2724
2725
2726
2727
2728
2729
2730
2731
2732
2733
2734
2735
2736
2737
2738
2739
2740
2741
2742
2743
2744
2745
2746
2747
2748
2749
2750
2751
2752
2753
2754
2755
2756
2757
2758
2759
2760
2761
2762
2763
2764
2765
2766
2767
2768
2769
2770
2771
2772
2773
2774
2775
2776
2777
2778
2779
2780
2781
2782
2783
2784
2785
2786
2787
2788
2789
2790
2791
2792
2793
2794
2795
2796
2797
2798
2799
2800
2801
2802
2803
2804
2805
2806
2807
2808
2809
2810
2811
2812
2813
2814
2815
2816
2817
2818
2819
2820
2821
2822
2823
2824
2825
2826
2827
2828
2829
2830
2831
2832
2833
2834
2835
2836
2837
2838
2839
# Owner(s): ["module: dataloader"]

import math
import sys
import errno
import os
import ctypes
import faulthandler
import torch
import gc
import time
import signal
import unittest
import itertools
import warnings
import tempfile
import torch.utils.data.datapipes as dp
from torch import multiprocessing as mp
from torch.utils.data import (
    ChainDataset,
    ConcatDataset,
    DataLoader,
    DataLoader2,
    Dataset,
    IterableDataset,
    IterDataPipe,
    Subset,
    TensorDataset,
    communication,
    _utils
)
from torch.utils.data._utils import MP_STATUS_CHECK_INTERVAL
from torch.utils.data.dataset import random_split
from torch.utils.data.datapipes.iter import IterableWrapper
from torch.utils.data.datapipes.map import SequenceWrapper
from torch._utils import ExceptionWrapper
from torch.testing._internal.common_utils import (TestCase, run_tests, TEST_NUMPY, IS_WINDOWS,
                                                  IS_CI, NO_MULTIPROCESSING_SPAWN, skipIfRocm, slowTest,
                                                  load_tests, TEST_WITH_ASAN, TEST_WITH_TSAN, IS_SANDCASTLE,
                                                  IS_MACOS)


try:
    import psutil
    HAS_PSUTIL = True
except ImportError:
    HAS_PSUTIL = False
    err_msg = ("psutil not found. Some critical data loader tests relying on it "
               "(e.g., TestDataLoader.test_proper_exit) will not run.")
    if IS_CI:
        raise ImportError(err_msg) from None
    else:
        warnings.warn(err_msg)

try:
    import dill
    # XXX: By default, dill writes the Pickler dispatch table to inject its
    # own logic there. This globally affects the behavior of the standard library
    # pickler for any user who transitively depends on this module!
    # Undo this extension to avoid altering the behavior of the pickler globally.
    dill.extend(use_dill=False)
    HAS_DILL = True
except ImportError:
    HAS_DILL = False
skipIfNoDill = unittest.skipIf(not HAS_DILL, "no dill")


try:
    import numpy as np
    HAS_NUMPY = True
except ImportError:
    HAS_NUMPY = False
skipIfNoNumpy = unittest.skipIf(not HAS_NUMPY, "no NumPy")

# load_tests from torch.testing._internal.common_utils is used to automatically filter tests for
# sharding on sandcastle. This line silences flake warnings
load_tests = load_tests

# We cannot import TEST_CUDA from torch.testing._internal.common_cuda here, because if we do that,
# the TEST_CUDNN line from torch.testing._internal.common_cuda will be executed multiple times
# as well during the execution of this test suite, and it will cause
# CUDA OOM error on Windows.
TEST_CUDA = torch.cuda.is_available()
if TEST_CUDA:
    dev_name = torch.cuda.get_device_name(torch.cuda.current_device()).lower()
    IS_JETSON = 'xavier' in dev_name or 'nano' in dev_name or 'jetson' in dev_name or 'tegra' in dev_name
else:
    IS_JETSON = False

if not NO_MULTIPROCESSING_SPAWN:
    # We want to use `spawn` if able because some of our tests check that the
    # data loader terminiates gracefully. To prevent hanging in the testing
    # process, such data loaders are run in a separate subprocess.
    #
    # We also want to test the `pin_memory=True` configuration, thus `spawn` is
    # required to launch such processes and they initialize the CUDA context.
    #
    # Mixing different start method is a recipe for disaster (e.g., using a fork
    # `mp.Event` with a spawn `mp.Process` segfaults). So we set this globally
    # to avoid bugs.
    #
    # Get a multiprocessing context because some test / third party library will
    # set start_method when imported, and setting again triggers `RuntimeError`.
    mp = mp.get_context(method='spawn')


# 60s of timeout?
# Yes, in environments where physical CPU resources are shared, e.g., CI, the
# time for a inter-process communication can be highly varying.  With 15~17s of
# timeout, we have observed flakiness in some CI builds (see
# pytorch/pytorch#14501, pytorch/pytorch#16608).  We follow the CPython
# multiprocessing setup and set the timeout to 60s here:
#
# https://github.com/python/cpython/blob/e8113f51a8bdf33188ee30a1c038a298329e7bfa/Lib/test/_test_multiprocessing.py#L73
JOIN_TIMEOUT = 60.0  # seconds


supported_multiprocessing_contexts = [None] + list(torch.multiprocessing.get_all_start_methods())


@unittest.skipIf(
    TEST_WITH_TSAN,
    "Fails with TSAN with the following error: starting new threads after multi-threaded "
    "fork is not supported. Dying (set die_after_fork=0 to override)")
class TestDatasetRandomSplit(TestCase):
    def test_lengths_must_equal_dataset_size(self):
        with self.assertRaises(ValueError):
            random_split([1, 2, 3, 4], [1, 2])

    def test_splits_have_correct_size(self):
        splits = random_split([1, 2, 3, 4, 5, 6], [2, 4])
        self.assertEqual(len(splits), 2)
        self.assertEqual(len(splits[0]), 2)
        self.assertEqual(len(splits[1]), 4)

        splits = random_split([1, 2, 3, 4, 5, 6], [0.5, 0.5])
        self.assertEqual(len(splits), 2)
        self.assertEqual(len(splits[0]), 3)
        self.assertEqual(len(splits[1]), 3)

        # Odd size splits
        self.assertEqual(
            len(random_split(range(3), [0.5, 0.5], generator=torch.Generator().manual_seed(1))),
            2
        )

        # Odd sized round-robin splits
        splits = random_split(range(106), [0.1, 0.2, 0.3, 0.4],
                              generator=torch.Generator().manual_seed(1))
        self.assertEqual(len(splits[0]), 11)
        self.assertEqual(len(splits[1]), 22)
        self.assertEqual(len(splits[2]), 31)
        self.assertEqual(len(splits[3]), 42)


    def test_splits_are_mutually_exclusive(self):
        data = [5, 2, 3, 4, 1, 6]
        splits = random_split(data, [2, 4])
        all_values = []
        all_values.extend(list(splits[0]))
        all_values.extend(list(splits[1]))
        data.sort()
        all_values.sort()
        self.assertListEqual(data, all_values)

        splits = random_split(data, [0.33, 0.67])
        all_values = []
        all_values.extend(list(splits[0]))
        all_values.extend(list(splits[1]))
        data.sort()
        all_values.sort()
        self.assertListEqual(data, all_values)

        data = [1, 2, 3, 4]
        splits = random_split(data, [0.25, 0.75])
        all_values = []
        all_values.extend(list(splits[0]))
        all_values.extend(list(splits[1]))
        data.sort()
        all_values.sort()
        self.assertListEqual(data, all_values)

    def test_splits_indexing_type(self):
        r"""Indices generated by random_split
          should be of integer type
        """
        class CustomDataset():
            def __init__(self, test_object, custom_list):
                self.data = custom_list
                self.test_object = test_object

            def __getitem__(self, key):
                self.test_object.assertEqual(type(key), type(0))
                return self.data[key]

            def __len__(self):
                return len(self.data)

        x = [1, 2, 3, 4, 5]
        dataset = CustomDataset(self, x)
        dataset = random_split(dataset, [5])[0]
        data_loader = DataLoader(dataset)
        for batch in data_loader:
            pass

        # fractional splitting
        dataset = CustomDataset(self, x)
        dataset = random_split(dataset, [1.0])[0]
        data_loader = DataLoader(dataset)
        for batch in data_loader:
            pass

    def test_splits_reproducibility(self):
        self.assertEqual(
            [list(x) for x in random_split(range(10), [3, 7], generator=torch.Generator().manual_seed(1))],
            [[5, 6, 1], [2, 0, 8, 9, 3, 7, 4]],
        )
        self.assertEqual(
            random_split(range(100), [60, 40], generator=torch.Generator().manual_seed(42)),
            random_split(range(100), [60, 40], generator=torch.Generator().manual_seed(42)),
        )
        self.assertEqual(
            random_split(range(100), [0.5, 0.5], generator=torch.Generator().manual_seed(42)),
            random_split(range(100), [0.5, 0.5], generator=torch.Generator().manual_seed(42)),
        )
        self.assertEqual(
            random_split(range(100), [0.33, 0.33, 0.34], generator=torch.Generator().manual_seed(42)),
            random_split(range(100), [0.33, 0.33, 0.34], generator=torch.Generator().manual_seed(42)),
        )

    def test_incomplete_fractional_splits(self):
        with self.assertRaises(ValueError):
            # should raise since the sum of fractions is not 1
            random_split([1, 2, 3, 4], [0.1])

        with self.assertRaises(ValueError):
            # should raise since fraction > 1
            random_split([1, 2, 3, 4], [1.1])

    def test_splits_generator(self):
        # A random_split without a specific generator should affect the default one
        state = torch.get_rng_state()
        a = torch.rand(10)
        torch.set_rng_state(state)
        random_split(range(10), [5, 5])
        b = torch.rand(10)
        self.assertNotEqual(a, b)

        # A random_split with a specific generator should not affect the default one
        state = torch.get_rng_state()
        a = torch.rand(10)
        torch.set_rng_state(state)
        random_split(range(10), [5, 5], generator=torch.Generator().manual_seed(42))
        b = torch.rand(10)
        self.assertEqual(a, b)

    def test_slicing_of_subset_of_dataset(self):
        # Testing slicing a subset initialized with a dataset
        dataset = TensorDataset(torch.tensor([1, 2, 3, 4, 5]))
        subset_of_dataset = Subset(dataset, [0, 1, 2, 3, 4])
        self.assertEqual(subset_of_dataset[:], dataset[:])
        self.assertEqual(subset_of_dataset[1:2], dataset[1:2])
        self.assertEqual(subset_of_dataset[0:-1:2], dataset[0:-1:2])
        # Testing slicing of subset from random split
        subset1, subset2 = random_split(dataset, [3, 2])
        self.assertEqual(subset1[:], dataset[subset1.indices[:]])
        self.assertEqual(subset1[0:2], dataset[subset1.indices[0:2]])
        self.assertEqual(subset1[0:-1:2], dataset[subset1.indices[0:-1:2]])

    def test_slicing_of_subset_of_subset(self):
        # Testing slicing a subset initialized with a subset
        dataset = TensorDataset(torch.tensor([1, 2, 3, 4, 5]))
        subset_of_dataset = Subset(dataset, [0, 1, 2, 3, 4])
        subset_of_subset = Subset(subset_of_dataset, [0, 1, 2, 3, 4])
        self.assertEqual(subset_of_subset[:], dataset[:])
        self.assertEqual(subset_of_subset[0:2], dataset[0:2])
        self.assertEqual(subset_of_subset[0:-1:2], dataset[0:-1:2])
        # Testing slicing of subset of subset from random split
        subset1, subset2 = random_split(dataset, [4, 1])
        subset_of_subset1, subset_of_subset2 = random_split(subset1, [3, 1])
        idx = [subset1.indices[i] for i in subset_of_subset1.indices]
        self.assertEqual(subset_of_subset1[:], dataset[idx[:]])
        self.assertEqual(subset_of_subset1[0:2], dataset[idx[0:2]])
        self.assertEqual(subset_of_subset1[0:-1:2], dataset[idx[0:-1:2]])


class CUDACountingDataset(Dataset):
    def __init__(self, n):
        super(CUDACountingDataset, self).__init__()
        self.n = n

    def __getitem__(self, i):
        return torch.as_tensor(i, device='cuda')

    def __len__(self):
        return self.n


class CountingDataset(Dataset):
    def __init__(self, n):
        super(CountingDataset, self).__init__()
        self.n = n

    def __getitem__(self, i):
        return i

    def __len__(self):
        return self.n


class CountingIterableDataset(IterableDataset):
    def __init__(self, n):
        super(CountingIterableDataset, self).__init__()
        self.n = n

    def __iter__(self):
        return iter(range(self.n))

    def __len__(self):
        return self.n


@unittest.skipIf(
    TEST_WITH_TSAN,
    "Fails with TSAN with the following error: starting new threads after multi-threaded "
    "fork is not supported. Dying (set die_after_fork=0 to override)")
class TestTensorDataset(TestCase):

    def test_len(self):
        source = TensorDataset(torch.randn(15, 10, 2, 3, 4, 5), torch.randperm(15))
        self.assertEqual(len(source), 15)

    def test_getitem(self):
        t = torch.randn(15, 10, 2, 3, 4, 5)
        l = torch.randn(15, 10)
        source = TensorDataset(t, l)
        for i in range(15):
            self.assertEqual(t[i], source[i][0])
            self.assertEqual(l[i], source[i][1])

    def test_getitem_1d(self):
        t = torch.randn(15)
        l = torch.randn(15)
        source = TensorDataset(t, l)
        for i in range(15):
            self.assertEqual(t[i], source[i][0])
            self.assertEqual(l[i], source[i][1])

    def test_single_tensor(self):
        t = torch.randn(5, 10)
        source = TensorDataset(t)
        self.assertEqual(len(source), 5)
        for i in range(5):
            self.assertEqual(t[i], source[i][0])

    def test_many_tensors(self):
        t0 = torch.randn(5, 10, 2, 3, 4, 5)
        t1 = torch.randn(5, 10)
        t2 = torch.randn(5, 10, 2, 5)
        t3 = torch.randn(5, 10, 3, 7)
        source = TensorDataset(t0, t1, t2, t3)
        self.assertEqual(len(source), 5)
        for i in range(5):
            self.assertEqual(t0[i], source[i][0])
            self.assertEqual(t1[i], source[i][1])
            self.assertEqual(t2[i], source[i][2])
            self.assertEqual(t3[i], source[i][3])


@unittest.skipIf(
    TEST_WITH_TSAN,
    "Fails with TSAN with the following error: starting new threads after multi-threaded "
    "fork is not supported. Dying (set die_after_fork=0 to override)")
class TestConcatDataset(TestCase):

    def test_concat_two_singletons(self):
        result = ConcatDataset([[0], [1]])
        self.assertEqual(2, len(result))
        self.assertEqual(0, result[0])
        self.assertEqual(1, result[1])

    def test_concat_two_non_singletons(self):
        result = ConcatDataset([[0, 1, 2, 3, 4],
                                [5, 6, 7, 8, 9]])
        self.assertEqual(10, len(result))
        self.assertEqual(0, result[0])
        self.assertEqual(5, result[5])

    def test_concat_two_non_singletons_with_empty(self):
        # Adding an empty dataset somewhere is correctly handled
        result = ConcatDataset([[0, 1, 2, 3, 4],
                                [],
                                [5, 6, 7, 8, 9]])
        self.assertEqual(10, len(result))
        self.assertEqual(0, result[0])
        self.assertEqual(5, result[5])

    def test_concat_raises_index_error(self):
        result = ConcatDataset([[0, 1, 2, 3, 4],
                                [5, 6, 7, 8, 9]])
        with self.assertRaises(IndexError):
            # this one goes to 11
            result[11]

    def test_add_dataset(self):
        d1 = TensorDataset(torch.rand(7, 3, 28, 28), torch.rand(7))
        d2 = TensorDataset(torch.rand(7, 3, 28, 28), torch.rand(7))
        d3 = TensorDataset(torch.rand(7, 3, 28, 28), torch.rand(7))
        result = d1 + d2 + d3
        self.assertEqual(21, len(result))
        self.assertEqual(0, (d1[0][0] - result[0][0]).abs().sum())
        self.assertEqual(0, (d2[0][0] - result[7][0]).abs().sum())
        self.assertEqual(0, (d3[0][0] - result[14][0]).abs().sum())

    def test_iterable_dataset_err(self):
        d1 = TensorDataset(torch.rand(7, 3, 28, 28), torch.rand(7))
        it1 = CountingIterableDataset(5)
        it2 = CountingIterableDataset(10)

        with self.assertRaisesRegex(AssertionError, "does not support IterableDataset"):
            ConcatDataset([d1, it2, it1])

        with self.assertRaisesRegex(AssertionError, "does not support IterableDataset"):
            ConcatDataset([it2])

        with self.assertRaisesRegex(AssertionError, "does not support IterableDataset"):
            ConcatDataset([it1, d1])


# takes in dummy var so this can also be used as a `worker_init_fn`
def set_faulthander_if_available(_=None):
    faulthandler.enable(sys.__stderr__)
    if not IS_WINDOWS:
        # windows does not have faulthandler.register
        # chain=False prevents the default behavior of killing the process
        faulthandler.register(signal.SIGUSR1, file=sys.__stderr__, chain=False)


set_faulthander_if_available()

# Process `pid` must have called `set_faulthander_if_available`
def print_traces_of_all_threads(pid):
    if not IS_WINDOWS:
        # use the custom signal if available
        os.kill(pid, signal.SIGUSR1)
    else:
        # otherwise we can still use the handler given by faulthandler.enable()
        # at the cost of killing the process.
        os.kill(pid, signal.SIGSEGV)

    # wait in parent process to give subprocess some time to print
    time.sleep(5)


# The following `ErrorTrackingProcess` stores the first encountered exception in
# its `.exception` attribute.
# Inspired by https://stackoverflow.com/a/33599967
class ErrorTrackingProcess(mp.Process):

    # Why no *args?
    #   py2 doesn't support def fn(x, *args, key=val, **kwargs)
    # Setting disable_stderr=True may generate a lot of unrelated error outputs
    # but could be helpful for debugging.
    def __init__(self, disable_stderr=True, **kwargs):
        super(ErrorTrackingProcess, self).__init__(**kwargs)
        self._pconn, self._cconn = mp.Pipe()
        self._exception = None
        self.disable_stderr = disable_stderr

    def run(self):
        set_faulthander_if_available()
        if self.disable_stderr:
            # Disable polluting stderr with errors that are supposed to happen.
            with open(os.devnull, 'w') as devnull:
                os.dup2(devnull.fileno(), sys.stderr.fileno())
        try:
            super(ErrorTrackingProcess, self).run()
            self._cconn.send(None)
        except Exception:
            self._cconn.send(ExceptionWrapper(sys.exc_info()))
            raise

    def print_traces_of_all_threads(self):
        assert self.is_alive(), "can only use print_traces_of_all_threads if the process is alive"
        assert not self.disable_stderr, "do not disable stderr if you use print_traces_of_all_threads"
        # On platforms without `SIGUSR1`, `set_faulthander_if_available` sets
        # `faulthandler.enable()`, and `print_traces_of_all_threads` may kill
        # the process. So let's poll the exception first
        _ = self.exception
        print_traces_of_all_threads(self.pid)

    @property
    def exception(self):
        if self._pconn.poll():
            self._exception = self._pconn.recv()
        if self._exception is None:
            return None
        else:
            return self._exception.exc_type(self._exception.exc_msg)

    # ESRCH means that os.kill can't finds alive proc
    def send_signal(self, signum, ignore_ESRCH=False):
        try:
            os.kill(self.pid, signum)
        except OSError as e:
            if not ignore_ESRCH or e.errno != errno.ESRCH:
                raise


class ErrorDataset(Dataset):

    def __init__(self, size):
        self.size = size

    def __len__(self):
        return self.size


class SegfaultDataset(Dataset):

    def __init__(self, size):
        self.size = size

    def __getitem__(self, idx):
        return ctypes.string_at(0)

    def __len__(self):
        return self.size


class SleepDataset(Dataset):

    def __init__(self, size, sleep_sec):
        self.size = size
        self.sleep_sec = sleep_sec
        self.sleeped = False

    def __getitem__(self, idx):
        if not self.sleeped:
            time.sleep(self.sleep_sec)
            self.sleeped = True
        return idx

    def __len__(self):
        return self.size


class SeedDataset(Dataset):

    def __init__(self, size):
        self.size = size

    def __getitem__(self, idx):
        return torch.initial_seed()

    def __len__(self):
        return self.size


class WorkerSpecificIterableDataset(IterableDataset):
    def __init__(self, sizes_for_all_workers):
        self.sizes_for_all_workers = sizes_for_all_workers

    def __iter__(self):
        worker_info = torch.utils.data.get_worker_info()
        assert worker_info is not None
        return iter(range(self.sizes_for_all_workers[worker_info.id]))

    def __len__(self):
        return sum(self.sizes_for_all_workers)


# Inspired by https://stackoverflow.com/a/26703365
# If all workers will call `sync_once`, they will be blocked until all workers
# reach the call (i.e., acting like a barrier).
# This can be used to ensure that each worker at least processes one data.
class SynchronizedDataset(Dataset):

    def __init__(self, size, batch_size, num_workers):
        assert size >= num_workers * batch_size
        self.count = mp.Value('i', 0, lock=True)
        self.barrier = mp.Semaphore(0)
        self.num_workers = num_workers
        self.size = size

    def sync_once(self):
        with self.count.get_lock():
            self.count.value += 1
            if self.count.value == self.num_workers:
                self.barrier.release()
        self.barrier.acquire()
        self.barrier.release()

    def __getitem__(self, idx):
        raise NotImplementedError

    def __len__(self):
        return self.size


class EmptyTensorDataset(torch.utils.data.Dataset):
    def __init__(self, len):
        self.len = len

    def __len__(self):
        return self.len

    def __getitem__(self, any):
        return torch.empty(0)


class SynchronizedSeedDataset(SynchronizedDataset):
    def __getitem__(self, idx):
        self.sync_once()
        return torch.initial_seed()


def _test_timeout(persistent_workers):
    dataset = SleepDataset(10, 3)
    dataloader = DataLoader(dataset, batch_size=2, num_workers=2, timeout=1,
                            persistent_workers=persistent_workers)
    _ = next(iter(dataloader))


def _test_timeout_pin_memory(persistent_workers):
    dataset = SleepDataset(10, 3)
    dataloader = DataLoader(dataset, batch_size=2, num_workers=2, timeout=1, pin_memory=True,
                            persistent_workers=persistent_workers)
    _ = next(iter(dataloader))


def _test_large_sampler_indices(persistent_workers):
    # See
    #   test_large_sampler_indices
    #   https://github.com/pytorch/pytorch/issues/48666

    dataloader = torch.utils.data.DataLoader(
        EmptyTensorDataset(10000000),
        batch_size=40960,
        persistent_workers=persistent_workers,
        num_workers=1)

    it = iter(dataloader)

    for x in it:
        assert x.numel() == 0
        raise RuntimeError('My Error')


def disable_stderr(worker_id):
    r"""
    Avoids printing "ERROR: Unexpected segmentation fault encountered in worker."
    from workers. Since worker signal handler prints with low-level write(),
    this has to be done on OS level via dup.

    This is used as worker_init_fn for test_segfault.
    """
    sys.stderr.flush()  # flush library buffers that dup2 knows nothing about
    # Can't use a with-block because otherwise the fd will be closed when this
    # function ends.
    with open(os.devnull, 'w') as devnull:
        os.dup2(devnull.fileno(), sys.stderr.fileno())


def _test_segfault():
    dataset = SegfaultDataset(10)
    dataloader = DataLoader(dataset, batch_size=2, num_workers=2, worker_init_fn=disable_stderr)
    _ = next(iter(dataloader))


def _test_no_segfault():
    dataset = [1, 2, 3]
    num_threads = torch.get_num_threads()
    if num_threads < 4:
        torch.set_num_threads(4)
    else:
        torch.set_num_threads(num_threads)
    mp_ctx = torch.multiprocessing.get_context(method='fork')
    dataloader = DataLoader(dataset, num_workers=1, worker_init_fn=disable_stderr,
                            multiprocessing_context=mp_ctx)
    _ = next(iter(dataloader))


class TestProperExitDataset(Dataset):
    def __init__(self, size, error_event):
        self.size = size
        self.error_event = error_event

    def __len__(self):
        return self.size

    def __getitem__(self, idx):
        worker_info = torch.utils.data.get_worker_info()
        if self.error_event is not None and self.error_event.is_set() and \
                worker_info.id == worker_info.num_workers - 1:
            # only error in the last worker
            raise RuntimeError('Worker error')
        return torch.tensor([idx])


class TestProperExitIterableDataset(IterableDataset):
    def __init__(self, size, error_event):
        self.error_event = error_event
        self.size = size
        self.remaining = size

    def __len__(self):
        return self.size

    def __iter__(self):
        return self

    def __next__(self):
        worker_info = torch.utils.data.get_worker_info()
        if self.error_event is not None and self.error_event.is_set() and \
                worker_info.id == worker_info.num_workers - 1:
            # only error in the last worker
            raise RuntimeError('Worker error')
        self.remaining -= 1
        if self.remaining < 0:
            raise StopIteration
        return torch.tensor(-1000)


# See TestDataLoader.test_proper_exit for usage
def _test_proper_exit(is_iterable_dataset, use_workers, pin_memory, exit_method,
                      hold_iter_reference, loader_setup_event, tester_setup_event,
                      persistent_workers):
    num_workers = 2 if use_workers else 0

    if exit_method == 'worker_error' or exit_method == 'worker_kill':
        assert use_workers is True

    if exit_method == 'worker_error':
        worker_error_event = mp.Event()
    else:
        worker_error_event = None

    if is_iterable_dataset:
        ds = TestProperExitIterableDataset(7, worker_error_event)
    else:
        ds = TestProperExitDataset(12, worker_error_event)

    loader = DataLoader(ds, batch_size=1, shuffle=False,
                        num_workers=num_workers, pin_memory=pin_memory,
                        worker_init_fn=set_faulthander_if_available,
                        persistent_workers=persistent_workers)

    error_it = 2

    if use_workers:
        # 2 is the magical per-worker prefetch number...
        # FIXME: change this after the number becomes configurable.
        if is_iterable_dataset:
            assert len(ds) * num_workers > (error_it + 2 + 1)
        else:
            assert len(loader) > (error_it + 2 + 1) * num_workers
    else:
        if is_iterable_dataset:
            assert len(ds) > error_it + 1
        else:
            assert len(loader) > error_it + 1

    it = iter(loader)
    if use_workers:
        workers = it._workers

    def kill_pid(pid):
        psutil_p = psutil.Process(pid)
        psutil_p.kill()
        psutil_p.wait(JOIN_TIMEOUT)
        assert not psutil_p.is_running()

    for i, _ in enumerate(it):
        if i == 0:
            if not hold_iter_reference:
                del it
                del loader
            loader_setup_event.set()
            tester_setup_event.wait()
            # ensure that the workers are still alive
            if use_workers:
                for w in workers:
                    assert w.is_alive()
            if worker_error_event is not None:
                worker_error_event.set()

        if i == error_it:
            if exit_method == 'loader_error':
                raise RuntimeError('Loader error')
            elif exit_method == 'loader_kill':
                kill_pid(os.getpid())
            elif exit_method == 'worker_kill':
                kill_pid(workers[-1].pid)  # kill last worker

    if not hold_iter_reference:
        # Tries to trigger the __del__ clean-up rather than the automatic
        # exiting of daemonic children. Technically it should be automatically
        # triggered, but I don't want to rely on the implementation detail of
        # Python gc.
        gc.collect()


class TestWorkerInfoDataset(SynchronizedDataset):
    def __getitem__(self, idx):
        self.sync_once()
        return torch.tensor(self.value)


# Should be used as worker_init_fn with TestWorkerInfoDataset.
# See _test_get_worker_info below for usage.
def _test_worker_info_init_fn(worker_id):
    worker_info = torch.utils.data.get_worker_info()
    assert worker_id == worker_info.id, "worker_init_fn and worker_info should have consistent id"
    assert worker_id < worker_info.num_workers, "worker_init_fn and worker_info should have valid id"
    assert worker_info.seed == torch.initial_seed(), "worker_init_fn and worker_info should have consistent seed"
    dataset = worker_info.dataset
    assert isinstance(dataset, TestWorkerInfoDataset), "worker_info should have correct dataset copy"
    assert not hasattr(dataset, 'value'), "worker_info should have correct dataset copy"
    # test that WorkerInfo attributes are read-only
    try:
        worker_info.id = 3999
    except RuntimeError as e:
        assert str(e) == "Cannot assign attributes to WorkerInfo objects"
    try:
        worker_info.a = 3
    except RuntimeError as e:
        assert str(e) == "Cannot assign attributes to WorkerInfo objects"
    for k in ['id', 'num_workers', 'seed', 'dataset']:
        assert "{}=".format(k) in repr(worker_info)
    dataset.value = [worker_id, os.getpid()]


def _test_get_worker_info():
    # get_worker_info returns None in main proc
    assert torch.utils.data.get_worker_info() is None
    num_workers = 2
    batch_size = 2
    dataset = TestWorkerInfoDataset(6, batch_size, num_workers)
    dataloader = DataLoader(dataset, batch_size=batch_size,
                            num_workers=num_workers,
                            worker_init_fn=_test_worker_info_init_fn)
    it = iter(dataloader)
    data = []
    for d in it:
        data.append(d)
    worker_pids = [w.pid for w in it._workers]
    data = torch.cat(data, 0)
    for d in data:
        # each `d` is a [worker_id, worker_pid] pair, which is set in
        # _test_worker_info_init_fn
        assert d[1] == worker_pids[d[0]]
    # get_worker_info returns None in main proc after data loading
    assert torch.utils.data.get_worker_info() is None
    # main proc dataset was never assigned this attribute
    assert not hasattr(dataset, 'value')
    try:
        _ = dataset[0]
    except AttributeError:
        return
    raise RuntimeError('Expected AttributeError')


# test custom init function
def init_fn(worker_id):
    torch.manual_seed(12345)


# used with test_error_in_init
class ErrorIterableDataset(IterableDataset):
    def __iter__(self):
        raise RuntimeError("Error in __iter__")


# used with test_error_in_init
def error_worker_init_fn(_):
    raise RuntimeError("Error in worker_init_fn")


class BulkLoadingDataset(Dataset):
    def __init__(self, length):
        self.length = length

    def __getitem__(self, indices):
        assert isinstance(indices, (list, tuple))
        return torch.as_tensor(indices)

    def __len__(self):
        return self.length


class BulkLoadingSampler(torch.utils.data.Sampler):
    def __init__(self, dataset, batch_size):
        self.dataset = dataset
        self.batch_size = batch_size

    def __iter__(self):
        for x in torch.randperm(len(self.dataset)).split(self.batch_size):
            yield x.tolist()

    def __len__(self):
        return int(math.ceil(len(self.dataset) / float(self.batch_size)))


class TestMultiEpochDataset(IterableDataset):
    def __init__(self, length):
        self.length = length

    def __iter__(self):
        worker_info = torch.utils.data.get_worker_info()
        assert worker_info is not None
        worker_id = worker_info.id
        for idx in range(self.length // worker_info.num_workers):
            yield worker_id

    def __len__(self):
        return self.length


class CustomList(list):
    pass


class CustomDict(dict):
    pass


def row_processor(row):
    return np.add(row, 1)


def filter_len(row):
    return len(row) == 4


@unittest.skipIf(
    TEST_WITH_TSAN,
    "Fails with TSAN with the following error: starting new threads after multi-threaded "
    "fork is not supported. Dying (set die_after_fork=0 to override)")
@unittest.skipIf(
    TEST_WITH_ASAN,
    "DataLoader tests hang in ASAN, see: https://github.com/pytorch/pytorch/issues/66223")
class TestDataLoader(TestCase):

    def setUp(self):
        super(TestDataLoader, self).setUp()
        self.data = torch.randn(100, 2, 3, 5)
        self.labels = torch.randperm(50).repeat(2)
        self.dataset = TensorDataset(self.data, self.labels)
        self.persistent_workers = False

    def _get_data_loader(self, dataset, **kwargs):
        persistent_workers = kwargs.get('persistent_workers', self.persistent_workers)
        if persistent_workers and kwargs.get('num_workers', 0) == 0:
            persistent_workers = False
        kwargs['persistent_workers'] = persistent_workers
        return DataLoader(dataset, **kwargs)

    def _test_sequential(self, loader):
        batch_size = loader.batch_size
        if batch_size is None:
            for idx, (sample, target) in enumerate(loader):
                self.assertEqual(sample, self.data[idx])
                self.assertEqual(target, self.labels[idx])
            self.assertEqual(idx, len(self.dataset) - 1)
        else:
            for i, (sample, target) in enumerate(loader):
                idx = i * batch_size
                self.assertEqual(sample, self.data[idx:idx + batch_size])
                self.assertEqual(target, self.labels[idx:idx + batch_size])
            self.assertEqual(i, math.floor((len(self.dataset) - 1) / batch_size))

    def _test_shuffle(self, loader):
        found_data = {i: 0 for i in range(self.data.size(0))}
        found_labels = {i: 0 for i in range(self.labels.size(0))}
        batch_size = loader.batch_size
        if batch_size is None:
            for i, (batch_samples, batch_targets) in enumerate(loader):
                sample, target = (batch_samples, batch_targets)
                for data_point_idx, data_point in enumerate(self.data):
                    if data_point.eq(sample).all():
                        self.assertFalse(found_data[data_point_idx])
                        found_data[data_point_idx] += 1
                        break
                self.assertEqual(target, self.labels[data_point_idx])
                found_labels[data_point_idx] += 1
                self.assertEqual(sum(found_data.values()), (i + 1))
                self.assertEqual(sum(found_labels.values()), (i + 1))
            self.assertEqual(i, (len(self.dataset) - 1))
        else:
            for i, (batch_samples, batch_targets) in enumerate(loader):
                for sample, target in zip(batch_samples, batch_targets):
                    for data_point_idx, data_point in enumerate(self.data):
                        if data_point.eq(sample).all():
                            self.assertFalse(found_data[data_point_idx])
                            found_data[data_point_idx] += 1
                            break
                    self.assertEqual(target, self.labels[data_point_idx])
                    found_labels[data_point_idx] += 1
                self.assertEqual(sum(found_data.values()), (i + 1) * batch_size)
                self.assertEqual(sum(found_labels.values()), (i + 1) * batch_size)
            self.assertEqual(i, math.floor((len(self.dataset) - 1) / batch_size))

    def _test_error(self, loader):
        it = iter(loader)
        errors = 0
        while True:
            try:
                next(it)
            except NotImplementedError:
                errors += 1
            except StopIteration:
                self.assertEqual(errors,
                                 math.ceil(float(len(loader.dataset)) / loader.batch_size))
                return

    def test_error_in_init(self):
        for num_workers in [0, 2]:
            loader = self._get_data_loader(ErrorIterableDataset(), num_workers=num_workers)
            with self.assertRaisesRegex(RuntimeError, 'Error in __iter__'):
                list(iter(loader))

        loader = self._get_data_loader(self.dataset, num_workers=2, worker_init_fn=error_worker_init_fn)
        with self.assertRaisesRegex(RuntimeError, 'Error in worker_init_fn'):
            list(iter(loader))

    def test_typing(self):
        from typing import List
        # Make sure there is no TypeError

        class SomeDatasetClass(Dataset[List[torch.Tensor]]):
            pass

        def _create_dataloader(is_train: bool) -> DataLoader[List[torch.Tensor]]:
            pass

    @unittest.skipIf(IS_SANDCASTLE, "subprocess doesn't work in FB internal CI")
    @unittest.skipIf(IS_WINDOWS, "No 'resource' module on Windows")
    def test_fd_limit_exceeded(self):
        # See NOTE [ DataLoader on Linux and open files limit ]
        import subprocess
        subprocess.check_output([sys.executable, '-c', """\
import torch
import resource
from torch.utils.data import DataLoader, IterableDataset

class RandomDataset(IterableDataset):
    def __init__(self, len, size):
        super(RandomDataset).__init__()
        self.len = len
        self.size = size

    def __iter__(self):
        return self

    def __next__(self):
        if self.len <= 0:
            raise StopIteration
        self.len -= 1
        return torch.randn(self.size)

try:
    keep_fds_alive = []
    resource.setrlimit(resource.RLIMIT_NOFILE, (100, 100))
    for random_t in DataLoader(RandomDataset(200, (2,2)), multiprocessing_context="fork",
                               num_workers=1):
      random_t.max(dim=0)
      keep_fds_alive.append(random_t)
except RuntimeError as e:
    assert "ulimit -n" in str(e)
    assert "set_sharing_strategy" in str(e)
"""])

    def test_invalid_assign_after_init(self):
        dl = self._get_data_loader(self.dataset)
        for attr in ('batch_size', 'sampler', 'batch_sampler', 'drop_last', 'dataset'):
            def fn():
                setattr(dl, attr, {})

            self.assertRaises(ValueError, fn)

    def test_sequential_nonbatch(self):
        self._test_sequential(self._get_data_loader(self.dataset, batch_size=None))

    def test_sequential_batch(self):
        self._test_sequential(self._get_data_loader(self.dataset))
        self._test_sequential(self._get_data_loader(self.dataset, batch_size=2))

    def test_bulk_loading_nobatch(self):
        n = 35
        bs = 4
        ds = BulkLoadingDataset(n)
        sampler = BulkLoadingSampler(ds, batch_size=4)

        for num_workers in [0, 4]:
            dl = self._get_data_loader(ds, num_workers=num_workers, batch_size=None, sampler=sampler, pin_memory=TEST_CUDA)
            self.assertFalse(dl._auto_collation)
            samples = list(dl)
            self.assertEqual(samples[0].is_pinned(), TEST_CUDA)
            self.assertEqual(set(torch.cat(samples, 0).tolist()), set(range(n)))

    def test_growing_dataset(self):
        dataset = [torch.ones(4) for _ in range(4)]
        dataloader_seq = self._get_data_loader(dataset, shuffle=False)
        dataloader_shuffle = self._get_data_loader(dataset, shuffle=True)
        dataset.append(torch.ones(4))
        self.assertEqual(len(dataloader_seq), 5)
        self.assertEqual(len(dataloader_shuffle), 5)

    @unittest.skipIf(not TEST_CUDA, "CUDA unavailable")
    def test_sequential_pin_memory(self):
        loader = self._get_data_loader(self.dataset, batch_size=2, pin_memory=True)
        for input, target in loader:
            self.assertTrue(input.is_pinned())
            self.assertTrue(target.is_pinned())

    def test_multiple_dataloaders(self):
        for multiprocessing_context in supported_multiprocessing_contexts:
            loader1_it = iter(self._get_data_loader(self.dataset, num_workers=1))
            loader2_it = iter(self._get_data_loader(self.dataset, num_workers=2, multiprocessing_context=multiprocessing_context))
            next(loader1_it)
            next(loader1_it)
            next(loader2_it)
            next(loader2_it)
            next(loader1_it)
            next(loader2_it)
            del loader1_it
            del loader2_it

    def test_segfault(self):
        p = ErrorTrackingProcess(target=_test_segfault)
        p.start()
        p.join(JOIN_TIMEOUT)
        try:
            self.assertFalse(p.is_alive())
            self.assertNotEqual(p.exitcode, 0)
            if IS_WINDOWS:
                self.assertIsInstance(p.exception, OSError)
                self.assertRegex(str(p.exception), r'access violation reading ')
            else:
                self.assertIsInstance(p.exception, RuntimeError)
                self.assertRegex(str(p.exception), r'DataLoader worker \(pid \d+\) is killed by signal: ')
        finally:
            p.terminate()

    # Tests if the child process forked by the DataLoader segfaults due to having more than 3 threads
    # in the parent process after at least one set_num_threads invocation in the parent process.
    # After forking, set_num_threads(1) in the child process entails handling some inherited data-structures
    # of the Caffe2 thread-pool of the parent process, culminating in a segfault.
    # Reference: https://github.com/pytorch/pytorch/issues/54752
    @unittest.skipIf(IS_WINDOWS, "Needs fork")
    def test_no_segfault(self):
        p = ErrorTrackingProcess(target=_test_no_segfault)
        p.start()
        p.join(JOIN_TIMEOUT)
        try:
            self.assertFalse(p.is_alive())
            if p.exception:
                self.assertIsInstance(p.exception, RuntimeError)
                self.assertRegex(str(p.exception), r'DataLoader worker \(pid \d+\) is killed by signal: ')
                self.fail("Segfault occurred in worker process after fork")
        finally:
            p.terminate()

    def test_timeout(self):
        if TEST_CUDA and not NO_MULTIPROCESSING_SPAWN:
            # This test runs in a subprocess, which can only initialize CUDA with spawn.
            # _test_timeout_pin_memory with pin_memory=True initializes CUDA when the iterator is
            # constructed.
            targets = (_test_timeout, _test_timeout_pin_memory)
        else:
            targets = (_test_timeout,)
        for target in targets:
            p = ErrorTrackingProcess(target=target, args=(self.persistent_workers,))
            p.start()
            p.join(JOIN_TIMEOUT)
            try:
                self.assertFalse(p.is_alive())
                self.assertNotEqual(p.exitcode, 0)
                self.assertIsInstance(p.exception, RuntimeError)
                self.assertRegex(str(p.exception), r'DataLoader timed out after \d+ seconds')
            finally:
                p.terminate()

    def test_large_sampler_indices(self):
        # Test that the data loader cleanly exit when the process errors
        #   1. having an reference to the iterator
        #   2. using a sampler that yields big elements s.t. _index_queues putters block
        #
        # More context: https://github.com/pytorch/pytorch/issues/48666

        p = ErrorTrackingProcess(target=_test_large_sampler_indices, args=(self.persistent_workers,))
        p.start()
        p.join(JOIN_TIMEOUT)
        try:
            self.assertFalse(p.is_alive())
            self.assertNotEqual(p.exitcode, 0)
            self.assertIsInstance(p.exception, RuntimeError)
            self.assertRegex(str(p.exception), r'My Error')
        finally:
            p.terminate()

    def test_invalid_ctor_args_combinations(self):
        # general
        with self.assertRaisesRegex(ValueError, "num_workers option should be non-negative"):
            self._get_data_loader(self.dataset, num_workers=-1)
        with self.assertRaisesRegex(ValueError, "timeout option should be non-negative"):
            self._get_data_loader(self.dataset, timeout=-1)

        # disable auto-batching
        with self.assertRaisesRegex(ValueError,
                                    "batch_size=None option disables auto-batching and is mutually exclusive"):
            self._get_data_loader(self.dataset, batch_size=None, drop_last=True)

        valid_ctx = list(torch.multiprocessing.get_all_start_methods())[-1]
        with self.assertRaisesRegex(ValueError, r"multi-process loading \(num_workers > 0\), but got"):
            self._get_data_loader(self.dataset, num_workers=0, multiprocessing_context=valid_ctx)
        with self.assertRaisesRegex(ValueError, "should specify a valid start method in"):
            self._get_data_loader(self.dataset, num_workers=1, multiprocessing_context='bad')
        with self.assertRaisesRegex(TypeError, "multiprocessing_context option should be a valid context "):
            self._get_data_loader(self.dataset, num_workers=1, multiprocessing_context=object())

        # map-style
        sampler = torch.utils.data.SequentialSampler(self.dataset)
        batch_sampler = torch.utils.data.BatchSampler(sampler, 3, False)
        with self.assertRaisesRegex(ValueError, "sampler option is mutually exclusive with shuffle"):
            self._get_data_loader(self.dataset, batch_size=11, sampler=sampler, shuffle=True)
        with self.assertRaisesRegex(ValueError, "sampler option is mutually exclusive with shuffle"):
            self._get_data_loader(self.dataset, batch_sampler=batch_sampler, sampler=sampler, shuffle=True)
        with self.assertRaisesRegex(ValueError, "sampler option is mutually exclusive with shuffle"):
            self._get_data_loader(self.dataset, batch_sampler=batch_sampler, sampler=sampler, shuffle=3)
        with self.assertRaisesRegex(ValueError, "batch_sampler option is mutually exclusive with"):
            self._get_data_loader(self.dataset, batch_size=11, batch_sampler=batch_sampler)
        with self.assertRaisesRegex(ValueError, "batch_sampler option is mutually exclusive with"):
            self._get_data_loader(self.dataset, shuffle=True, batch_sampler=batch_sampler)
        with self.assertRaisesRegex(ValueError, "batch_sampler option is mutually exclusive with"):
            self._get_data_loader(self.dataset, drop_last=True, batch_sampler=batch_sampler)
        with self.assertRaisesRegex(ValueError, "batch_sampler option is mutually exclusive with"):
            self._get_data_loader(self.dataset, drop_last=3, batch_sampler=batch_sampler)

        # iterable-style
        dataset = CountingIterableDataset(20)
        with self.assertRaisesRegex(ValueError, "DataLoader with IterableDataset: expected unspecified shuffle"):
            self._get_data_loader(dataset, shuffle=True)
        with self.assertRaisesRegex(ValueError, "DataLoader with IterableDataset: expected unspecified shuffle"):
            self._get_data_loader(dataset, shuffle=3)
        with self.assertRaisesRegex(ValueError, "DataLoader with IterableDataset: expected unspecified sampler"):
            self._get_data_loader(dataset, sampler=torch.utils.data.SequentialSampler(dataset))
        with self.assertRaisesRegex(ValueError, "DataLoader with IterableDataset: expected unspecified sampler"):
            self._get_data_loader(dataset, sampler=3)
        with self.assertRaisesRegex(ValueError, "DataLoader with IterableDataset: expected unspecified batch_sampler"):
            self._get_data_loader(dataset, batch_sampler=torch.utils.data.BatchSampler(
                torch.utils.data.SequentialSampler(dataset), 3, False))
        with self.assertRaisesRegex(ValueError, "DataLoader with IterableDataset: expected unspecified batch_sampler"):
            self._get_data_loader(dataset, batch_sampler=3)

    def test_builtin_collection_conversion(self):
        for coll_ty in (list, tuple):
            for num_workers in (0, 1):
                # map-style dataset
                dataset = CountingDataset(20)
                # no auto-batching
                fetched = coll_ty(self._get_data_loader(dataset, batch_size=None, num_workers=num_workers))
                self.assertEqual(fetched, coll_ty(range(20)))
                # auto-batching
                fetched = coll_ty(self._get_data_loader(dataset, batch_size=2, num_workers=num_workers))
                self.assertEqual(fetched, coll_ty(torch.tensor([i, i + 1]) for i in range(0, 20, 2)))

                # iterable-style dataset
                dataset = CountingIterableDataset(20)
                # no auto-batching
                fetched = coll_ty(self._get_data_loader(dataset, batch_size=None, num_workers=num_workers))
                self.assertEqual(fetched, coll_ty(range(20)))
                # auto-batching
                # this IterableDataset isn't configured for each worker, so for
                # the equality test below to be valid, we cannot have more than 1 workers.
                assert num_workers in [0, 1], "invalid test"
                fetched = coll_ty(self._get_data_loader(dataset, batch_size=2, num_workers=num_workers))
                self.assertEqual(fetched, coll_ty(torch.tensor([i, i + 1]) for i in range(0, 20, 2)))

    def test_iterable_style_dataset(self):
        # [no auto-batching] single process loading
        dataset = CountingIterableDataset(20)
        dataloader = self._get_data_loader(dataset, batch_size=None)
        fetched = list(dataloader)
        self.assertEqual(len(fetched), 20)
        for i, d in enumerate(fetched):
            # non-batched should not convert ints into tensors
            self.assertIsInstance(d, int)
            self.assertEqual(d, i)
        # DataLoader should match len of the iterable-style dataset (if implemented)
        self.assertEqual(len(dataloader), len(dataset))

        # [no auto-batching] multiprocessing loading
        num_workers = 3
        sizes_for_all_workers = [0, 4, 20]
        expected = sorted(sum((list(range(s)) for s in sizes_for_all_workers), []))
        assert len(sizes_for_all_workers) == num_workers, 'invalid test case'
        for prefetch_factor in [2, 3, 4]:
            dataset = WorkerSpecificIterableDataset(sizes_for_all_workers)
            dataloader = self._get_data_loader(dataset, num_workers=num_workers, batch_size=None,
                                               worker_init_fn=set_faulthander_if_available,
                                               prefetch_factor=prefetch_factor)
            dataloader_iter = iter(dataloader)
            fetched = sorted(dataloader_iter)
            for a, b in zip(fetched, expected):
                # non-batched should not convert ints into tensors
                self.assertIsInstance(a, int)
                self.assertEqual(a, b)
            # DataLoader should match len of the iterable-style dataset (if implemented)
            self.assertEqual(len(dataloader), len(dataset))
            # When loading more than len(dataset) data, after accessing len(dataloader),
            # we should get a warning. See NOTE [ IterableDataset and __len__ ].
            dataset = CountingIterableDataset(20)
            dataloader = self._get_data_loader(dataset, num_workers=num_workers,
                                               worker_init_fn=set_faulthander_if_available,
                                               prefetch_factor=prefetch_factor)
            it = iter(dataloader)
            for _ in range(40):
                self.assertNotWarn(lambda: next(it), "Should not warn before accessing len(dataloader)")
            self.assertEqual(len(dataloader), len(dataset))
            self.assertEqual(len(dataloader), 20)
            it = iter(dataloader)
            for _ in range(20):
                self.assertNotWarn(lambda: next(it), "Should not warn before exceeding length")
            for _ in range(3):
                with self.assertWarnsRegex(
                    UserWarning,
                    r"but [0-9]+ samples have been fetched\. For multiprocessing data-loading, this",
                        msg="Should always warn after exceeding length"):
                    next(it)
        # [no auto-batching] test that workers exit gracefully
        workers = dataloader_iter._workers
        del dataloader_iter
        del dataloader
        try:
            for w in workers:
                w.join(JOIN_TIMEOUT)
                self.assertFalse(w.is_alive())
                self.assertEqual(w.exitcode, 0)
        finally:
            for w in workers:
                w.terminate()

        # [auto-batching] single process loading
        dataset = CountingIterableDataset(20)
        fetched = list(self._get_data_loader(dataset, batch_size=7))
        self.assertEqual(len(fetched), 3)
        self.assertEqual(fetched[0].tolist(), list(range(7)))
        self.assertEqual(fetched[1].tolist(), list(range(7, 14)))
        self.assertEqual(fetched[2].tolist(), list(range(14, 20)))

        # [auto-batching] multiprocessing loading
        num_workers = 3
        sizes_for_all_workers = [0, 4, 20]
        expected = sorted(sum((list(range(s)) for s in sizes_for_all_workers), []))
        assert len(sizes_for_all_workers) == num_workers, 'invalid test case'
        for prefetch_factor in [2, 3, 4]:
            dataset = WorkerSpecificIterableDataset(sizes_for_all_workers)
            # worker 0 should return 0 batches
            # worker 1 should return 1 batches
            # worker 2 should return 3 batches
            dataloader = self._get_data_loader(dataset, num_workers=num_workers, batch_size=7, prefetch_factor=prefetch_factor)
            dataloader_iter = iter(dataloader)
            fetched = list(dataloader_iter)
            self.assertEqual(len(fetched), 4)
            fetched = set(tuple(t.tolist()) for t in fetched)
            self.assertEqual(fetched, {tuple(range(4)), tuple(range(7)), tuple(range(7, 14)), tuple(range(14, 20))})

            # [auto-batching] test that workers exit gracefully
            workers = dataloader_iter._workers
            del dataloader_iter
            del dataloader
            try:
                for w in workers:
                    w.join(JOIN_TIMEOUT)
                    self.assertFalse(w.is_alive())
                    self.assertEqual(w.exitcode, 0)
            finally:
                for w in workers:
                    w.terminate()
        # [auto-batching & drop_last] single process loading
        dataset = CountingIterableDataset(20)
        fetched = list(self._get_data_loader(dataset, batch_size=7, drop_last=True))
        self.assertEqual(len(fetched), 2)
        self.assertEqual(fetched[0].tolist(), list(range(7)))
        self.assertEqual(fetched[1].tolist(), list(range(7, 14)))

        # [auto-batching & drop_last] multiprocessing loading
        num_workers = 3
        sizes_for_all_workers = [0, 4, 20]
        expected = sorted(sum((list(range(s)) for s in sizes_for_all_workers), []))
        assert len(sizes_for_all_workers) == num_workers, 'invalid test case'
        for prefetch_factor in [2, 3, 4]:
            dataset = WorkerSpecificIterableDataset(sizes_for_all_workers)
            # worker 0 should return 0 batches
            # worker 1 should return 1 batches
            # worker 2 should return 3 batches
            dataloader = self._get_data_loader(dataset, num_workers=num_workers, batch_size=7, drop_last=True,
                                               worker_init_fn=set_faulthander_if_available,
                                               prefetch_factor=prefetch_factor)
            dataloader_iter = iter(dataloader)
            fetched = list(dataloader_iter)
            self.assertEqual(len(fetched), 2)
            fetched = set(tuple(t.tolist()) for t in fetched)
            self.assertEqual(fetched, {tuple(range(7)), tuple(range(7, 14))})

            # [auto-batching & drop_last] test that workers exit gracefully
            workers = dataloader_iter._workers
            del dataloader_iter
            del dataloader
            try:
                for w in workers:
                    w.join(JOIN_TIMEOUT)
                    self.assertFalse(w.is_alive())
                    self.assertEqual(w.exitcode, 0)
            finally:
                for w in workers:
                    w.terminate()

    def test_chain_iterable_style_dataset(self):
        # chaining (concatenation)
        dataset1 = CountingIterableDataset(20)
        dataset2 = CountingIterableDataset(15)
        expected = list(range(20)) + list(range(15))
        for num_workers in [0, 1]:
            for chained_dataset in [dataset1 + dataset2, ChainDataset([dataset1, dataset2])]:
                fetched = list(self._get_data_loader(chained_dataset, num_workers=num_workers))
                self.assertEqual(len(fetched), len(expected))
                for e, d in zip(expected, fetched):
                    self.assertIsInstance(d, torch.Tensor)
                    self.assertEqual(e, d)

        with self.assertRaisesRegex(AssertionError, "ChainDataset only supports IterableDataset"):
            list(iter(dataset1 + self.dataset))

        with self.assertRaisesRegex(AssertionError, "ChainDataset only supports IterableDataset"):
            list(iter(ChainDataset([dataset1, self.dataset])))

    @unittest.skipIf(IS_MACOS, "Not working on macos")
    def test_multiprocessing_contexts(self):
        reference = [
            torch.arange(3),
            torch.arange(3, 6),
            torch.arange(6, 9),
            torch.arange(9, 11),
        ]
        counting_ds_n = 11
        dl_common_args = dict(num_workers=3, batch_size=3, pin_memory=(not TEST_CUDA))
        for ctx in supported_multiprocessing_contexts:
            # windows and jetson devices don't support sharing cuda tensor; ROCm does not yet fully support IPC
            if ctx in ['spawn', 'forkserver'] and TEST_CUDA and not IS_WINDOWS and not IS_JETSON:
                ds_cls = CUDACountingDataset
            else:
                ds_cls = CountingDataset
            self.assertEqual(
                reference, list(self._get_data_loader(ds_cls(counting_ds_n), multiprocessing_context=ctx, **dl_common_args)))
            if ctx is not None:
                # test ctx object
                ctx = mp.get_context(ctx)
                self.assertEqual(
                    reference, list(self._get_data_loader(ds_cls(counting_ds_n), multiprocessing_context=ctx, **dl_common_args)))

    @skipIfNoNumpy
    def test_multiprocessing_iterdatapipe(self):
        # Testing to make sure that function from global scope (e.g. imported from library) can be serialized
        # and used with multiprocess DataLoader

        reference = [torch.as_tensor([[2, 3, 4, 5]], dtype=torch.int64),
                     torch.as_tensor([[2, 3, 4, 5]], dtype=torch.int64)]
        datapipe: IterDataPipe = IterableWrapper([[1, 2, 3, 4], [1, 2, 3, 4, 5, 6]])
        datapipe = datapipe.map(row_processor)
        datapipe = datapipe.filter(lambda row: len(row) == 4) if HAS_DILL else datapipe.filter(filter_len)

        dl_common_args = dict(num_workers=2, batch_size=2, shuffle=True, pin_memory=(not TEST_CUDA))
        for ctx in supported_multiprocessing_contexts:
            self.assertEqual(reference,
                             [t.type(torch.int64)
                              for t in self._get_data_loader(datapipe, multiprocessing_context=ctx, **dl_common_args)])
            if ctx is not None:
                # test ctx object
                ctx = mp.get_context(ctx)
                self.assertEqual(reference,
                                 [t.type(torch.int64)
                                  for t in
                                  self._get_data_loader(datapipe, multiprocessing_context=ctx, **dl_common_args)])

    def test_worker_seed(self):
        num_workers = 6
        batch_size = 1
        dataset = SynchronizedSeedDataset(num_workers, batch_size, num_workers)
        dataloader = self._get_data_loader(dataset, batch_size=batch_size, num_workers=num_workers)
        seeds = set()
        for batch in dataloader:
            seeds.add(batch[0])
        self.assertEqual(len(seeds), num_workers)

    def test_worker_seed_reproducibility(self):
        def get_dataloader():
            return DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, generator=torch.Generator().manual_seed(42))

        num_workers = 6
        batch_size = 1
        dataset = SynchronizedSeedDataset(num_workers, batch_size, num_workers)
        self.assertEqual(set(int(batch) for batch in get_dataloader()), set(int(batch) for batch in get_dataloader()))

    def test_multi_epochs_reproducibility(self):
        num_workers = 2
        batch_size = 10
        num_epochs = 3

        dataset = TestMultiEpochDataset(batch_size * num_workers)
        dataloader = self._get_data_loader(dataset, batch_size=batch_size,
                                           shuffle=False, num_workers=num_workers)

        for ind in range(num_epochs):
            for batch_idx, sample in enumerate(dataloader):
                self.assertEqual(sample.tolist(), [batch_idx % num_workers] * batch_size)

    def test_worker_init_fn(self):
        dataset = SeedDataset(4)
        dataloader = self._get_data_loader(dataset, batch_size=2, num_workers=2,
                                           worker_init_fn=init_fn)
        for batch in dataloader:
            self.assertEqual(12345, batch[0])
            self.assertEqual(12345, batch[1])

    def test_get_worker_info(self):
        p = ErrorTrackingProcess(target=_test_get_worker_info)
        p.start()
        p.join(JOIN_TIMEOUT)
        try:
            self.assertFalse(p.is_alive())
            self.assertEqual(p.exitcode, 0)
        finally:
            p.terminate()

    def test_shuffle(self):
        self._test_shuffle(self._get_data_loader(self.dataset, shuffle=True))

    def test_shuffle_batch_none(self):
        self._test_shuffle(DataLoader(self.dataset, batch_size=None, shuffle=True))

    def test_shuffle_batch(self):
        self._test_shuffle(self._get_data_loader(self.dataset, batch_size=2, shuffle=True))

    def test_shuffle_reproducibility(self):
        for fn in (
            lambda: DataLoader(self.dataset, shuffle=True, num_workers=0, generator=torch.Generator().manual_seed(42)),
            lambda: DataLoader(self.dataset, shuffle=True, num_workers=2, generator=torch.Generator().manual_seed(42)),
        ):
            self.assertEqual(list(fn()), list(fn()))

    def test_sequential_workers(self):
        self._test_sequential(self._get_data_loader(self.dataset, num_workers=4))

    def test_seqential_batch_workers(self):
        self._test_sequential(self._get_data_loader(self.dataset, batch_size=2, num_workers=4))

    def test_seqential_batch_workers_prefetch(self):
        self._test_sequential(DataLoader(self.dataset, batch_size=2, num_workers=4, prefetch_factor=3))

    def test_shuffle_workers(self):
        self._test_shuffle(self._get_data_loader(self.dataset, shuffle=True, num_workers=4))

    def test_shuffle_batch_workers(self):
        self._test_shuffle(self._get_data_loader(self.dataset, batch_size=2, shuffle=True, num_workers=4))

    def test_shuffle_batch_workers_prefetch(self):
        self._test_shuffle(DataLoader(self.dataset, batch_size=2, shuffle=True, num_workers=4, prefetch_factor=3))

    def test_random_sampler(self):

        from collections import Counter
        from torch.utils.data import RandomSampler

        def sample_stat(sampler, num_samples):
            counts = Counter(sampler)
            count_repeated = sum(val > 1 for val in counts.values())
            return (count_repeated, min(counts.keys()), max(counts.keys()), sum(counts.values()))

        # test sample with replacement
        n = len(self.dataset) + 1  # ensure at least one sample is drawn more than once
        sampler_with_replacement = RandomSampler(self.dataset, replacement=True, num_samples=n)
        count_repeated, minval, maxval, count_total = sample_stat(sampler_with_replacement, n)
        self.assertTrue(count_repeated > 0)
        self.assertTrue(minval >= 0)
        self.assertTrue(maxval < len(self.dataset))
        self.assertTrue(count_total == n)

        # test sample without replacement and without specified num_samples
        sampler_without_replacement = RandomSampler(self.dataset)
        count_repeated, minval, maxval, count_total = sample_stat(sampler_without_replacement, len(self.dataset))
        self.assertTrue(count_repeated == 0)
        self.assertTrue(minval == 0)
        self.assertTrue(maxval == len(self.dataset) - 1)
        self.assertTrue(count_total == len(self.dataset))

        # test sample without replacement and with specified num_samples
        n = len(self.dataset) * 2
        sampler_without_replacement = RandomSampler(self.dataset, num_samples=n)
        count_repeated, minval, maxval, count_total = sample_stat(sampler_without_replacement, len(self.dataset))
        self.assertTrue(count_repeated == len(self.dataset))
        self.assertTrue(minval == 0)
        self.assertTrue(maxval == len(self.dataset) - 1)
        self.assertTrue(count_total == n)

        n = len(self.dataset) - 1
        sampler_without_replacement = RandomSampler(self.dataset, num_samples=n)
        count_repeated, minval, maxval, count_total = sample_stat(sampler_without_replacement, len(self.dataset))
        self.assertTrue(count_repeated == 0)
        self.assertTrue(minval >= 0)
        self.assertTrue(maxval < len(self.dataset))
        self.assertTrue(count_total == n)

        n = len(self.dataset) + 1
        sampler_without_replacement = RandomSampler(self.dataset, num_samples=n)
        count_repeated, minval, maxval, count_total = sample_stat(sampler_without_replacement, len(self.dataset))
        self.assertTrue(count_repeated == 1)
        self.assertTrue(minval == 0)
        self.assertTrue(maxval == len(self.dataset) - 1)
        self.assertTrue(count_total == n)

        # raise error when replacement is non-boolean
        with self.assertRaisesRegex(TypeError, "replacement should be a boolean value, but got replacement=0"):
            RandomSampler(self.dataset, replacement=0)

    def test_random_sampler_len_with_replacement(self):
        from torch.utils.data import RandomSampler
        # add 5 extra samples
        num_samples = len(self.dataset) + 5
        sampler = RandomSampler(self.dataset,
                                replacement=True,
                                num_samples=num_samples)
        # test len method
        self.assertEqual(num_samples, len(sampler))

        # test with iteration
        count_num_samples = sum(1 for _ in sampler)
        self.assertEqual(num_samples, count_num_samples)

        # test with dataloader, batch_size = 1
        batch_size = 1
        count_num_samples_in_data_loader = len(self._get_data_loader(
            self.dataset, batch_size=batch_size, sampler=sampler))
        self.assertEqual(num_samples, count_num_samples_in_data_loader)

        # test with dataloader, batch_size = 6
        batch_size = 6
        count_num_samples_in_data_loader = len(self._get_data_loader(
            self.dataset, batch_size=batch_size, sampler=sampler))
        self.assertEqual(int(math.ceil(float(num_samples) / batch_size)),
                         count_num_samples_in_data_loader)

    def test_random_sampler_len_without_replacement(self):
        from torch.utils.data import RandomSampler
        # add 5 extra samples
        num_samples = len(self.dataset) + 5
        sampler = RandomSampler(self.dataset,
                                replacement=False,
                                num_samples=num_samples)
        # test len method
        self.assertEqual(num_samples, len(sampler))

        # test with iteration
        count_num_samples = sum(1 for _ in sampler)
        self.assertEqual(num_samples, count_num_samples)

        # test with dataloader, batch_size = 1
        batch_size = 1
        count_num_samples_in_data_loader = len(self._get_data_loader(
            self.dataset, batch_size=batch_size, sampler=sampler))
        self.assertEqual(num_samples, count_num_samples_in_data_loader)

        # test with dataloader, batch_size = 6
        batch_size = 6
        count_num_samples_in_data_loader = len(self._get_data_loader(
            self.dataset, batch_size=batch_size, sampler=sampler))
        self.assertEqual(num_samples // batch_size + (num_samples % batch_size > 0),
                         count_num_samples_in_data_loader)

    def test_distributed_sampler_invalid_rank(self):
        from torch.utils.data.distributed import DistributedSampler
        dataset = torch.IntTensor(range(10))
        with self.assertRaisesRegex(ValueError, "Invalid rank"):
            sampler = DistributedSampler(dataset, 3, 3)

        with self.assertRaisesRegex(ValueError, "Invalid rank"):
            sampler = DistributedSampler(dataset, 3, -1)

    def test_duplicating_data_with_drop_last(self):

        from torch.utils.data.distributed import DistributedSampler

        num_processes = 4
        num_batches = 9
        data_set = torch.IntTensor(range(num_batches))
        scanned_data = torch.IntTensor([])
        for i in range(num_processes):
            s = DistributedSampler(data_set, num_processes, i)
            d_loader = self._get_data_loader(data_set, batch_size=int(num_batches / num_processes), drop_last=True, sampler=s)
            for data in d_loader:
                scanned_data = torch.cat((scanned_data, data), 0)

        self.assertEqual(scanned_data.size(), scanned_data.unique().size())

    def test_sampler_reproducibility(self):
        from torch.utils.data import RandomSampler, WeightedRandomSampler, SubsetRandomSampler

        weights = [0.1, 0.9, 0.4, 0.7, 3.0, 0.6]
        for fn in (
            lambda: RandomSampler(self.dataset, num_samples=5, replacement=True, generator=torch.Generator().manual_seed(42)),
            lambda: RandomSampler(self.dataset, replacement=False, generator=torch.Generator().manual_seed(42)),
            lambda: WeightedRandomSampler(weights, num_samples=5, replacement=True, generator=torch.Generator().manual_seed(42)),
            lambda: WeightedRandomSampler(weights, num_samples=5, replacement=False, generator=torch.Generator().manual_seed(42)),
            lambda: SubsetRandomSampler(range(10), generator=torch.Generator().manual_seed(42)),
        ):
            self.assertEqual(list(fn()), list(fn()))

        for sampler in (
            RandomSampler(self.dataset, num_samples=5, replacement=True),
            RandomSampler(self.dataset, replacement=False),
            WeightedRandomSampler(weights, num_samples=5, replacement=True),
            WeightedRandomSampler(weights, num_samples=5, replacement=False),
            SubsetRandomSampler(range(10)),
        ):
            torch.manual_seed(0)
            l1 = list(sampler) + list(sampler)

            torch.manual_seed(0)
            l2 = list(sampler) + list(sampler)
            self.assertEqual(l1, l2)

            its = (iter(sampler), iter(sampler))
            ls = ([], [])
            for idx in range(len(sampler)):
                for i in range(2):
                    if idx == 0:
                        torch.manual_seed(0)
                    ls[i].append(next(its[i]))
            self.assertEqual(ls[0], ls[1])

    def _test_sampler(self, **kwargs):
        indices = range(2, 12)  # using a regular iterable
        dl = self._get_data_loader(self.dataset, sampler=indices, batch_size=2, **kwargs)
        self.assertEqual(len(dl), 5)
        for i, (input, _target) in enumerate(dl):
            self.assertEqual(len(input), 2)
            self.assertEqual(input, self.data[i * 2 + 2:i * 2 + 4])

    def test_sampler(self):
        self._test_sampler()
        self._test_sampler(num_workers=4)
        if not NO_MULTIPROCESSING_SPAWN:
            self._test_batch_sampler(num_workers=4, multiprocessing_context='spawn')

    def _test_batch_sampler(self, **kwargs):
        # [(0, 1), (2, 3, 4), (5, 6), (7, 8, 9), ...]
        batches = []  # using a regular iterable
        for i in range(0, 20, 5):
            batches.append(tuple(range(i, i + 2)))
            batches.append(tuple(range(i + 2, i + 5)))

        dl = self._get_data_loader(self.dataset, batch_sampler=batches, **kwargs)
        self.assertEqual(len(dl), 8)
        for i, (input, _target) in enumerate(dl):
            if i % 2 == 0:
                offset = i * 5 // 2
                self.assertEqual(len(input), 2)
                self.assertEqual(input, self.data[offset:offset + 2])
            else:
                offset = i * 5 // 2
                self.assertEqual(len(input), 3)
                self.assertEqual(input, self.data[offset:offset + 3])

    def test_batch_sampler(self):
        self._test_batch_sampler()
        self._test_batch_sampler(num_workers=4)
        if not NO_MULTIPROCESSING_SPAWN:
            self._test_batch_sampler(num_workers=4, multiprocessing_context='spawn')

    @unittest.skipIf(not TEST_CUDA, "CUDA unavailable")
    def test_shuffle_pin_memory(self):
        loader = self._get_data_loader(self.dataset, batch_size=2, shuffle=True, num_workers=4, pin_memory=True)
        for input, target in loader:
            self.assertTrue(input.is_pinned())
            self.assertTrue(target.is_pinned())

    @unittest.skipIf(not TEST_NUMPY, "numpy unavailable")
    def test_numpy(self):
        import numpy as np

        class TestDataset(torch.utils.data.Dataset):
            def __getitem__(self, i):
                return np.ones((2, 3, 4)) * i

            def __len__(self):
                return 1000

        loader = self._get_data_loader(TestDataset(), batch_size=12)
        batch = next(iter(loader))
        self.assertIsInstance(batch, torch.DoubleTensor)
        self.assertEqual(batch.size(), torch.Size([12, 2, 3, 4]))

    @unittest.skipIf(not TEST_NUMPY, "numpy unavailable")
    def test_numpy_gen_state(self):
        from torch.utils.data._utils.worker import _generate_state
        # Using NumPy generated states as the reference to test `_generate_state`
        # having the same result.
        # Test case: ((worker_id, base_seed), expected_state)
        test_cases = [
            ((4, 13434589827475259383), (2884386318, 1088094898, 3523808998, 3860348662)),
            ((1, 15014285634777110771), (1934848465, 763213760, 2959016433, 179751970)),
            ((10, 978296274032934101), (1759791917, 3550927336, 1225977135, 1036538043)),
            ((12, 11868770762134256968), (3974661794, 3331131333, 3630387033, 2885815368)),
            ((9, 15378787925219019706), (3815056996, 3162224466, 2735102421, 3190253477)),
            ((5, 9055612723125076328), (3522565701, 3368424109, 959377806, 621878693)),
            ((15, 14617792358407278405), (3402479508, 1588702753, 1169536393, 3675067356)),
            ((9, 17363320784006640087), (957989458, 2518334477, 1421725660, 3086155459)),
            ((12, 480002904169484764), (2732851467, 1762620729, 4055801988, 1277640511)),
            ((15, 16803975943592702950), (3479415043, 4022359553, 295994005, 3358606349)),
            ((9, 11704776406047813044), (1968928009, 710113752, 2442656196, 1587420279)),
            ((10, 16357891985431864516), (1271733898, 4197047399, 3727213786, 2338547348)),
            ((2, 17423369006318065007), (544294336, 1911284083, 3299147734, 3231058347)),
            ((2, 2889492011444113593), (3721591783, 2595811276, 2212881745, 977682627)),
            ((0, 8979703111668486195), (4276723937, 2556068849, 2962827292, 233130238)),
            ((6, 6269787272229682235), (2548857855, 1216457374, 1012973562, 2999759647))
        ]

        for (worker_id, base_seed), exp in test_cases:
            self.assertEqual(exp, _generate_state(base_seed, worker_id))

    def test_error(self):
        self._test_error(self._get_data_loader(ErrorDataset(100), batch_size=2, shuffle=True))

    def test_error_workers(self):
        self._test_error(self._get_data_loader(ErrorDataset(41), batch_size=2, shuffle=True, num_workers=4))

    @unittest.skipIf(IS_WINDOWS, "FIXME: stuck test")
    def test_partial_workers(self):
        r"""Check that workers exit even if the iterator is not exhausted."""
        if TEST_CUDA:
            pin_memory_configs = (True, False)
        else:
            pin_memory_configs = (False,)

        for pin_memory in pin_memory_configs:
            loader = iter(self._get_data_loader(self.dataset, batch_size=2, num_workers=4, pin_memory=pin_memory))
            workers = loader._workers
            if pin_memory:
                pin_memory_thread = loader._pin_memory_thread
            for i, _ in enumerate(loader):
                if i == 10:
                    break
            assert i == 10
            del loader
            for w in workers:
                w.join(JOIN_TIMEOUT)
                self.assertFalse(w.is_alive(), 'subprocess not terminated')
            if pin_memory:
                pin_memory_thread.join(JOIN_TIMEOUT)
                self.assertFalse(pin_memory_thread.is_alive())

    # Takes 2.5min to finish, see https://github.com/pytorch/pytorch/issues/46065
    @skipIfRocm
    @unittest.skipIf(not HAS_PSUTIL, "psutil not found")
    @slowTest
    def test_proper_exit(self):
        (r'''There might be ConnectionResetError or leaked semaphore warning '''
         r'''(due to dirty process exit), but they are all safe to ignore''')

        # TODO: test the case where the pin_memory_thread triggers an
        #       error/fatal signal. I haven't found out how to properly do that.

        for is_iterable_dataset, use_workers, pin_memory, hold_iter_reference in \
                itertools.product([True, False], repeat=4):

            # `hold_iter_reference` specifies whether we hold a reference to the
            # iterator. This is interesting because Python3 error traces holds a
            # reference to the frames, which hold references to all the local
            # variables including the iterator, and then the iterator dtor may
            # not be called before process end. It is important to see that the
            # processes still exit in both cases.

            if pin_memory and (not TEST_CUDA or NO_MULTIPROCESSING_SPAWN or IS_WINDOWS):
                # This test runs in a subprocess, which can only initialize CUDA with spawn.
                # DataLoader with pin_memory=True initializes CUDA when its iterator is constructed.
                # For windows, pin_memory sometimes causes CUDA oom.
                continue

            # `exit_method` controls the way the loader process ends.
            #   - `*_kill` means that `*` is killed by OS.
            #   - `*_error` means that `*` raises an error.
            #   - `None` means that no error happens.
            # In all cases, all processes should end properly.
            if use_workers:
                # TODO: Fix test for 'loader_kill' that would cause running out of shared memory.
                # Killing loader process would prevent DataLoader iterator clean up all queues
                # and worker processes
                exit_methods = [None, 'loader_error', 'worker_error', 'worker_kill']
                persistent_workers = self.persistent_workers
            else:
                exit_methods = [None, 'loader_error', 'loader_kill']
                persistent_workers = False

            for exit_method in exit_methods:
                if exit_method == 'worker_kill':
                    # FIXME: This sometimes hangs. See #16608.
                    continue

                desc = []
                desc.append('is_iterable_dataset={}'.format(is_iterable_dataset))
                desc.append('use_workers={}'.format(use_workers))
                desc.append('pin_memory={}'.format(pin_memory))
                desc.append('hold_iter_reference={}'.format(hold_iter_reference))
                desc.append('exit_method={}'.format(exit_method))
                desc = 'test_proper_exit with ' + ', '.join(desc)

                # Event that the loader process uses to signal testing process
                # that various things are setup, including that the worker pids
                # are specified in `worker_pids` array.
                loader_setup_event = mp.Event()

                # Event that this process has finished setting up, and the
                # loader process can now proceed to trigger error events or
                # finish normally.
                tester_setup_event = mp.Event()

                loader_p = ErrorTrackingProcess(target=_test_proper_exit,
                                                args=(is_iterable_dataset, use_workers, pin_memory,
                                                      exit_method, hold_iter_reference,
                                                      loader_setup_event, tester_setup_event,
                                                      persistent_workers),
                                                disable_stderr=False)
                loader_p.start()
                loader_psutil_p = psutil.Process(loader_p.pid)

                # Wait for loader process to set everything up, e.g., starting
                # workers.
                loader_setup_event.wait(timeout=JOIN_TIMEOUT)
                if not loader_setup_event.is_set():
                    fail_msg = desc + ': loader process failed to setup within given time'
                    if loader_p.exception is not None:
                        fail_msg += ', and had exception {}'.format(loader_p.exception)
                    elif not loader_p.is_alive():
                        fail_msg += ', and exited with code {} but had no exception'.format(loader_p.exitcode)
                    else:
                        fail_msg += ', and is still alive.'
                    if loader_p.is_alive():
                        # this may kill the process, needs to run after the above lines
                        loader_p.print_traces_of_all_threads()
                    self.fail(fail_msg)

                # We are certain that the workers have started now.
                worker_psutil_ps = loader_psutil_p.children()

                def fail(reason):
                    report_psutil_attrs = ['pid', 'name', 'cpu_times', 'io_counters',
                                           'memory_full_info', 'num_ctx_switches',
                                           'open_files', 'threads', 'status',
                                           'nice', 'ionice']
                    if reason is None:
                        err_msg = desc
                    else:
                        err_msg = '{}: {}'.format(desc, reason)
                    err_msg += '\nLoader info:\n\t'
                    if loader_psutil_p.is_running():
                        err_msg += str(loader_psutil_p.as_dict(attrs=report_psutil_attrs))
                        # this may kill the process, needs to run after the above line
                        loader_p.print_traces_of_all_threads()
                    else:
                        err_msg += 'exited with code {}'.format(loader_p.exitcode)
                    if use_workers:
                        err_msg += '\nWorker(s) info:'
                        for idx, worker_psutil_p in enumerate(worker_psutil_ps):
                            err_msg += '\n\tWorker {}:\n\t\t'.format(idx)
                            if worker_psutil_p.is_running():
                                err_msg += str(worker_psutil_p.as_dict(attrs=report_psutil_attrs))
                                # this may kill the process, needs to run after the above line
                                print_traces_of_all_threads(worker_psutil_p.pid)
                            else:
                                err_msg += 'exited with unknown code'
                    self.fail(err_msg)

                tester_setup_event.set()

                try:
                    loader_p.join(JOIN_TIMEOUT + MP_STATUS_CHECK_INTERVAL)
                    if loader_p.is_alive():
                        fail_reason = 'loader process did not terminate'
                        if loader_p.exception is not None:
                            fail(fail_reason + ', and had exception {}'.format(loader_p.exception))
                        else:
                            fail(fail_reason + ', and had no exception')
                    _, alive = psutil.wait_procs(worker_psutil_ps, timeout=(MP_STATUS_CHECK_INTERVAL + JOIN_TIMEOUT))
                    if len(alive) > 0:
                        fail('worker process (pid(s) {}) did not terminate'.format(
                            ', '.join(str(p.pid) for p in alive)))
                    if exit_method is None:
                        if loader_p.exitcode != 0:
                            fail('loader process had nonzero exitcode {}'.format(loader_p.exitcode))
                    else:
                        if loader_p.exitcode == 0:
                            fail('loader process had zero exitcode')
                        if exit_method == 'loader_error':
                            if not isinstance(loader_p.exception, RuntimeError) or \
                                    'Loader error' not in str(loader_p.exception):
                                fail('loader process did not raise expected exception, but had {}'.format(
                                    loader_p.exception))
                        elif exit_method == 'worker_kill':
                            if isinstance(loader_p.exception, RuntimeError):
                                if 'DataLoader worker (pid' not in str(loader_p.exception):
                                    fail('loader process did not raise expected exception, but had {}'.format(
                                        loader_p.exception))
                            elif isinstance(loader_p.exception, ConnectionRefusedError):
                                # Sometimes, when the worker is being killed and is freeing its
                                # resources, the unpickling in loader process will be met an
                                # a `ConnectionRefusedError` as it can not open a socket to receive
                                # resource. In such cases, the worker may not have fully exited,
                                # and the loader can't know this via `is_alive` check or `SIGCHLD`
                                # handler. So we permit this as an allowed error as well.
                                # After all, we are happy as long as it terminates.
                                pass
                            else:
                                fail('loader process did not raise expected exception, but had {}'.format(
                                    loader_p.exception))
                        elif exit_method == 'worker_error':
                            if not isinstance(loader_p.exception, RuntimeError) or \
                                    'Worker error' not in str(loader_p.exception):
                                fail('loader process did not raise expected exception, but had {}'.format(
                                    loader_p.exception))
                finally:
                    loader_p.terminate()

    def test_len(self):
        def check_len(dl, expected):
            self.assertEqual(len(dl), expected)
            n = 0
            for _ in dl:
                n += 1
            self.assertEqual(n, expected)
        check_len(self.dataset, 100)
        check_len(self._get_data_loader(self.dataset, batch_size=2), 50)
        check_len(self._get_data_loader(self.dataset, batch_size=3), 34)

    def test_iterabledataset_len(self):
        class IterableDataset(torch.utils.data.IterableDataset):
            def __len__(self):
                return 10

            def __iter__(self):
                return iter(range(10))

        iterable_loader = DataLoader(IterableDataset(), batch_size=1)
        self.assertEqual(len(iterable_loader), 10)
        iterable_loader = DataLoader(IterableDataset(), batch_size=1, drop_last=True)
        self.assertEqual(len(iterable_loader), 10)

        iterable_loader = DataLoader(IterableDataset(), batch_size=2)
        self.assertEqual(len(iterable_loader), 5)
        iterable_loader = DataLoader(IterableDataset(), batch_size=2, drop_last=True)
        self.assertEqual(len(iterable_loader), 5)

        iterable_loader = DataLoader(IterableDataset(), batch_size=3)
        self.assertEqual(len(iterable_loader), 4)
        iterable_loader = DataLoader(IterableDataset(), batch_size=3, drop_last=True)
        self.assertEqual(len(iterable_loader), 3)

    @unittest.skipIf(not TEST_NUMPY, "numpy unavailable")
    def test_numpy_scalars(self):
        import numpy as np

        class ScalarDataset(torch.utils.data.Dataset):
            def __init__(self, dtype):
                self.dtype = dtype

            def __getitem__(self, i):
                return self.dtype()

            def __len__(self):
                return 4

        dtypes = {
            np.float64: torch.DoubleTensor,
            np.float32: torch.FloatTensor,
            np.float16: torch.HalfTensor,
            np.int64: torch.LongTensor,
            np.int32: torch.IntTensor,
            np.int16: torch.ShortTensor,
            np.int8: torch.CharTensor,
            np.uint8: torch.ByteTensor,
        }
        for dt, tt in dtypes.items():
            dset = ScalarDataset(dt)
            loader = self._get_data_loader(dset, batch_size=2)
            batch = next(iter(loader))
            self.assertIsInstance(batch, tt)

    def test_default_convert_mapping_keep_type(self):
        data = CustomDict({"a": 1, "b": 2})
        converted = _utils.collate.default_convert(data)

        self.assertEqual(converted, data)

    def test_default_convert_sequence_keep_type(self):
        data = CustomList([1, 2, 3])
        converted = _utils.collate.default_convert(data)

        self.assertEqual(converted, data)

    def test_default_convert_sequence_dont_keep_type(self):
        data = range(2)
        converted = _utils.collate.default_convert(data)

        self.assertEqual(converted, [0, 1])

    def test_default_collate_dtype(self):
        arr = [1, 2, -1]
        collated = _utils.collate.default_collate(arr)
        self.assertEqual(collated, torch.tensor(arr))
        self.assertEqual(collated.dtype, torch.int64)

        arr = [1.1, 2.3, -0.9]
        collated = _utils.collate.default_collate(arr)
        # TODO(#38095): Replace assertEqualIgnoreType. See issue #38095
        self.assertEqualIgnoreType(collated, torch.tensor(arr))
        self.assertEqual(collated.dtype, torch.float64)

        arr = [True, False]
        collated = _utils.collate.default_collate(arr)
        self.assertEqual(collated, torch.tensor(arr))
        self.assertEqual(collated.dtype, torch.bool)

        # Should be a no-op
        arr = ['a', 'b', 'c']
        self.assertEqual(arr, _utils.collate.default_collate(arr))

    def test_default_collate_mapping_keep_type(self):
        batch = [CustomDict({"a": 1, "b": 2}), CustomDict({"a": 3, "b": 4})]
        collated = _utils.collate.default_collate(batch)

        expected = CustomDict({"a": torch.tensor([1, 3]), "b": torch.tensor([2, 4])})
        self.assertEqual(collated, expected)

    def test_default_collate_sequence_keep_type(self):
        batch = [CustomList([1, 2, 3]), CustomList([4, 5, 6])]
        collated = _utils.collate.default_collate(batch)

        expected = CustomList([
            torch.tensor([1, 4]),
            torch.tensor([2, 5]),
            torch.tensor([3, 6]),
        ])
        self.assertEqual(collated, expected)

    def test_default_collate_sequence_dont_keep_type(self):
        batch = [range(2), range(2)]
        collated = _utils.collate.default_collate(batch)

        self.assertEqual(collated, [torch.tensor([0, 0]), torch.tensor([1, 1])])

    @unittest.skipIf(not TEST_NUMPY, "numpy unavailable")
    def test_default_collate_bad_numpy_types(self):
        import numpy as np

        # Should be a no-op
        arr = np.array(['a', 'b', 'c'])
        self.assertEqual(arr, _utils.collate.default_collate(arr))

        arr = np.array([[['a', 'b', 'c']]])
        self.assertRaises(TypeError, lambda: _utils.collate.default_collate(arr))

        arr = np.array([object(), object(), object()])
        self.assertRaises(TypeError, lambda: _utils.collate.default_collate(arr))

        arr = np.array([[[object(), object(), object()]]])
        self.assertRaises(TypeError, lambda: _utils.collate.default_collate(arr))

    @unittest.skipIf(not TEST_NUMPY, "numpy unavailable")
    def test_default_collate_numpy_memmap(self):
        import numpy as np

        with tempfile.TemporaryFile() as f:
            arr = np.array([[0, 1], [2, 3], [4, 5], [6, 7]])
            arr_memmap = np.memmap(f, dtype=arr.dtype, mode='w+', shape=arr.shape)
            arr_memmap[:] = arr[:]
            arr_new = np.memmap(f, dtype=arr.dtype, mode='r', shape=arr.shape)
            tensor = _utils.collate.default_collate(list(arr_new))

        self.assertTrue((tensor == tensor.new_tensor([[0, 1], [2, 3], [4, 5], [6, 7]])).all().item())

    def test_default_collate_bad_sequence_type(self):
        batch = [['X'], ['X', 'X']]
        self.assertRaises(RuntimeError, lambda: _utils.collate.default_collate(batch))
        self.assertRaises(RuntimeError, lambda: _utils.collate.default_collate(batch[::-1]))

    @unittest.skipIf(not TEST_NUMPY, "numpy unavailable")
    def test_default_collate_shared_tensor(self):
        import numpy as np
        t_in = torch.zeros(1)
        n_in = np.zeros(1)

        self.assertEqual(t_in.is_shared(), False)

        self.assertEqual(_utils.collate.default_collate([t_in]).is_shared(), False)
        self.assertEqual(_utils.collate.default_collate([n_in]).is_shared(), False)

        # FIXME: fix the following hack that makes `default_collate` believe
        #        that it is in a worker process (since it tests
        #        `get_worker_info() != None`), even though it is not.
        old = _utils.worker._worker_info
        try:
            _utils.worker._worker_info = 'x'
            self.assertEqual(_utils.collate.default_collate([t_in]).is_shared(), True)
            self.assertEqual(_utils.collate.default_collate([n_in]).is_shared(), True)
        finally:
            _utils.worker._worker_info = old

    def test_excessive_thread_creation_warning(self):
        with self.assertWarnsRegex(
            UserWarning,
                r"excessive worker creation might get DataLoader running slow or even freeze"):
            dataloader = DataLoader(self.dataset, batch_size=2, num_workers=1000)

# Define a global function for testing purposes since local functions cannot be pickled
def identity(x):
    return x

@unittest.skipIf(
    TEST_WITH_TSAN,
    "Fails with TSAN with the following error: starting new threads after multi-threaded "
    "fork is not supported. Dying (set die_after_fork=0 to override)")
class TestDataLoader2(TestCase):
    @skipIfNoDill
    def test_basics(self):
        # TODO(VitalyFedyunin): This test will start breaking if we remove guaranteed order
        # of traversing workers
        dp = IterableWrapper(list(range(1000))).sharding_filter()
        dl = DataLoader(dp, batch_size=3, collate_fn=identity, num_workers=2)
        dl2 = DataLoader2(dp, batch_size=3, collate_fn=identity, num_workers=2)
        dl2_threading = DataLoader2(dp, batch_size=3, collate_fn=identity, num_workers=2, parallelism_mode='thread')
        self.assertEqual(list(dl), list(dl2))
        self.assertEqual(list(dl), list(dl2_threading))

    class Sorter(IterDataPipe):
        def __init__(self, datapipe):
            self.datapipe = datapipe

        def __iter__(self):
            return iter(sorted(self.datapipe))

    def test_shuffle(self):
        items = list(range(1000))
        dp = IterableWrapper(items).sharding_filter().shuffle()

        dl = DataLoader2(dp, batch_size=None, num_workers=2, shuffle=False)
        self.assertEqual(items, list(dl))

        dl = DataLoader2(dp, batch_size=None, num_workers=2, shuffle=True)
        self.assertNotEqual(items, list(dl))
        self.assertEqual(items, sorted(list(dl)))

        dl = DataLoader2(dp, batch_size=None, num_workers=2, shuffle=True)
        self.assertNotEqual(items, list(dl))
        self.assertEqual(items, sorted(list(dl)))

        dl = DataLoader2(self.Sorter(dp), batch_size=None, num_workers=2, shuffle=True)
        self.assertEqual(list(dl), items)

        dl = DataLoader2(self.Sorter(dp), batch_size=None, num_workers=2, shuffle=True)
        self.assertEqual(list(dl), items)


@unittest.skipIf(
    TEST_WITH_TSAN,
    "Fails with TSAN with the following error: starting new threads after multi-threaded "
    "fork is not supported. Dying (set die_after_fork=0 to override)")
class TestDataLoader2_EventLoop(TestCase):
    @skipIfNoDill
    def test_basic_threading(self):
        def clean_me(process, req_queue, res_queue):
            req_queue.put(communication.messages.TerminateRequest())
            _ = res_queue.get()
            process.join()

        it = list(range(100))
        numbers_dp = IterableWrapper(it)
        (process, req_queue, res_queue, _thread_local_datapipe) = communication.eventloop.SpawnThreadForDataPipeline(numbers_dp)

        process.start()
        local_datapipe = communication.iter.QueueWrapper(
            communication.protocol.IterDataPipeQueueProtocolClient(req_queue, res_queue))

        actual = list(local_datapipe)
        clean_me(process, req_queue, res_queue)

        self.assertEqual(list(range(100)), actual)

    @skipIfNoDill
    def test_basic_mapdatapipe_threading(self):
        def clean_me(process, req_queue, res_queue):
            req_queue.put(communication.messages.TerminateRequest())
            _ = res_queue.get()
            process.join()

        input_len = 100
        it = list(range(input_len))
        numbers_dp = SequenceWrapper(it)
        (process, req_queue, res_queue, _thread_local_datapipe) = communication.eventloop.SpawnThreadForDataPipeline(
            numbers_dp)

        process.start()

        # Functional Test: Ensure that you can retrieve every element from the Queue and DataPipe
        local_datapipe = communication.map.QueueWrapperForMap(
            communication.protocol.MapDataPipeQueueProtocolClient(req_queue, res_queue))
        actual = list(local_datapipe)
        self.assertEqual([(x, x) for x in range(100)], actual)

        # Functional Test: raise Error when input
        local_datapipe = communication.map.QueueWrapperForMap(
            communication.protocol.MapDataPipeQueueProtocolClient(req_queue, res_queue))
        with self.assertRaisesRegex(IndexError, "out of bound"):
            local_datapipe[1000]

        # __len__ Test: Ensure that the correct length is returned
        local_datapipe = communication.map.QueueWrapperForMap(
            communication.protocol.MapDataPipeQueueProtocolClient(req_queue, res_queue))
        self.assertEqual(input_len, len(local_datapipe))

        clean_me(process, req_queue, res_queue)


class IntegrationTestDataLoaderDataPipe(TestCase):
    r"""
    Verify the behavior of a certain ``DataPipes`` with ``DataLoader``
    """

    def test_shuffler_iterdatapipe(self):
        r"""
        Verify ``IterDataPipe.shuffle`` is controlled by ``DataLoader``
        to generate different seeds deterministically per epoch.
        """
        exp = list(range(100))

        def _create_dp(buffer_size):
            input_ds = dp.iter.IterableWrapper(exp)
            return input_ds.shuffle(buffer_size=buffer_size).sharding_filter()

        for bs in (5, 20, 33):
            # Test Deterministic
            for num_workers, pw in itertools.product((0, 1, 2), (True, False)):
                if num_workers == 0 and pw:
                    continue

                shuffle_dp = _create_dp(bs)

                mp_ctx = "spawn" if num_workers > 0 else None
                dl = DataLoader(
                    shuffle_dp,
                    num_workers=num_workers,
                    shuffle=True,
                    multiprocessing_context=mp_ctx,
                    persistent_workers=pw
                )

                # No seed
                dl_res_ns = list(dl)
                self.assertEqual(sorted(dl_res_ns), exp)

                # Same seeds
                dl_res = []
                for epoch in range(2):
                    torch.manual_seed(123)
                    dl_res.append(list(dl))
                self.assertEqual(dl_res[0], dl_res[1])
                self.assertEqual(sorted(dl_res[0]), exp)

                # Different seeds
                torch.manual_seed(321)
                dl_res.append(list(dl))

                self.assertEqual(len(dl_res[0]), len(dl_res[2]))
                self.assertNotEqual(dl_res[0], dl_res[2])
                self.assertEqual(sorted(dl_res[0]), sorted(dl_res[2]))

                if dl._iterator is not None:
                    dl._iterator._shutdown_workers()
                    dl._iterator = None
                del dl


class StringDataset(Dataset):
    def __init__(self):
        self.s = '12345'

    def __len__(self):
        return len(self.s)

    def __getitem__(self, ndx):
        return (self.s[ndx], ndx)


@unittest.skipIf(
    TEST_WITH_TSAN,
    "Fails with TSAN with the following error: starting new threads after multi-threaded "
    "fork is not supported. Dying (set die_after_fork=0 to override)")
class TestStringDataLoader(TestCase):
    def setUp(self):
        super(TestStringDataLoader, self).setUp()
        self.dataset = StringDataset()

    @unittest.skipIf(not TEST_CUDA, "CUDA unavailable")
    def test_shuffle_pin_memory(self):
        loader = DataLoader(self.dataset, batch_size=2, shuffle=True, num_workers=4, pin_memory=True)
        for (s, n) in loader:
            self.assertIsInstance(s[0], str)
            self.assertTrue(n.is_pinned())


class DictDataset(Dataset):
    def __len__(self):
        return 4

    def __getitem__(self, ndx):
        return {
            'a_tensor': torch.empty(4, 2).fill_(ndx),
            'another_dict': {
                'a_number': ndx,
            },
        }


@unittest.skipIf(
    TEST_WITH_TSAN,
    "Fails with TSAN with the following error: starting new threads after multi-threaded "
    "fork is not supported. Dying (set die_after_fork=0 to override)")
class TestDictDataLoader(TestCase):
    def setUp(self):
        super(TestDictDataLoader, self).setUp()
        self.dataset = DictDataset()

    def test_sequential_batch(self):
        for persistent_workers in (False, True):
            if persistent_workers:
                loader = DataLoader(self.dataset, batch_size=2, shuffle=False,
                                    persistent_workers=persistent_workers, num_workers=1)
            else:
                loader = DataLoader(self.dataset, batch_size=2, shuffle=False,
                                    persistent_workers=persistent_workers)
            batch_size = loader.batch_size
            for i, sample in enumerate(loader):
                idx = i * batch_size
                self.assertEqual(set(sample.keys()), {'a_tensor', 'another_dict'})
                self.assertEqual(set(sample['another_dict'].keys()), {'a_number'})

                t = sample['a_tensor']
                self.assertEqual(t.size(), torch.Size([batch_size, 4, 2]))
                self.assertTrue((t[0] == idx).all())
                self.assertTrue((t[1] == idx + 1).all())

                n = sample['another_dict']['a_number']
                self.assertEqual(n.size(), torch.Size([batch_size]))
                self.assertEqual(n[0], idx)
                self.assertEqual(n[1], idx + 1)

    @unittest.skipIf(not TEST_CUDA, "CUDA unavailable")
    def test_pin_memory(self):
        loader = DataLoader(self.dataset, batch_size=2, pin_memory=True)
        for sample in loader:
            self.assertTrue(sample['a_tensor'].is_pinned())
            self.assertTrue(sample['another_dict']['a_number'].is_pinned())

    @unittest.skipIf(not TEST_CUDA, "CUDA unavailable")
    def test_pin_memory_device(self):
        loader = DataLoader(self.dataset, batch_size=2, pin_memory=True, pin_memory_device='cuda')
        for sample in loader:
            self.assertTrue(sample['a_tensor'].is_pinned(device='cuda'))
            self.assertTrue(sample['another_dict']['a_number'].is_pinned(device='cuda'))

    @unittest.skipIf(not TEST_CUDA, "CUDA unavailable")
    def test_pin_memory_with_only_device(self):
        loader = DataLoader(self.dataset, batch_size=2, pin_memory_device='cuda')
        for sample in loader:
            self.assertFalse(sample['a_tensor'].is_pinned(device='cuda'))
            self.assertFalse(sample['another_dict']['a_number'].is_pinned(device='cuda'))

class DummyDataset(torch.utils.data.Dataset):
    def __init__(self):
        self.data = list(range(10))

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        if torch.is_tensor(idx):
            idx = idx.tolist()
        # The persistent workers always maintain the original
        # dataset through the dataloader lifetime
        # so the attributes will remain the same as the
        # first time the workers where spawned (dataloader iteration)
        assert self.start == 0
        return self.data[idx]


@unittest.skipIf(
    TEST_WITH_TSAN,
    "Fails with TSAN with the following error: starting new threads after multi-threaded "
    "fork is not supported. Dying (set die_after_fork=0 to override)")
@unittest.skipIf(
    TEST_WITH_ASAN, "DataLoader tests hang in ASAN, see: https://github.com/pytorch/pytorch/issues/66223")
class TestDataLoaderPersistentWorkers(TestDataLoader):

    def setUp(self):
        super(TestDataLoaderPersistentWorkers, self).setUp()
        self.persistent_workers = True

    @unittest.skipIf(IS_SANDCASTLE, "subprocess doesn't work in FB internal CI")
    @unittest.skipIf(IS_WINDOWS, "No 'resource' module on Windows")
    def test_fd_limit_exceeded(self):
        # See NOTE [ DataLoader on Linux and open files limit ]
        import subprocess
        subprocess.check_output([sys.executable, '-c', """\
import torch
import resource
from torch.utils.data import DataLoader, IterableDataset

class RandomDataset(IterableDataset):
    def __init__(self, len, size):
        super(RandomDataset).__init__()
        self.len = len
        self.size = size

    def __iter__(self):
        return self

    def __next__(self):
        if self.len <= 0:
            raise StopIteration
        self.len -= 1
        return torch.randn(self.size)

try:
    keep_fds_alive = []
    resource.setrlimit(resource.RLIMIT_NOFILE, (100, 100))
    for random_t in DataLoader(RandomDataset(200, (2,2)), multiprocessing_context="fork",
                               num_workers=1, persistent_workers=True):
      random_t.max(dim=0)
      keep_fds_alive.append(random_t)
except RuntimeError as e:
    assert "ulimit -n" in str(e)
    assert "set_sharing_strategy" in str(e)
"""])

    def test_dataset_not_reset(self):
        dataset = DummyDataset()
        pin_memory_configs = [False]
        if TEST_CUDA:
            pin_memory_configs.append(True)
        for pin_memory in pin_memory_configs:
            dataloader = self._get_data_loader(dataset, num_workers=2, pin_memory=pin_memory)
            dataset.start = 0
            for i in range(10):
                for x in dataloader:
                    pass
                # Changing the start value here doesn't have any effect in the dataset
                # cached by the workers. since they are not recreated between epochs
                # and can cache values safely
                dataset.start = i

    @unittest.skipIf(IS_SANDCASTLE, "subprocess doesn't work in FB internal CI")
    @unittest.skipIf(IS_WINDOWS, "Needs fork")
    def test_early_exit(self):
        import subprocess
        proc = subprocess.check_output([sys.executable, '-c', """\
import torch
from torch.utils.data import DataLoader, IterableDataset

class RandomDataset(IterableDataset):
    def __init__(self, len, size):
        super(RandomDataset).__init__()
        self.len = len
        self.size = size

    def __iter__(self):
        return self

    def __next__(self):
        if self.len <= 0:
            raise StopIteration
        self.len -= 1
        return torch.randn(self.size)

if __name__ == '__main__':
    dl = DataLoader(
        RandomDataset(64, (28, 28)),
        batch_size=16,
        num_workers=2,
        pin_memory=True,
        persistent_workers=True,
        multiprocessing_context="fork",
    )

    for _ in dl:
        break
"""])


class NamedTupleDataset(Dataset):
    from collections import namedtuple
    Batch = namedtuple('Batch', ['data', 'label', 'random_tensor'])
    Data = namedtuple('Data', ['positive', 'negative'])

    def __len__(self):
        return 4

    def __getitem__(self, ndx):
        return self.Batch(data=self.Data(positive=ndx, negative=-ndx),
                          label=str(ndx), random_tensor=torch.randn(3))


@unittest.skipIf(
    TEST_WITH_TSAN,
    "Fails with TSAN with the following error: starting new threads after multi-threaded "
    "fork is not supported. Dying (set die_after_fork=0 to override)")
class TestNamedTupleDataLoader(TestCase):
    def setUp(self):
        super(TestNamedTupleDataLoader, self).setUp()
        self.dataset = NamedTupleDataset()

    def test_dataloader_with_namedtuple(self):
        # auto-collation
        loader = DataLoader(self.dataset, batch_size=2, pin_memory=TEST_CUDA)
        for batch in loader:
            self.assertIsInstance(batch, NamedTupleDataset.Batch)
            self.assertEqual(batch.random_tensor.is_pinned(), TEST_CUDA)
            self.assertIsInstance(batch.data, NamedTupleDataset.Data)
            self.assertIsInstance(batch.data.positive, torch.Tensor)
            self.assertEqual(batch.data.positive.is_pinned(), TEST_CUDA)
        # no auto-collation
        loader = DataLoader(self.dataset, batch_size=None, pin_memory=TEST_CUDA)
        for batch in loader:
            self.assertIsInstance(batch, NamedTupleDataset.Batch)
            self.assertEqual(batch.random_tensor.is_pinned(), TEST_CUDA)
            self.assertIsInstance(batch.data, NamedTupleDataset.Data)
            self.assertNotIsInstance(batch.data.positive, torch.Tensor)

class SimpleCustomBatch(object):
    def __init__(self, data):
        transposed_data = list(zip(*data))
        self.inp = torch.stack(transposed_data[0], 0)
        self.tgt = torch.stack(transposed_data[1], 0)

    def pin_memory(self):
        self.inp = self.inp.pin_memory()
        self.tgt = self.tgt.pin_memory()
        return self

    def is_pinned(self):
        return self.inp.is_pinned() and self.tgt.is_pinned()

# Workaround for https://github.com/pytorch/pytorch/issues/50661
# Classes from  `__main__` can not be correctly unpickled from spawned module
# See https://docs.python.org/3/library/multiprocessing.html#multiprocessing-programming
self_module = __import__(os.path.splitext(os.path.basename(__file__))[0])

def collate_wrapper(batch):
    return self_module.SimpleCustomBatch(batch)


def collate_into_packed_sequence(batch):
    data = torch.stack([sample[0] for sample in batch], 1)
    t, b = data.size()
    lengths = torch.randint(1, t, size=(b,), dtype=torch.int64)
    return torch.nn.utils.rnn.pack_padded_sequence(data, lengths, enforce_sorted=False)


def collate_into_packed_sequence_batch_first(batch):
    data = torch.stack([sample[0] for sample in batch], 0)
    b, t = data.size()
    lengths = torch.randint(1, t, size=(b,), dtype=torch.int64)
    return torch.nn.utils.rnn.pack_padded_sequence(data, lengths, batch_first=True, enforce_sorted=False)


@unittest.skipIf(
    TEST_WITH_TSAN,
    "Fails with TSAN with the following error: starting new threads after multi-threaded "
    "fork is not supported. Dying (set die_after_fork=0 to override)")
class TestCustomPinFn(TestCase):
    def setUp(self):
        super(TestCustomPinFn, self).setUp()
        inps = torch.arange(10 * 5, dtype=torch.float32).view(10, 5)
        tgts = torch.arange(10 * 5, dtype=torch.float32).view(10, 5)
        self.dataset = TensorDataset(inps, tgts)

    @unittest.skipIf(not TEST_CUDA, "CUDA unavailable")
    def test_custom_batch_pin(self):
        test_cases = [
            (collate_wrapper, self_module.SimpleCustomBatch),
            (collate_into_packed_sequence, torch.nn.utils.rnn.PackedSequence),
            (collate_into_packed_sequence_batch_first, torch.nn.utils.rnn.PackedSequence),
        ]
        for collate_fn, elem_cls in test_cases:
            loader = DataLoader(self.dataset, batch_size=2, collate_fn=collate_fn,
                                pin_memory=True)
            for sample in loader:
                self.assertIsInstance(sample, elem_cls)
                self.assertTrue(sample.is_pinned())

    @unittest.skipIf(not TEST_CUDA, "CUDA unavailable")
    def test_custom_batch_pin_worker(self):
        test_cases = [
            (collate_wrapper, self_module.SimpleCustomBatch),
            (collate_into_packed_sequence, torch.nn.utils.rnn.PackedSequence),
            (collate_into_packed_sequence_batch_first, torch.nn.utils.rnn.PackedSequence),
        ]
        for collate_fn, elem_cls in test_cases:
            loader = DataLoader(self.dataset, batch_size=2, collate_fn=collate_fn,
                                pin_memory=True, num_workers=1)
            for sample in loader:
                self.assertIsInstance(sample, elem_cls)
                self.assertTrue(sample.is_pinned())


class TestWorkerQueueDataset(Dataset):
    def __init__(self, data):
        self.data = data
        self.worker_id = None

    def worker_init_fn(self, worker_id):
        self.worker_id = worker_id

    def __getitem__(self, item):
        return self.worker_id, self.data[item]

    def __len__(self):
        return len(self.data)


@unittest.skipIf(
    TEST_WITH_TSAN,
    "Fails with TSAN with the following error: starting new threads after multi-threaded "
    "fork is not supported. Dying (set die_after_fork=0 to override)")
@unittest.skipIf(
    TEST_WITH_ASAN,
    "Flaky with ASAN, see https://github.com/pytorch/pytorch/issues/65727")
class TestIndividualWorkerQueue(TestCase):
    def setUp(self):
        super(TestIndividualWorkerQueue, self).setUp()
        self.dataset = TestWorkerQueueDataset(list(range(128)))

    def _run_ind_worker_queue_test(self, batch_size, num_workers):
        loader = DataLoader(
            self.dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers,
            timeout=5, worker_init_fn=self.dataset.worker_init_fn
        )
        current_worker_idx = 0
        for i, (worker_ids, sample) in enumerate(loader):
            self.assertEqual(worker_ids.tolist(), [current_worker_idx] * batch_size)
            self.assertEqual(sample.tolist(), list(range(i * batch_size, (i + 1) * batch_size)))
            current_worker_idx += 1
            if current_worker_idx == num_workers:
                current_worker_idx = 0

    def test_ind_worker_queue(self):
        max_num_workers = None
        if hasattr(os, 'sched_getaffinity'):
            try:
                max_num_workers = len(os.sched_getaffinity(0))
            except Exception:
                pass
        if max_num_workers is None:
            cpu_count = os.cpu_count()
            if cpu_count is not None:
                # Use half number of CPUs
                max_num_workers = cpu_count // 2

        if max_num_workers is None:
            max_num_workers = 1

        for batch_size in (8, 16, 32, 64):
            for num_workers in range(0, min(6, max_num_workers)):
                self._run_ind_worker_queue_test(batch_size=batch_size, num_workers=num_workers + 1)


class SetAffinityDataset(IterableDataset):

    def __iter__(self):
        torch.randperm(1)
        after = os.sched_getaffinity(0)
        return iter(after)

@unittest.skipIf(
    not hasattr(os, 'sched_setaffinity'),
    "os.sched_setaffinity is not available")
class TestSetAffinity(TestCase):
    def test_set_affinity_in_worker_init(self):
        # Query the current affinity mask to avoid setting a disallowed one
        old_affinity = os.sched_getaffinity(0)
        if not old_affinity:
            self.skipTest("No affinity information")
        # Choose any
        expected_affinity = list(old_affinity)[-1]

        def worker_set_affinity(_):
            os.sched_setaffinity(0, [expected_affinity])


        dataset = SetAffinityDataset()

        dataloader = torch.utils.data.DataLoader(
            dataset, num_workers=2, worker_init_fn=worker_set_affinity)
        for sample in dataloader:
            self.assertEqual(sample, [expected_affinity])

class ConvDataset(Dataset):
    def __init__(self):
        self.x = torch.ones(1, 1, 24000)
        # Call convolution on parent process
        self[0]

    def __len__(self):
        return 1

    def __getitem__(self, index):
        return torch.nn.functional.conv1d(self.x, torch.ones(1, 1, 2))


@unittest.skipIf(IS_WINDOWS, "Needs fork")
class TestConvAfterFork(TestCase):
    # Tests crash reported in https://github.com/pytorch/pytorch/issues/53565
    def test_conv_after_fork(self):
        loader = DataLoader(ConvDataset(), num_workers=1)
        for x in loader:
            self.assertEqual(x.shape, (1, 1, 1, 23999))


if __name__ == '__main__':
    run_tests()