File: test_ops.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 (1966 lines) | stat: -rw-r--r-- 83,887 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
# Owner(s): ["module: unknown"]

from collections.abc import Sequence
from functools import partial
import warnings
import unittest
import itertools
import torch
import contextlib
from collections import defaultdict
from importlib import import_module
from torch.utils._pytree import tree_map

from torch.testing import make_tensor
from torch.testing._internal.common_dtype import (
    floating_and_complex_types_and,
    all_types_and_complex_and,
)
from test_proxy_tensor import xfail, skip, skipOps

from torch.testing._internal.common_utils import (
    TestCase,
    is_iterable_of_tensors,
    run_tests,
    IS_SANDCASTLE,
    clone_input_helper,
    IS_CI,
    suppress_warnings,
    noncontiguous_like,
    TEST_WITH_ASAN,
    TEST_WITH_UBSAN,
    skipIfRocm,
    IS_WINDOWS,
    IS_FBCODE,
    first_sample,
    parametrize,
    skipIfSlowGradcheckEnv,
)
from torch.testing._internal.common_methods_invocations import (
    op_db,
    UnaryUfuncInfo,
    ReductionOpInfo,
    ReductionPythonRefInfo,
    SpectralFuncInfo,
    ops_and_refs,
    python_ref_db,
    BinaryUfuncInfo,
)
from torch.testing._internal.common_device_type import (
    deviceCountAtLeast,
    instantiate_device_type_tests,
    ops,
    onlyCUDA,
    onlyCPU,
    onlyNativeDeviceTypes,
    OpDTypes,
    skipCUDAIfRocm,
    skipMeta,
)
from torch._subclasses.fake_tensor import (
    FakeTensor,
    FakeTensorMode,
)
from torch._subclasses.fake_utils import outputs_alias_inputs

import torch._prims as prims
from torch._prims.context import TorchRefsMode

from torch.testing._internal import opinfo
from torch.testing._internal import composite_compliance

from torch.utils._pytree import tree_flatten
from torch.utils._python_dispatch import TorchDispatchMode

# TODO: fixme https://github.com/pytorch/pytorch/issues/68972
torch.set_default_dtype(torch.float32)

# variant testing is only done with torch.float and torch.cfloat to avoid
#   excessive test times and maximize signal to noise ratio
_variant_ops = partial(
    ops, dtypes=OpDTypes.supported, allowed_dtypes=(torch.float, torch.cfloat)
)

# Get names of all the operators which have ref in their entry in OpInfo (testing infra)
#   except for elementwise unary operators (separately implemented in test/test_unary_ufuncs.py),
#   elementwise binary operators (separately implemented in test_binary_ufuncs.py),
#   reduction operations (separately impelemented in test_reductions.py),
#   and Spectral Functions (separately implemented for only 1D as of now, in test/test_spectral_ops.py)
_ref_test_ops = tuple(
    filter(
        lambda op: not isinstance(
            op, (UnaryUfuncInfo, ReductionOpInfo, SpectralFuncInfo, BinaryUfuncInfo)
        )
        and op.ref is not None,
        op_db,
    )
)
_ops_and_refs = op_db + python_ref_db

aten = torch.ops.aten

# Tests that apply to all operators and aren't related to any particular
#   system
@skipIfSlowGradcheckEnv
class TestCommon(TestCase):
    exact_dtype = True

    # Verifies, on teardown, that no OpInfo is still using dynamic dtypes in CI
    @classmethod
    def tearDownClass(cls):
        super().tearDownClass()

        if IS_CI:
            err_msg = (
                "The operator(s) below is(are) using dynamic_dtypes in the OpInfo entries."
                "This is OK for testing, but be sure to set the dtypes manually before landing your PR!"
            )
            # Assure no opinfo entry has dynamic_dtypes
            filtered_ops = list(filter(opinfo.utils.is_dynamic_dtype_set, op_db))
            for op in filtered_ops:
                fmt_str = opinfo.utils.str_format_dynamic_dtype(op)
                err_msg += "\n" + fmt_str

            assert len(filtered_ops) == 0, err_msg

    # Validates that each OpInfo works correctly on different CUDA devices
    @onlyCUDA
    @deviceCountAtLeast(2)
    @ops(op_db, allowed_dtypes=(torch.float32, torch.long))
    def test_multiple_devices(self, devices, dtype, op):
        for cuda_device_str in devices:
            cuda_device = torch.device(cuda_device_str)
            # NOTE: only tests on first sample
            samples = op.sample_inputs(cuda_device, dtype)
            sample = first_sample(self, samples)
            result = op(sample.input, *sample.args, **sample.kwargs)

            if isinstance(result, torch.Tensor):
                self.assertTrue(result.device == cuda_device)
            elif is_iterable_of_tensors(result):
                self.assertTrue(all(map(lambda t: t.device == cuda_device, result)))
            else:
                self.skipTest(
                    "Skipped! Only supports single tensor or iterable of tensor outputs."
                )

    # Tests that the function and its (ndarray-accepting) reference produce the same
    #   values on the tensors from sample_inputs func for the corresponding op.
    # This test runs in double and complex double precision because
    # NumPy does computation internally using double precision for many functions
    # resulting in possible equality check failures.
    @unittest.skipIf(TEST_WITH_ASAN, "Skipped under ASAN")
    @onlyNativeDeviceTypes
    @suppress_warnings
    @ops(_ref_test_ops, allowed_dtypes=(torch.float64, torch.long, torch.complex128))
    def test_numpy_ref(self, device, dtype, op):
        try:
            # Sets the default dtype to NumPy's default dtype of double
            cur_default = torch.get_default_dtype()
            torch.set_default_dtype(torch.double)
            for sample_input in op.reference_inputs(device, dtype):
                self.compare_with_reference(
                    op, op.ref, sample_input, exact_dtype=(dtype is not torch.long)
                )
        finally:
            torch.set_default_dtype(cur_default)

    # Tests that experimental Python References can propagate shape, dtype,
    # and device metadata properly.
    # See https://github.com/pytorch/pytorch/issues/78050 for a discussion of stride propagation.
    @unittest.skipIf(TEST_WITH_ASAN, "Skipped under ASAN")
    @onlyNativeDeviceTypes
    @ops(python_ref_db)
    def test_python_ref_meta(self, device, dtype, op):
        with FakeTensorMode() as mode:
            pass

        def _to_tensormeta(x):
            if isinstance(x, torch.Tensor):
                out = FakeTensor.from_tensor(x, mode)
                return out
            return x

        # TODO: iterate over requires_grad true/false
        for sample in op.reference_inputs(device, dtype, requires_grad=False):
            result = op(sample.input, *sample.args, **sample.kwargs)

            meta_sample = sample.transform(_to_tensormeta)
            try:
                with mode:
                    meta_result = op(meta_sample.input, *meta_sample.args, **meta_sample.kwargs)
            except torch._subclasses.fake_tensor.UnsupportedFakeTensorException:
                continue
            except torch._subclasses.fake_tensor.DataDependentOutputException:
                continue

            if isinstance(result, torch.Tensor):
                self.assertTrue(isinstance(meta_result, FakeTensor))
                prims.utils.compare_tensor_meta(result, meta_result)
            elif isinstance(result, Sequence):
                for a, b in zip(result, meta_result):
                    if isinstance(a, torch.Tensor) or isinstance(b, torch.Tensor):
                        self.assertTrue(isinstance(b, FakeTensor))
                        prims.utils.compare_tensor_meta(a, b)

    def _ref_test_helper(
        self,
        ctx,
        device,
        dtype,
        op,
        skip_zero_numel=False,
        skip_zero_dim=False,
        skip_bfloat=False,
        skip_view_consistency=False,
    ):
        # NOTE: this test works by comparing the reference
        ex = None
        for sample in op.reference_inputs(device, dtype, requires_grad=False):
            if isinstance(sample.input, torch.Tensor) and sample.input.numel() == 0 and skip_zero_numel:
                continue
            if isinstance(sample.input, torch.Tensor) and sample.input.ndim == 0 and skip_zero_dim:
                continue

            is_lower_than_cuda11_0 = (
                (torch.version.cuda is not None)
                and ([int(x) for x in torch.version.cuda.split(".")] < [11, 0]))

            if (
                skip_bfloat
                and is_lower_than_cuda11_0
                and (
                    (
                        isinstance(sample.input, torch.Tensor)
                        and sample.input.dtype == torch.bfloat16
                    )
                    or any(
                        isinstance(arg, torch.Tensor) and arg.dtype == torch.bfloat16
                        for arg in sample.args
                    )
                )
            ):
                continue
            with ctx():
                ref_result = op(sample.input, *sample.args, **sample.kwargs)
            torch_result = op.torch_opinfo(sample.input, *sample.args, **sample.kwargs)

            for a, b in zip(tree_flatten(ref_result)[0], tree_flatten(torch_result)[0]):
                if isinstance(a, torch.Tensor) or isinstance(b, torch.Tensor):
                    prims.utils.compare_tensor_meta(a, b)
                    if getattr(op, 'validate_view_consistency', True) and not skip_view_consistency:
                        msg = (f"The torch implementation {'returns' if b._is_view() else 'does not return'} "
                               f"a view, while the reference {'does' if a._is_view() else 'does not'}")
                        self.assertEqual(a._is_view(), b._is_view(), msg)

            # Computes the dtype the more precise computatino would occur in
            precise_dtype = torch.bool
            if prims.utils.is_integer_dtype(dtype):
                # Note: bool and integer dtypes do not have more
                # precise dtypes -- they simply must be close
                precise_dtype = dtype
            if prims.utils.is_float_dtype(dtype):
                precise_dtype = torch.double
            if prims.utils.is_complex_dtype(dtype):
                precise_dtype = torch.cdouble

            # Checks if the results are close
            try:
                self.assertEqual(
                    ref_result,
                    torch_result,
                    exact_stride=False,
                    exact_device=True,
                    exact_layout=True,
                    exact_is_coalesced=True,
                )
            except AssertionError as e:
                # Raises the error if the precise dtype comparison wouldn't be
                # different
                if dtype is precise_dtype:
                    raise e

                ex = e


            # Goes to next sample if these results are close
            if not ex:
                continue

            # If the results are not close, checks that the
            # reference is more accurate than the torch op
            def _make_precise(x):
                if isinstance(x, torch.dtype):
                    return precise_dtype
                if isinstance(x, torch.Tensor) and x.dtype is dtype:
                    return x.to(precise_dtype)
                return x

            precise_sample = sample.transform(_make_precise)
            precise_result = op.torch_opinfo(precise_sample.input, *precise_sample.args, **precise_sample.kwargs)

            def _distance(a, b):
                # Special-cases boolean comparisons
                if prims.utils.is_boolean_dtype(a.dtype):
                    assert b.dtype is torch.bool
                    return (a ^ b).sum()

                same = (a == b)
                if prims.utils.is_float_dtype(a.dtype) or prims.utils.is_complex_dtype(a.dtype):
                    same = torch.logical_or(same, torch.logical_and(torch.isnan(a), torch.isnan(b)))

                actual_error = torch.where(same, 0, torch.abs(a - b)).sum()
                return actual_error

            ref_distance = 0
            for a, b in zip(tree_flatten(ref_result)[0], tree_flatten(precise_result)[0]):
                ref_distance = ref_distance + _distance(a, b)

            torch_distance = 0
            for a, b in zip(tree_flatten(torch_result)[0], tree_flatten(precise_result)[0]):
                torch_distance = torch_distance + _distance(a, b)

            # TODO: consider adding some tolerance to this comparison
            msg = f"Reference result was farther ({ref_distance}) from the precise " \
                  f"computation than the torch result was ({torch_distance})!"
            self.assertTrue(ref_distance <= torch_distance, msg=msg)

        # Reports numerical accuracy discrepancies
        if ex is not None:
            msg = "Test passed because the reference was more accurate than the torch operator."
            warnings.warn(msg)

    # Tests that experimental Python References perform the same computation
    # as the operators they reference, when operator calls in the torch
    # namesapce are remapped to the refs namespace (torch.foo becomes refs.foo).
    @unittest.skipIf(TEST_WITH_ASAN, "Skipped under ASAN")
    @onlyNativeDeviceTypes
    @ops(python_ref_db)
    def test_python_ref(self, device, dtype, op):
        # In this test, primTorch refs call into the refs namespace
        # For example, a ref with torch.foo in it will calls refs.foo instead
        # Direct calls to refs and prims are not affected
        self._ref_test_helper(lambda: TorchRefsMode(strict=True), device, dtype, op)

    # Tests that experimental Python References perform the same computation
    # as the operators they reference, when operator calls in the torch
    # namespace are preserved (torch.foo remains torch.foo).
    @unittest.skipIf(TEST_WITH_ASAN, "Skipped under ASAN")
    @onlyNativeDeviceTypes
    @ops(python_ref_db)
    def test_python_ref_torch_fallback(self, device, dtype, op):
        # In this test, refs call into the torch namespace (after the initial invocation)
        # For example, a ref with torch.foo in it will call torch.foo instead of refs.foo
        # Direct calls to refs and prims are not translated
        self._ref_test_helper(contextlib.nullcontext, device, dtype, op)

    @unittest.skipIf(TEST_WITH_ASAN, "Skipped under ASAN")
    @onlyCUDA
    @skipCUDAIfRocm
    @ops(python_ref_db)
    @parametrize('executor', ['aten', 'nvfuser'])
    def test_python_ref_executor(self, device, dtype, op, executor):
        # TODO: Not all dtypes are supported with nvfuser
        from torch._prims_common import _torch_dtype_to_nvfuser_dtype_map
        if executor == "nvfuser" and dtype not in _torch_dtype_to_nvfuser_dtype_map:
            raise unittest.SkipTest(f"nvfuser doesn't support dtype {dtype}")

        # nvFuser tests are rather slow so we only run int32 and float32 types
        if executor == "nvfuser" and dtype not in [torch.int32, torch.float32]:
            raise unittest.SkipTest("skipped for speed")

        if executor == "nvfuser" and not op.supports_nvfuser:
            raise unittest.SkipTest(f"{op.name} doesn't support nvfuser")

        # nvFuser doesn't support reduction operations on 0-dim tensors yet
        skip_zero_dim = False
        if executor == "nvfuser" and isinstance(op, ReductionPythonRefInfo):
            skip_zero_dim = True

        # skip zero-dim tensors for some composites of reduction operations
        normalization_ops = ["_refs.softmax", "_refs.logsumexp", "_refs.log_softmax", "_refs.sum_to_size"]
        if executor == "nvfuser" and op.name in normalization_ops:
            skip_zero_dim = True

        from torch._prims.executor import make_traced
        from copy import copy
        op = copy(op)
        executor = "strictly_nvfuser" if executor == "nvfuser" else executor
        op.op = partial(make_traced(op.op), executor=executor)
        self._ref_test_helper(
            contextlib.nullcontext,
            device,
            dtype,
            op,
            skip_zero_numel=("nvfuser" in executor),  # nvfuser doesn't support zero-sized tensors
            skip_zero_dim=skip_zero_dim,
            skip_bfloat=("nvfuser" in executor),  # nvfuser doesn't support bfloat tensors for pre-11 cuda TK
            # # nvfuser doesn't support view consistency
            # https://github.com/pytorch/pytorch/issues/84863
            skip_view_consistency=("nvfuser" in executor),
        )

    @skipMeta
    @onlyNativeDeviceTypes
    @ops([op for op in op_db if op.error_inputs_func is not None], dtypes=OpDTypes.none)
    def test_errors(self, device, op):
        error_inputs = op.error_inputs(device)
        for ei in error_inputs:
            si = ei.sample_input
            with self.assertRaisesRegex(ei.error_type, ei.error_regex):
                op(si.input, *si.args, **si.kwargs)

    @skipMeta
    @onlyNativeDeviceTypes
    @ops([op for op in python_ref_db if op.error_inputs_func is not None], dtypes=OpDTypes.none)
    def test_python_ref_errors(self, device, op):
        mode = FakeTensorMode()
        with mode:
            pass

        def _to_tensormeta(x):
            if isinstance(x, torch.Tensor):
                return FakeTensor.from_tensor(x, mode)
            return x

        error_inputs = op.error_inputs(device)
        for ei in error_inputs:
            si = ei.sample_input
            meta_sample = si.transform(_to_tensormeta)
            # TODO: match strings
            with self.assertRaisesRegex(ei.error_type, ""):
                op(meta_sample.input, *meta_sample.args, **meta_sample.kwargs)

    # Tests that the function produces the same result when called with
    #   noncontiguous tensors.
    # TODO: get working with Windows by addressing failing operators
    # TODO: get working with ASAN by addressing failing operators
    @unittest.skipIf(IS_WINDOWS, "Skipped under Windows")
    @unittest.skipIf(TEST_WITH_ASAN, "Skipped under ASAN")
    @onlyNativeDeviceTypes
    @suppress_warnings
    @ops(op_db, allowed_dtypes=(torch.float32, torch.long, torch.complex64))
    def test_noncontiguous_samples(self, device, dtype, op):
        test_grad = dtype in op.supported_backward_dtypes(torch.device(device).type)
        sample_inputs = op.sample_inputs(device, dtype, requires_grad=test_grad)
        for sample_input in sample_inputs:
            t_inp, t_args, t_kwargs = (
                sample_input.input,
                sample_input.args,
                sample_input.kwargs,
            )
            noncontig_sample = sample_input.noncontiguous()
            n_inp, n_args, n_kwargs = (
                noncontig_sample.input,
                noncontig_sample.args,
                noncontig_sample.kwargs,
            )

            # Verifies sample input tensors should have no grad or history
            sample_tensor = t_inp if isinstance(t_inp, torch.Tensor) else t_inp[0]
            assert sample_tensor.grad is None
            assert sample_tensor.grad_fn is None

            # validates forward
            expected = op(t_inp, *t_args, **t_kwargs)
            actual = op(n_inp, *n_args, **n_kwargs)

            self.assertEqual(actual, expected)

            # Validate backward
            # Short-circuits if the op doesn't support grad in this device x dtype
            if not test_grad:
                continue

            expected = sample_input.output_process_fn_grad(expected)
            actual = sample_input.output_process_fn_grad(actual)

            if isinstance(expected, torch.Tensor):
                grad_for_expected = torch.randn_like(expected)
                grad_for_actual = noncontiguous_like(grad_for_expected)
            elif isinstance(expected, Sequence):
                # Filter output elements that do not require grad
                expected = [
                    t
                    for t in expected
                    if isinstance(t, torch.Tensor) and t.requires_grad
                ]
                actual = [
                    n for n in actual if isinstance(n, torch.Tensor) and n.requires_grad
                ]
                grad_for_expected = [torch.randn_like(t) for t in expected]
                grad_for_actual = [noncontiguous_like(n) for n in grad_for_expected]
            else:
                # Nothing to do if it returns a scalar or things like that
                continue

            # Concatenate inputs into a tuple
            t_inputs = (
                (t_inp,) + t_args
                if isinstance(t_inp, torch.Tensor)
                else tuple(t_inp) + t_args
            )
            n_inputs = (
                (n_inp,) + n_args
                if isinstance(n_inp, torch.Tensor)
                else tuple(n_inp) + n_args
            )

            # Filter the elemnts that are tensors that require grad
            t_input_tensors = [
                t for t in t_inputs if isinstance(t, torch.Tensor) and t.requires_grad
            ]
            n_input_tensors = [
                n for n in n_inputs if isinstance(n, torch.Tensor) and n.requires_grad
            ]

            self.assertEqual(len(t_input_tensors), len(n_input_tensors))

            # Some functions may not use all the inputs to generate gradients. One of the
            # few examples of this "odd" behaviour is F.hinge_embedding_loss
            t_grads = torch.autograd.grad(
                expected, t_input_tensors, grad_for_expected, allow_unused=True
            )
            n_grads = torch.autograd.grad(
                actual, n_input_tensors, grad_for_actual, allow_unused=True
            )

            msg = "Got different gradients for contiguous / non-contiguous inputs wrt input {}."
            for i, (t, n) in enumerate(zip(t_grads, n_grads)):
                self.assertEqual(t, n, msg=msg.format(i))

    # Separates one case from the following test_out because many ops don't properly implement the
    #   incorrectly sized out parameter warning properly yet
    # Cases test here:
    #   - out= with the correct dtype and device, but the wrong shape
    @ops(_ops_and_refs, dtypes=OpDTypes.none)
    def test_out_warning(self, device, op):
        # Prefers running in float32 but has a fallback for the first listed supported dtype
        supported_dtypes = op.supported_dtypes(self.device_type)
        if len(supported_dtypes) == 0:
            self.skipTest("Skipped! Op has not supported dtypes on this device.")
        dtype = (
            torch.float32
            if torch.float32 in supported_dtypes
            else list(supported_dtypes)[0]
        )

        samples = op.sample_inputs(device, dtype)
        for sample in samples:
            # calls it normally to get the expected result
            expected = op(sample.input, *sample.args, **sample.kwargs)
            op_out = partial(op, sample.input, *sample.args, **sample.kwargs)

            # Short-circuits if output is not a single tensor or an
            #   iterable of tensors
            if not isinstance(expected, torch.Tensor) and not is_iterable_of_tensors(
                expected, include_empty=True
            ):
                self.skipTest(
                    "Skipped! Only supports single tensor or iterable of tensor outputs."
                )

            # Validates the op doesn't support out if it claims not to
            if not op.supports_out:
                with self.assertRaises(Exception):
                    assert op_out(out=expected) != NotImplemented
                return

            # A wrapper around map that works with single tensors and always
            #   instantiates the map. Used below to apply transforms to
            #   single tensor and iterable tensor outputs.
            def _apply_out_transform(fn, out):
                if isinstance(out, torch.Tensor):
                    return fn(out)

                # assumes (see above) that out is an iterable of tensors
                return tuple(map(fn, out))

            # Extracts strides from a tensor or iterable of tensors into a tuple
            def _extract_strides(out):
                if isinstance(out, torch.Tensor):
                    return (out.stride(),)

                # assumes (see above) that out is an iterable of tensors
                return tuple(map(lambda t: t.stride(), out))

            # Extracts data pointers from a tensor or iterable of tensors into a tuple
            # NOTE: only extracts on the CPU and CUDA device types since some
            #   device types don't have storage
            def _extract_data_ptrs(out):
                if self.device_type != "cpu" and self.device_type != "cuda":
                    return ()

                if isinstance(out, torch.Tensor):
                    return (out.data_ptr(),)

                # assumes (see above) that out is an iterable of tensors
                return tuple(map(lambda t: t.data_ptr(), out))

            @suppress_warnings
            def _compare_out(transform, *, compare_strides_and_data_ptrs=True):
                out = _apply_out_transform(transform, expected)
                original_strides = _extract_strides(out)
                original_ptrs = _extract_data_ptrs(out)

                op_out(out=out)
                final_strides = _extract_strides(out)
                final_ptrs = _extract_data_ptrs(out)

                self.assertEqual(expected, out)

                if compare_strides_and_data_ptrs:
                    stride_msg = "Strides are not the same! Original strides were {0} and strides are now {1}".format(
                        original_strides, final_strides
                    )
                    self.assertEqual(original_strides, final_strides, msg=stride_msg)
                    self.assertEqual(original_ptrs, final_ptrs)

            # Case Zero: out= with the correct dtype and device, but the wrong shape
            #   Expected behavior: if nonempty, resize with a warning.
            def _case_zero_transform(t):
                wrong_shape = list(t.shape)

                if len(wrong_shape) == 0:
                    # Handles scalar tensor case (empty list)
                    wrong_shape = [2]
                else:
                    wrong_shape[-1] = wrong_shape[-1] + 1
                return make_tensor(wrong_shape, dtype=t.dtype, device=t.device)

            # Verifies the out values are correct
            _compare_out(_case_zero_transform, compare_strides_and_data_ptrs=False)

            # Additionally validates that the appropriate warning is thrown if a nonempty
            #   tensor is resized.
            def _any_nonempty(out):
                if isinstance(out, torch.Tensor):
                    return out.numel() > 0

                return any(x.numel() > 0 for x in out)

            out = _apply_out_transform(_case_zero_transform, expected)
            msg_fail = "Resized a non-empty tensor but did not warn about it."
            if _any_nonempty(out):
                with self.assertWarnsRegex(
                    UserWarning, "An output with one or more elements", msg=msg_fail
                ):
                    op_out(out=out)

    # Validates ops implement the correct out= behavior
    # See https://github.com/pytorch/pytorch/wiki/Developer-FAQ#how-does-out-work-in-pytorch
    #   for a description of the correct behavior
    # Validates the following cases:
    #   - Case 0: out has the correct shape, dtype, and device but is full of extremal values
    #   - Case 1: out has the correct shape, dtype, and device but is noncontiguous
    #   - Case 2: out has the correct dtype and device, but is zero elements
    #   - Case 3: out has the correct shape and dtype, but is on a different device type
    #   - Case 4: out has the correct shape and device, but a dtype that cannot
    #       "safely" cast to
    #
    # Case 3 and 4 are slightly different when the op is a factory function:
    #   - if device, dtype are NOT passed, any combination of dtype/device should be OK for out
    #   - if device, dtype are passed, device and dtype should match
    @ops(_ops_and_refs, dtypes=OpDTypes.any_one)
    def test_out(self, device, dtype, op):
        # Prefers running in float32 but has a fallback for the first listed supported dtype
        samples = op.sample_inputs(device, dtype)
        for sample in samples:
            # calls it normally to get the expected result
            expected = op(sample.input, *sample.args, **sample.kwargs)
            op_out = partial(op, sample.input, *sample.args, **sample.kwargs)

            # Short-circuits if output is not a single tensor or an
            #   iterable of tensors
            if not isinstance(expected, torch.Tensor) and not is_iterable_of_tensors(
                expected, include_empty=True
            ):
                self.skipTest(
                    "Skipped! Only supports single tensor or iterable of tensor outputs."
                )

            # Validates the op doesn't support out if it claims not to
            if not op.supports_out:
                with self.assertRaises(Exception):
                    assert op_out(out=expected) != NotImplemented
                return

            # A wrapper around map that works with single tensors and always
            #   instantiates the map. Used below to apply transforms to
            #   single tensor and iterable tensor outputs.
            def _apply_out_transform(fn, out):
                if isinstance(out, torch.Tensor):
                    return fn(out)

                # assumes (see above) that out is an iterable of tensors
                return tuple(map(fn, out))

            # Extracts strides from a tensor or iterable of tensors into a tuple
            def _extract_strides(out):
                if isinstance(out, torch.Tensor):
                    return (out.stride(),)

                # assumes (see above) that out is an iterable of tensors
                return tuple(map(lambda t: t.stride(), out))

            # Extracts data pointers from a tensor or iterable of tensors into a tuple
            # NOTE: only extracts on the CPU and CUDA device types since some
            #   device types don't have storage
            def _extract_data_ptrs(out):
                if self.device_type != "cpu" and self.device_type != "cuda":
                    return ()

                if isinstance(out, torch.Tensor):
                    return (out.data_ptr(),)

                # assumes (see above) that out is an iterable of tensors
                return tuple(map(lambda t: t.data_ptr(), out))

            def _compare_out(transform, *, compare_strides_and_data_ptrs=True):
                out = _apply_out_transform(transform, expected)
                original_strides = _extract_strides(out)
                original_ptrs = _extract_data_ptrs(out)

                op_out(out=out)
                final_strides = _extract_strides(out)
                final_ptrs = _extract_data_ptrs(out)
                self.assertEqual(expected, out)

                if compare_strides_and_data_ptrs:
                    stride_msg = "Strides are not the same! Original strides were {0} and strides are now {1}".format(
                        original_strides, final_strides
                    )
                    self.assertEqual(original_strides, final_strides, msg=stride_msg)
                    self.assertEqual(original_ptrs, final_ptrs)

            # Case 0: out= with the correct shape, dtype, and device
            #   but NaN values for floating point and complex tensors, and
            #   maximum values for integer tensors.
            #   Expected behavior: out= values have no effect on the computation.
            def _case_zero_transform(t):
                try:
                    info = torch.iinfo(t.dtype)
                    return torch.full_like(t, info.max)
                except TypeError as te:
                    # for non-integer types fills with NaN
                    return torch.full_like(t, float("nan"))


            _compare_out(_case_zero_transform)

            # Case 1: out= with the correct shape, dtype, and device,
            #   but noncontiguous.
            #   Expected behavior: strides are respected and `out` storage is not changed.
            def _case_one_transform(t):
                return make_tensor(
                    t.shape, dtype=t.dtype, device=t.device, noncontiguous=True
                )

            _compare_out(_case_one_transform)

            # Case 2: out= with the correct dtype and device, but has no elements.
            #   Expected behavior: resize without warning.
            def _case_two_transform(t):
                return make_tensor((0,), dtype=t.dtype, device=t.device)

            _compare_out(_case_two_transform, compare_strides_and_data_ptrs=False)

            # Also validates that no warning is thrown when this out is resized
            out = _apply_out_transform(_case_two_transform, expected)
            with warnings.catch_warnings(record=True) as caught:
                warnings.simplefilter("always")
                op_out(out=out)

            # Verifies no warning is a resize warning
            for w in caught:
                if "An output with one or more elements" in str(w.message):
                    self.fail(
                        "Resizing an out= argument with no elements threw a resize warning!"
                    )

            # Case 3: out= with correct shape and dtype, but wrong device.
            wrong_device = None
            if torch.device(device).type != "cpu":
                wrong_device = "cpu"
            elif torch.cuda.is_available():
                wrong_device = "cuda"


            factory_fn_msg = (
                "\n\nNOTE: If your op is a factory function (i.e., it accepts TensorOptions) you should mark its "
                "OpInfo with `is_factory_function=True`."
            )
            if wrong_device is not None:

                def _case_three_transform(t):
                    return make_tensor(t.shape, dtype=t.dtype, device=wrong_device)

                out = _apply_out_transform(_case_three_transform, expected)

                if op.is_factory_function and sample.kwargs.get("device", None) is None:
                    op_out(out=out)
                else:
                    msg_fail = (
                        f"Expected RuntimeError when calling with input.device={device} and out.device={wrong_device}."
                    ) + factory_fn_msg
                    with self.assertRaises(RuntimeError, msg=msg_fail):
                        op_out(out=out)

            # Case 4: out= with correct shape and device, but a dtype
            #   that output cannot be "safely" cast to (long).
            #   Expected behavior: error.
            # NOTE: this case is filtered by dtype since some ops produce
            #   bool tensors, for example, which can be safely cast to any
            #   dtype. It is applied when single tensors are floating point or complex
            #   dtypes, or if an op returns multiple tensors when at least one such
            #   tensor is a floating point or complex dtype.
            _dtypes = floating_and_complex_types_and(torch.float16, torch.bfloat16)
            if (
                isinstance(expected, torch.Tensor)
                and expected.dtype in _dtypes
                or (
                    not isinstance(expected, torch.Tensor)
                    and any(t.dtype in _dtypes for t in expected)
                )
            ):

                def _case_four_transform(t):
                    return make_tensor(t.shape, dtype=torch.long, device=t.device)

                out = _apply_out_transform(_case_four_transform, expected)
                msg_fail = "Expected RuntimeError when doing an unsafe cast!"
                msg_fail = (
                    msg_fail
                    if not isinstance(expected, torch.Tensor)
                    else (
                        "Expected RuntimeError when doing an unsafe cast from a result of dtype "
                        f"{expected.dtype} into an out= with dtype torch.long"
                    )
                ) + factory_fn_msg

                if op.is_factory_function and sample.kwargs.get("dtype", None) is None:
                    op_out(out=out)
                else:
                    with self.assertRaises(RuntimeError, msg=msg_fail):
                        op_out(out=out)

    # Tests that the forward and backward passes of operations produce the
    #   same values for the cross-product of op variants (method, inplace)
    #   against eager's gold standard op function variant
    @_variant_ops(op_db)
    def test_variant_consistency_eager(self, device, dtype, op):
        # Acquires variants (method variant, inplace variant, operator variant, inplace_operator variant, aliases)

        method = op.method_variant
        inplace = op.inplace_variant
        operator = op.operator_variant
        inplace_operator = op.inplace_operator_variant


        # list of all inplace ops: inplace variant + alias inplace variants if exist
        inplace_ops = [inplace, inplace_operator]
        variants = [method, inplace, operator, inplace_operator]
        operators = [operator, inplace_operator]

        for a_op in op.aliases:
            variants.append(a_op.op)
            variants.append(a_op.method_variant)
            variants.append(a_op.inplace_variant)
            inplace_ops.append(a_op.inplace_variant)

        inplace_variants = tuple(filter(None, inplace_ops))
        variants = tuple(filter(None, variants))
        operators = tuple(filter(None, operators))

        _requires_grad = dtype in op.supported_backward_dtypes(
            torch.device(device).type
        )

        include_conjugated_inputs = op.test_conjugated_samples and dtype.is_complex
        samples = op.sample_inputs(
            device,
            dtype,
            requires_grad=_requires_grad,
            include_conjugated_inputs=include_conjugated_inputs,
        )
        samples = list(samples)

        def _test_consistency_helper(samples, variants):
            for sample in samples:
                # TODO: Check grad for all Tensors requiring grad if sample.input is TensorList
                tensor = (
                    sample.input
                    if isinstance(sample.input, torch.Tensor)
                    else sample.input[0]
                )

                # Computes function forward and backward values
                tensor.grad = None
                expected_forward = op(sample.input, *sample.args, **sample.kwargs)
                expected_grad = None

                output_process_fn_grad = (
                    sample.output_process_fn_grad
                    if sample.output_process_fn_grad
                    else lambda x: x
                )

                # Skips inplace variants if the output dtype is not the same as
                #   the input dtype
                skip_inplace = False
                if (
                    isinstance(expected_forward, torch.Tensor)
                    and expected_forward.dtype is not tensor.dtype
                ):
                    skip_inplace = True

                # TODO: backward consistency only supported for single tensor outputs
                # TODO: backward consistency only checked on sample.input, not all
                #   tensor inputs
                # TODO: update to handle checking grads of all tensor inputs as
                #   derived from each tensor output
                if isinstance(
                    expected_forward, torch.Tensor
                ) and dtype in op.supported_backward_dtypes(torch.device(device).type):
                    output_process_fn_grad(expected_forward).sum().backward()
                    expected_grad = tensor.grad

                # Test eager consistency
                for variant in variants:
                    # Skips inplace ops
                    if variant in inplace_ops and skip_inplace:
                        continue

                    # Compares variant's forward
                    # Note: copies the to-be-modified input when testing the inplace variant
                    tensor.grad = None
                    cloned = (
                        clone_input_helper(sample.input)
                        if variant in inplace_ops
                        else sample.input
                    )

                    if variant in inplace_ops and sample.broadcasts_input:
                        with self.assertRaises(
                            RuntimeError,
                            msg=(
                                "inplace variant either incorrectly allowed "
                                "resizing or you have marked the sample {}"
                                " incorrectly with `broadcasts_self=True".format(
                                    sample.summary()
                                )
                            ),
                        ):
                            variant_forward = variant(
                                cloned, *sample.args, **sample.kwargs
                            )
                        continue

                    if variant in operators and sample.kwargs:
                        # skip samples with kwargs for operator variants
                        continue

                    variant_forward = variant(cloned, *sample.args, **sample.kwargs)
                    self.assertEqual(expected_forward, variant_forward)

                    # Compares variant's backward
                    if expected_grad is not None and (
                        variant not in inplace_ops or op.supports_inplace_autograd
                    ):
                        output_process_fn_grad(variant_forward).sum().backward()
                        self.assertEqual(expected_grad, tensor.grad)

        _test_consistency_helper(samples, variants)

        def _test_inplace_preserve_storage(samples, variants):
            for sample in samples:
                # Skips inplace variants if the output dtype is not the same as
                #   the input dtype
                expected_forward = op(sample.input, *sample.args, **sample.kwargs)
                tensor = (
                    sample.input
                    if isinstance(sample.input, torch.Tensor)
                    else sample.input[0]
                )
                skip_inplace = False
                if (
                    isinstance(expected_forward, torch.Tensor)
                    and expected_forward.dtype is not tensor.dtype
                ):
                    skip_inplace = True
                if skip_inplace:
                    return
                for variant in variants:
                    cloned = (
                        clone_input_helper(sample.input)
                        if variant in inplace_ops
                        else sample.input
                    )
                    inp_tensor = (
                        cloned if isinstance(cloned, torch.Tensor) else cloned[0]
                    )
                    data_ptr = inp_tensor.data_ptr()
                    if variant in operators and sample.kwargs:
                        # skip samples with kwargs for operator variants
                        continue

                    variant_forward = variant(cloned, *sample.args, **sample.kwargs)
                    # TODO Support non-tensor outputs if they exist for inplace ops
                    if isinstance(variant_forward, torch.Tensor):
                        self.assertEqual(
                            data_ptr, variant_forward.data_ptr(), atol=0, rtol=0
                        )
                    else:
                        self.assertTrue(
                            False,
                            "Non-tensor outputs for inplace ops are not supported",
                        )

        if len(inplace_ops) > 0:
            inplace_samples = list(
                filter(lambda sample: not sample.broadcasts_input, samples)
            )
            _test_inplace_preserve_storage(inplace_samples, inplace_variants)

    # Reference testing for operations in complex32 against complex64.
    # NOTE: We test against complex64 as NumPy doesn't have a complex32 equivalent dtype.
    @ops(op_db, allowed_dtypes=(torch.complex32,))
    def test_complex_half_reference_testing(self, device, dtype, op):
        if not op.supports_dtype(torch.complex32, device):
            unittest.skip("Does not support complex32")

        for sample in op.sample_inputs(device, dtype):
            actual = op(sample.input, *sample.args, **sample.kwargs)
            # sample.transform applies the lambda to torch.Tensor and torch.dtype.
            # However, we only want to apply it to Tensors with dtype `torch.complex32`..
            transformed_sample = sample.transform(lambda x: x.to(torch.complex64) if isinstance(
                x, torch.Tensor) and x.dtype is torch.complex32 else x)
            expected = op(
                transformed_sample.input,
                *transformed_sample.args,
                **transformed_sample.kwargs,
            )
            # Since range of chalf is much less compared to cfloat,
            # we get `inf`s easily (eg. with `pow`, `exp`),
            # so we cast `cfloat` back to `chalf`.
            expected = tree_map(lambda x: x.to(torch.complex32) if isinstance(
                x, torch.Tensor) and x.dtype is torch.complex64 else x, expected)

            # `exact_dtype` is False because for ops like real, imag
            # we get different dtypes for `actual` and `expected`
            # `chalf` input -> `half` output
            # `cfloat` input -> `float` output
            self.assertEqual(actual, expected, exact_dtype=False)

    @ops(op_db, allowed_dtypes=(torch.bool,))
    @unittest.skipIf(TEST_WITH_UBSAN, "Test uses undefined behavior")
    def test_non_standard_bool_values(self, device, dtype, op):
        # Test boolean values other than 0x00 and 0x01 (gh-54789)
        def convert_boolean_tensors(x):
            if not isinstance(x, torch.Tensor) or x.dtype != torch.bool:
                return x

            # Map False -> 0 and True -> Random value in [2, 255]
            true_vals = torch.randint(2, 255, x.shape, dtype=torch.uint8, device=x.device)
            false_vals = torch.zeros((), dtype=torch.uint8, device=x.device)
            x_int = torch.where(x, true_vals, false_vals)

            ret = x_int.view(torch.bool)
            self.assertEqual(ret, x)
            return ret

        for sample in op.sample_inputs(device, dtype):
            expect = op(sample.input, *sample.args, **sample.kwargs)

            transformed = sample.transform(convert_boolean_tensors)
            actual = op(transformed.input, *transformed.args, **transformed.kwargs)

            self.assertEqual(expect, actual)

    # Validates that each OpInfo specifies its forward and backward dtypes
    #   correctly for CPU and CUDA devices
    @unittest.skipIf(TEST_WITH_ASAN, "Skipped under ASAN")
    @skipMeta
    @onlyNativeDeviceTypes
    @ops(ops_and_refs, dtypes=OpDTypes.none)
    def test_dtypes(self, device, op):
        # Check complex32 support only if the op claims.
        # TODO: Once the complex32 support is better, we should add check for complex32 unconditionally.
        device_type = torch.device(device).type
        include_complex32 = (
            (torch.complex32,)
            if op.supports_dtype(torch.complex32, device_type)
            else ()
        )

        # dtypes to try to backward in
        allowed_backward_dtypes = floating_and_complex_types_and(
            *((torch.half, torch.bfloat16) + include_complex32)
        )

        # lists for (un)supported dtypes
        supported_dtypes = set()
        unsupported_dtypes = set()
        supported_backward_dtypes = set()
        unsupported_backward_dtypes = set()

        def unsupported(dtype):
            unsupported_dtypes.add(dtype)
            if dtype in allowed_backward_dtypes:
                unsupported_backward_dtypes.add(dtype)

        for dtype in all_types_and_complex_and(
            *((torch.half, torch.bfloat16, torch.bool) + include_complex32)
        ):
            # tries to acquire samples - failure indicates lack of support
            requires_grad = dtype in allowed_backward_dtypes
            try:
                samples = tuple(
                    op.sample_inputs(device, dtype, requires_grad=requires_grad)
                )
            except Exception as e:
                unsupported(dtype)
                continue

            for sample in samples:
                # tries to call operator with the sample - failure indicates
                #   lack of support
                try:
                    result = op(sample.input, *sample.args, **sample.kwargs)
                    supported_dtypes.add(dtype)
                except Exception as e:
                    # NOTE: some ops will fail in forward if their inputs
                    #   require grad but they don't support computing the gradient
                    #   in that type! This is a bug in the op!
                    unsupported(dtype)
                    continue

                # Checks for backward support in the same dtype, if the input has
                # one or more tensors requiring grad
                def _tensor_requires_grad(x):
                    if isinstance(x, dict):
                        for k, v in x.items():
                            if _tensor_requires_grad(v):
                                return True
                    if isinstance(x, (list, tuple)):
                        for a in x:
                            if _tensor_requires_grad(a):
                                return True
                    if isinstance(x, torch.Tensor) and x.requires_grad:
                        return True

                    return False

                requires_grad = _tensor_requires_grad(sample.input) \
                    or _tensor_requires_grad(sample.args) or _tensor_requires_grad(sample.kwargs)
                if not requires_grad:
                    continue

                try:
                    result = sample.output_process_fn_grad(result)
                    if isinstance(result, torch.Tensor):
                        backward_tensor = result
                    elif isinstance(result, Sequence) and isinstance(
                        result[0], torch.Tensor
                    ):
                        backward_tensor = result[0]
                    else:
                        continue

                    # Note: this grad may not have the same dtype as dtype
                    # For functions like complex (float -> complex) or abs
                    #   (complex -> float) the grad tensor will have a
                    #   different dtype than the input.
                    #   For simplicity, this is still modeled as these ops
                    #   supporting grad in the input dtype.
                    grad = torch.randn_like(backward_tensor)
                    backward_tensor.backward(grad)
                    supported_backward_dtypes.add(dtype)
                except Exception as e:
                    unsupported_backward_dtypes.add(dtype)

        # Checks that dtypes are listed correctly and generates an informative
        #   error message

        supported_forward = supported_dtypes - unsupported_dtypes
        partially_supported_forward = supported_dtypes & unsupported_dtypes
        unsupported_forward = unsupported_dtypes - supported_dtypes
        supported_backward = supported_backward_dtypes - unsupported_backward_dtypes
        partially_supported_backward = (
            supported_backward_dtypes & unsupported_backward_dtypes
        )
        unsupported_backward = unsupported_backward_dtypes - supported_backward_dtypes

        device_type = torch.device(device).type

        claimed_forward = set(op.supported_dtypes(device_type))
        supported_but_unclaimed_forward = supported_forward - claimed_forward
        claimed_but_unsupported_forward = claimed_forward & unsupported_forward

        claimed_backward = set(op.supported_backward_dtypes(device_type))
        supported_but_unclaimed_backward = supported_backward - claimed_backward
        claimed_but_unsupported_backward = claimed_backward & unsupported_backward

        # Partially supporting a dtype is not an error, but we print a warning
        if (len(partially_supported_forward) + len(partially_supported_backward)) > 0:
            msg = "Some dtypes for {0} on device type {1} are only partially supported!\n".format(
                op.name, device_type
            )
            if len(partially_supported_forward) > 0:
                msg = (
                    msg
                    + "The following dtypes only worked on some samples during forward: {0}.\n".format(
                        partially_supported_forward
                    )
                )
            if len(partially_supported_backward) > 0:
                msg = (
                    msg
                    + "The following dtypes only worked on some samples during backward: {0}.\n".format(
                        partially_supported_backward
                    )
                )
            print(msg)

        if (
            len(supported_but_unclaimed_forward)
            + len(claimed_but_unsupported_forward)
            + len(supported_but_unclaimed_backward)
            + len(claimed_but_unsupported_backward)
        ) == 0:
            return

        # Reference operators often support additional dtypes, and that's OK
        if op in python_ref_db:
            if (
                len(claimed_but_unsupported_forward)
                + len(claimed_but_unsupported_backward)
            ) == 0:
                return

        # Generates error msg
        msg = "The supported dtypes for {0} on device type {1} are incorrect!\n".format(
            op.name, device_type
        )
        if len(supported_but_unclaimed_forward) > 0:
            msg = (
                msg
                + "The following dtypes worked in forward but are not listed by the OpInfo: {0}.\n".format(
                    supported_but_unclaimed_forward
                )
            )
        if len(supported_but_unclaimed_backward) > 0:
            msg = (
                msg
                + "The following dtypes worked in backward but are not listed by the OpInfo: {0}.\n".format(
                    supported_but_unclaimed_backward
                )
            )
        if len(claimed_but_unsupported_forward) > 0:
            msg = (
                msg
                + "The following dtypes did not work in forward but are listed by the OpInfo: {0}.\n".format(
                    claimed_but_unsupported_forward
                )
            )
        if len(claimed_but_unsupported_backward) > 0:
            msg = (
                msg
                + "The following dtypes did not work in backward but are listed by the OpInfo: {0}.\n".format(
                    claimed_but_unsupported_backward
                )
            )

        self.fail(msg)


class TestCompositeCompliance(TestCase):
    # Checks if the operator (if it is composite) is written to support most
    # backends and Tensor subclasses. See "CompositeImplicitAutograd Compliance"
    # in aten/src/ATen/native/README.md for more details
    @unittest.skipIf(
        IS_FBCODE or IS_SANDCASTLE, "__torch_dispatch__ does not work in fbcode"
    )
    @ops(op_db, allowed_dtypes=(torch.float,))
    def test_operator(self, device, dtype, op):
        samples = op.sample_inputs(device, dtype, requires_grad=False)

        for sample in samples:
            args = [sample.input] + list(sample.args)
            kwargs = sample.kwargs
            composite_compliance.check_with_mode(op, args, kwargs, self.assertEqual)
            composite_compliance.check_all_permutations(op, args, kwargs, self.assertEqual)

    @unittest.skipIf(
        IS_FBCODE or IS_SANDCASTLE, "__torch_dispatch__ does not work in fbcode"
    )
    @ops([op for op in op_db if op.supports_autograd], allowed_dtypes=(torch.float,))
    def test_backward(self, device, dtype, op):
        samples = op.sample_inputs(device, dtype, requires_grad=True)

        for sample in samples:
            args = [sample.input] + list(sample.args)
            kwargs = sample.kwargs
            # We pass assertEqual so that decorators like `toleranceOverride`
            # actually work (otherwise they silently do nothing!)
            composite_compliance.check_backward_formula(
                op.get_op(), args, kwargs,
                sample.output_process_fn_grad,
                op.gradcheck_wrapper, self.assertEqual)

    @unittest.skipIf(
        IS_FBCODE or IS_SANDCASTLE, "__torch_dispatch__ does not work in fbcode"
    )
    @ops(op_db, allowed_dtypes=(torch.float,))
    def test_forward_ad(self, device, dtype, op):
        if torch.float not in op.supported_backward_dtypes(device):
            raise unittest.SkipTest("Does not support autograd")

        if not op.supports_forward_ad:
            raise unittest.SkipTest("Does not support forward_ad")

        samples = op.sample_inputs(device, dtype, requires_grad=True)

        for sample in samples:
            args = [sample.input] + list(sample.args)
            kwargs = sample.kwargs
            # We pass assertEqual so that decorators like `toleranceOverride`
            # actually work (otherwise they silently do nothing!)
            composite_compliance.check_forward_ad_formula(
                op.get_op(), args, kwargs, op.gradcheck_wrapper, self.assertEqual)


@skipIfSlowGradcheckEnv
class TestMathBits(TestCase):
    # Tests that
    # 1. The operator's output for physically conjugated/negated tensors and conjugate/negative view tensors
    # produces the same value
    # 2. The gradients are same in both cases mentioned in (1)
    # 3. If the operator's inplace variant is supported, tests that the inplace operation
    #    produces the correct value when called on a conjugate/negative view tensor and that the output
    #    has its conj/neg bit set to true
    # This test only runs for C -> R and C -> C functions
    # TODO: add tests for `R->C` functions
    # Note: This test runs for functions that take both tensors and tensorlists as input.
    def _test_math_view(
        self,
        device,
        dtype,
        op,
        samples,
        math_op_physical,
        math_op_view,
        is_bit_set,
        out_type,
    ):
        inplace_variant = op.inplace_variant

        # helper function to clone and conjugate/negate the input if its a tensor
        # else clone the sequence and conjugate/negate the first element in the sequence
        # If a requires_grad argument is provided the tensor being conjugated/negated will
        # have its requires_grad set to that value.
        def clone_and_perform_view(input, **kwargs):
            if isinstance(input, torch.Tensor):
                requires_grad = kwargs.get("requires_grad", input.requires_grad)
                with torch.no_grad():
                    # Ensure view represents the original sample input
                    input = math_op_physical(input)
                # Note: .conj() is not called under no_grad mode since it's not allowed to modify a
                # view created in no_grad mode. Here it's ok to do so, so as a workaround we call conj
                # before resetting the requires_grad field for input
                input = math_op_view(input)
                assert input.is_leaf
                return input.requires_grad_(requires_grad)

            if isinstance(input, Sequence):
                out = list(map(clone_input_helper, input))
                out[0] = clone_and_perform_view(out[0])
                return tuple(out)

        for sample in samples:
            tensor = (
                sample.input
                if isinstance(sample.input, torch.Tensor)
                else sample.input[0]
            )
            cloned1 = clone_and_perform_view(sample.input)

            # Computes function forward value with a physically conjugated/negated tensor and
            # a conj/neg view tensor and verifies that the output in both case are equal.
            expected_forward = op(sample.input, *sample.args, **sample.kwargs)
            forward_with_mathview = op(cloned1, *sample.args, **sample.kwargs)
            self.assertEqual(expected_forward, forward_with_mathview)

            # If the op has an inplace variant, and the input doesn't require broadcasting
            # and has the same dtype as output, verify that the inplace operation on a conjugated/negated
            # input produces correct output, and the output tensor has the conj/neg bit set to True
            if inplace_variant is not None and not sample.broadcasts_input:
                cloned2 = clone_and_perform_view(tensor, requires_grad=False)
                if (
                    isinstance(expected_forward, torch.Tensor)
                    and expected_forward.dtype is tensor.dtype
                ):
                    inplace_forward = inplace_variant(
                        cloned2, *sample.args, **sample.kwargs
                    )
                    self.assertTrue(is_bit_set(inplace_forward))
                    self.assertEqual(inplace_forward, expected_forward)

            # TODO: backward consistency only supported for single tensor outputs
            # TODO: backward consistency only checked on sample.input, not all
            #   tensor inputs
            # TODO: update to handle checking grads of all tensor inputs as
            #   derived from each tensor output
            if (
                isinstance(expected_forward, torch.Tensor)
                and expected_forward.requires_grad
            ):
                output_process_fn_grad = sample.output_process_fn_grad or (lambda x: x)
                expected_forward = output_process_fn_grad(expected_forward)
                forward_with_mathview = output_process_fn_grad(forward_with_mathview)

                tensor = (
                    sample.input
                    if isinstance(sample.input, torch.Tensor)
                    else sample.input[0]
                )
                expected_forward.sum().backward(retain_graph=True)
                forward_with_mathview.sum().backward(retain_graph=True)
                if tensor.grad is not None:
                    cloned1_tensor = (
                        cloned1 if isinstance(cloned1, torch.Tensor) else cloned1[0]
                    )
                    self.assertEqual(tensor.grad, cloned1_tensor.grad)

                    tensor.grad, cloned1_tensor.grad = None, None

                    # a repeat of the above test if output is not complex valued
                    if out_type(expected_forward):
                        grad = torch.randn_like(expected_forward)
                        expected_forward.backward(grad)
                        forward_with_mathview.backward(
                            math_op_view(math_op_physical(grad))
                        )

                        self.assertEqual(tensor.grad, cloned1_tensor.grad)

    @ops(ops_and_refs, allowed_dtypes=(torch.cfloat,))
    def test_conj_view(self, device, dtype, op):
        if not op.test_conjugated_samples:
            self.skipTest("Operation doesn't support conjugated inputs.")
        math_op_physical = torch.conj_physical
        math_op_view = torch.conj
        _requires_grad = torch.cfloat in op.supported_backward_dtypes(
            torch.device(device).type
        )
        is_bit_set = torch.is_conj
        samples = op.sample_inputs(device, dtype, requires_grad=_requires_grad)
        self._test_math_view(
            device,
            dtype,
            op,
            samples,
            math_op_physical,
            math_op_view,
            is_bit_set,
            torch.is_complex,
        )

    @ops(ops_and_refs, allowed_dtypes=(torch.double,))
    def test_neg_view(self, device, dtype, op):
        if not op.test_neg_view:
            self.skipTest("Operation not tested with tensors with negative bit.")
        math_op_physical = torch.neg
        math_op_view = torch._neg_view
        is_bit_set = torch.is_neg
        samples = op.sample_inputs(device, dtype, requires_grad=op.supports_autograd)
        self._test_math_view(
            device,
            dtype,
            op,
            samples,
            math_op_physical,
            math_op_view,
            is_bit_set,
            lambda x: True,
        )

    @ops(ops_and_refs, allowed_dtypes=(torch.cdouble,))
    def test_neg_conj_view(self, device, dtype, op):
        if not op.test_neg_view:
            self.skipTest("Operation not tested with tensors with negative bit.")
        if not op.test_conjugated_samples:
            self.skipTest("Operation doesn't support conjugated inputs.")

        def math_op_physical(x):
            return -x.conj_physical()

        def math_op_view(x):
            return torch._neg_view(x).conj()

        def is_bit_set(x):
            return torch.is_neg(x) and torch.is_conj(x)

        _requires_grad = dtype in op.supported_backward_dtypes(
            torch.device(device).type
        )
        samples = op.sample_inputs(device, dtype, requires_grad=_requires_grad)
        # Only test one sample
        samples = itertools.islice(samples, 1)
        self._test_math_view(
            device,
            dtype,
            op,
            samples,
            math_op_physical,
            math_op_view,
            is_bit_set,
            torch.is_complex,
        )

# input strides and size may have been altered due to the result of an inplace op
def check_inplace_view(func, input, rs, input_size, input_strides):
    if func is None:
        return
    # TODO: extend this test to test ops with multiple outputs and ops like native_batch_norm.out
    # which mutate not necessarily the first input.
    if isinstance(rs, torch.Tensor) and rs is input:
        unequal_size = rs.size() != input_size
        unequal_strides = rs.stride() != input_strides
        # resize_ should probably have inplace_view tag. Not adding the tag since it
        # breaks some codegen logic
        if (unequal_size or unequal_strides):
            if isinstance(func, torch._ops.OpOverloadPacket):
                func = func.default
            # Reference: https://github.com/pytorch/pytorch/issues/78759
            if func is not torch.ops.aten.resize_.default:
                # TODO: use self.assertIn when we have separate tests for each tag
                assert torch.Tag.inplace_view in func.tags

# A mode that when enabled runs correctness checks to ensure
# that operators have expected tags based on their input and
# ouput tensor properties
@skipIfSlowGradcheckEnv
class TestTagsMode(TorchDispatchMode):
    def __torch_dispatch__(self, func, types, args=(), kwargs=None):
        if isinstance(args[0], torch.Tensor):
            old_size = args[0].size()
            old_stride = args[0].stride()
            rs = func(*args, **kwargs)
            check_inplace_view(func, args[0], rs, old_size, old_stride)
        else:
            rs = func(*args, **kwargs)
        return rs

# Test to verify the correctness for tags in `tags.yaml`, also available for access through `torch.Tags`
@skipIfSlowGradcheckEnv
class TestTags(TestCase):
    @onlyCPU
    @ops(ops_and_refs, dtypes=OpDTypes.any_one)
    def test_tags(self, device, dtype, op):
        samples = op.sample_inputs(device, dtype, requires_grad=False)
        for sample in samples:
            # TODO: Test tags for ops that return a list of tensors
            input = sample.input
            if isinstance(input, torch.Tensor):
                old_size = input.size()
                old_stride = input.stride()
                with TestTagsMode():
                    rs = op(input, *sample.args, **sample.kwargs)
                # TODO: add test for aliases: https://github.com/pytorch/pytorch/issues/78761
                aten_name = op.aten_name if op.aten_name is not None else op.name
                opoverloadpacket = getattr(torch.ops.aten, aten_name, None)
                check_inplace_view(opoverloadpacket, input, rs, old_size, old_stride)


@skipIfSlowGradcheckEnv
class TestRefsOpsInfo(TestCase):

    import_paths = ["_refs", "_refs.special", "_refs.nn.functional", "_refs.fft"]
    module_alls = [(path, import_module(f"torch.{path}").__all__) for path in import_paths]
    ref_ops_names = tuple(itertools.chain.from_iterable(
        [f"{path}.{op}" for op in module_all] for path, module_all in module_alls))
    ref_db_names = set(ref_op.name for ref_op in python_ref_db)

    # TODO: References that do not have an entry in python_ref_db
    skip_ref_ops = {
        '_refs.bitwise_right_shift',
        '_refs.copy_to',
        '_refs.empty_strided',
        '_refs.equal',
        '_refs.full',
        '_refs.full_like',
        '_refs.item',
        '_refs.to',
        '_refs.ones',
        '_refs.ones_like',
        '_refs.std_var',
        '_refs.swap_axes',
        '_refs.uniform',
        '_refs.scalar_tensor',
        '_refs.trunc_divide',
        '_refs.zeros',
        '_refs.zeros_like',
        '_refs.rfloordiv',
        '_refs.rtruediv',
        '_refs.rpow',
        # These should be tested with their out-of-place counterparts
        '_refs.index_add_',
        '_refs.index_copy_',
        '_refs.index_fill_',
    }

    not_in_decomp_table = {
        # duplicated in _decomp and _refs
        '_refs.nn.functional.elu',
        '_refs.nn.functional.mse_loss',
        '_refs.var',
        '_refs.rsub',
        # duplicated due to efficiency concerns of the ref vs the decomp
        '_refs.index_add_',
        # these are not aten ops?
        '_refs.broadcast_shapes',
        '_refs.broadcast_tensors',
        '_refs.nn.functional.tanhshrink',
        '_refs.rfloordiv',
        '_refs.rtruediv',
        '_refs.rpow',
        # CompositeImplicitAutograd
        '_refs.allclose',
        '_refs.atleast_1d',
        '_refs.atleast_2d',
        '_refs.atleast_3d',
        '_refs.broadcast_to',
        '_refs.chunk',
        '_refs.column_stack',
        '_refs.contiguous',
        '_refs.dsplit',
        '_refs.dstack',
        '_refs.fill',
        '_refs.flatten',
        '_refs.fliplr',
        '_refs.flipud',
        '_refs.float_power',
        '_refs.hsplit',
        '_refs.hstack',
        '_refs.isclose',
        '_refs.isfinite',
        '_refs.isreal',
        '_refs.movedim',
        '_refs.narrow',
        '_refs.nn.functional.l1_loss',
        '_refs.nn.functional.poisson_nll_loss',
        '_refs.positive',
        '_refs.ravel',
        '_refs.reshape',
        '_refs.square',
        '_refs.tensor_split',
        '_refs.to',
        '_refs.true_divide',
        '_refs.trunc_divide',
        '_refs.vsplit',
        '_refs.vstack',
        '_refs.linalg.matrix_norm',
        '_refs.linalg.norm',
        '_refs.linalg.svd',
        '_refs.linalg.svdvals',
        '_refs.unflatten',
        '_refs.sum_to_size',
        # ref implementation missing kwargs
        '_refs.full',  # missing "layout"
        '_refs.full_like',  # missing "layout"
        '_refs.ones_like',  # missing "layout"
        '_refs.round',  # missing "decimals"
        '_refs.scalar_tensor',  # missing "layout"
        '_refs.zeros_like',  # missing "layout"
        # other
        '_refs.expand_as',
        '_refs.as_strided',  # _prims._as_strided_meta: "reduce() of empty sequence with no initial value"
        '_refs.copy_to',  # torch._C._jit_get_operation: No such operator aten::copy_to
        '_refs.equal',  # 'bool' object has no attribute 'dtype'
        '_refs.conj',  # Calls _prims.conj
        '_refs.real',
        '_refs.imag',
    }

    @parametrize("op", ref_ops_names)
    def test_refs_are_in_python_ref_db(self, op):
        if op in self.skip_ref_ops:
            raise unittest.SkipTest(f"{op} does not have an entry in python_ref_db")
        self.assertIn(op, self.ref_db_names)

    @parametrize("op", ref_ops_names)
    def test_refs_are_in_decomp_table(self, op):
        path = op.split('.')
        module_path = '.'.join(path[:-1])
        op_name = path[-1]
        op_impl = getattr(import_module(f"torch.{module_path}"), op_name)

        if op in self.not_in_decomp_table:
            self.assertNotIn(op_impl, torch._decomp.decomposition_table.values(),
                             f"Unexpectedly found {op} in torch._decomp.decomposition_table.values()")
        else:
            self.assertIn(op_impl, torch._decomp.decomposition_table.values(),
                          f"Did not find {op} in torch._decomp.decomposition_table.values()")


fake_skips = (
    "aminmax",  # failing input
    "cholesky",  # Could not run 'aten::cholesky' with arguments from the 'Meta' backend
    "cholesky_inverse",  # Could not run 'aten::cholesky' with arguments from the 'Meta' backend
    "cov",  # aweights cannot be negtaive
    "istft",  # window overlap add min: 0
    "linalg.eigvals",  # The tensor has a non-zero number of elements, but its data is not allocated yet
    "linalg.eigvalsh",  # aten::linalg_eigvalsh.out' with arguments from the 'Meta' backend
    "linalg.matrix_power",  # Could not run 'aten::eye.m_out' with arguments from the 'Meta' backend
    # "linalg.pinv",  # Could not run 'aten::pinv.out' with arguments from the 'Meta' backen
    "linalg.matrix_rank.hermitian",  # Could not run 'aten::linalg_eigvalsh.out' with arguments from the 'Meta' backend
    "linalg.pinv.hermitian",  # tensor.mH is only supported on matrices or batches of matrices. Got 1-D tensor
    "linalg.solve",  # Could not run 'aten::linalg_solve' with arguments from the 'Meta' backend
    "linalg.tensorsolve",  # Could not run 'aten::linalg_solve' with arguments from the 'Meta'
    "lu_solve",  # MALLOC ERROR: debug
    "multinomial",  # Could not run 'aten::multinomial' with arguments from the 'Meta' backend
    "mvlgamma.mvlgamma_p_1",  # Could not run 'aten::_local_scalar_dense' with arguments from the 'Meta' backend
    "mvlgamma.mvlgamma_p_3",  # Could not run 'aten::_local_scalar_dense' with arguments from the 'Meta' backend
    "mvlgamma.mvlgamma_p_5",  # Could not run 'aten::_local_scalar_dense' with arguments from the 'Meta' backend
    "nanmean",  # logical_not() got an unexpected keyword argument 'out'
    "quantile",  # quantile() q values must be in the range [0, 1]
    "nanquantile",  # quantile() q values must be in the range [0, 1]
    "nn.functional.ctc_loss",  # The tensor has a non-zero number of elements, but its data is not allocated yet
    "nn.functional.embedding_bag",  # sometimes errors
    "nn.functional.nll_loss",  # sometimes errors
    "nn.functional.max_pool1d",  # The tensor has a non-zero number of elements
    "to_sparse",  # Could not run 'aten::to_sparse' with arguments from the 'Meta' backend
    "tensor_split",  # The tensor has a non-zero number of elements, but its data is not allocated yet
    "repeat_interleave",  # cannot repeat_interleave a meta tensor without output_size
    "segment_reduce.lengths",  # Could not run 'aten::segment_reduce' with arguments from the 'Meta' backend.
    "sparse.sampled.addmm",  # sparsity not supported
    # Can not infer total number of classes from meta. no way at present to throw DynamicOutputShapeException
    "nn.functional.one_hot",
    "narrow",  # Fails only for one overload with DataDependentOutputException (hence skip).
)

fake_autocast_device_skips = defaultdict(dict)

# TODO: investigate/fix
fake_autocast_device_skips["cpu"] = set(
    ("linalg.pinv",)
)


dynamic_output_op_tests = (
    "argwhere",
    "bincount",
    "combinations",
    "linalg.lstsq",
    "masked_select",
    "nonzero",
    "unique_consecutive",
    "unique",
    "linalg.lstsq.grad_oriented",
)

# some inputs invoke dynamic output shape operators, some do not
sometimes_dynamic_output_op_test = (
    "__getitem__",
    "index_select",
)

data_dependent_op_tests = (
    "equal",
    "corrcoef",
    "nn.functional.gaussian_nll_loss",
    "allclose",
)

aliasing_failures = (
    "histogramdd",
    "nn.functional.pixel_shuffle",
    "nn.functional.pixel_unshuffle",
)

# tests which have inconsistent fake tensor stride propagation
# XXX: no new tests should be added to this list as a result of a
# decomp or prim, see https://github.com/pytorch/pytorch/issues/78050#issuecomment-1253950325
fake_tensor_stride_failing_ops = {
    "fft.fft2",
    "fft.fft",
    "fft.fftn",
    "fft.hfft2",
    "fft.hfft",
    "fft.hfftn",
    "fft.ifft2",
    "fft.ifft",
    "fft.ifftn",
    "fft.ihfft2",
    "fft.ihfft",
    "fft.ihfftn",
    "fft.irfft2",
    "fft.irfft",
    "fft.irfftn",
    "fft.rfft2",
    "fft.rfft",
    "fft.rfftn",
    "svd",
    "linalg.svd",
}

fake_backward_xfails = fake_tensor_stride_failing_ops | {
    "linalg.cond",
    "linalg.matrix_norm",
    "linalg.norm",
    "linalg.svd",
    "linalg.svdvals",
    "nn.functional.binary_cross_entropy_with_logits",
    "nn.functional.huber_loss",
    "nn.functional.logsigmoid",
    "nn.functional.multilabel_soft_margin_loss",
    "pca_lowrank",
    "roll",
    "svd_lowrank",
    "sgn",
    "cholesky",
    "linalg.eigh",
    "symeig",
}

fake_backward_xfails = {xfail(stride_skip) for stride_skip in fake_backward_xfails} | {
    xfail("segment_reduce", "lengths"),
    xfail("norm", "nuc"),
    xfail("linalg.norm", "subgradients_at_zero"),  # can accept vector inputs
    skip('nn.functional.ctc_loss'),
}

fake_autocast_backward_xfails = {
    skip("nn.functional.binary_cross_entropy"),
    skip("sparse.sampled_addmm"),
    skip("linalg.pinv"),
    skip("linalg.pinv", "hermitian"),
    skip("linalg.pinv", "singular"),
    skip('pinverse'),
}

@skipIfSlowGradcheckEnv
class TestFakeTensor(TestCase):
    def _test_fake_helper(self, device, dtype, op, context):
        name = op.name
        if op.variant_test_name:
            name += "." + op.variant_test_name
        if name in fake_skips or "sparse" in name or "jiterator" in name:
            self.skipTest("Skip failing test")

        samples = op.sample_inputs(device, dtype, requires_grad=False)
        for sample in samples:
            try:
                mode = FakeTensorMode(throw_on_data_dependent_ops=True)

                def map_to_fake(e):
                    if isinstance(e, torch.Tensor):
                        return mode.from_tensor(e)
                    else:
                        return e

                input = tree_map(map_to_fake, sample.input)
                args = tree_map(map_to_fake, sample.args)
                kwargs = tree_map(map_to_fake, sample.kwargs)

                try:
                    with context():
                        res = op(sample.input, *sample.args, **sample.kwargs)
                except Exception as e:
                    continue

                with context():
                    with mode:
                        res_fake = op(input, *args, **kwargs)


                for fake_out, real_out in zip(
                    tree_flatten(res_fake)[0], tree_flatten(res)[0]
                ):
                    if not isinstance(fake_out, torch.Tensor):
                        self.assertTrue(not isinstance(real_out, torch.Tensor))
                        continue

                    self.assertTrue(isinstance(fake_out, FakeTensor))
                    # if you see a shape exception here, you may need to add
                    # a `dynamic_output_shape` tag to an operator

                    check_strides = name not in fake_tensor_stride_failing_ops

                    # prims/decomps must correctly model strides,
                    # see https://github.com/pytorch/pytorch/issues/78050#issuecomment-1253950325
                    prims.utils.compare_tensor_meta(fake_out, real_out, check_strides)

                    if name not in aliasing_failures:
                        fake_aliasing = outputs_alias_inputs((input, args, kwargs), res_fake)
                        real_aliasing = outputs_alias_inputs((sample.input, sample, args, sample.kwargs), res)
                        self.assertEqual(fake_aliasing, real_aliasing)

                self.assertTrue(name not in dynamic_output_op_tests and name not in data_dependent_op_tests)

            except torch._subclasses.fake_tensor.UnsupportedFakeTensorException:
                pass
            except torch._subclasses.fake_tensor.DynamicOutputShapeException:
                self.assertTrue(name in dynamic_output_op_tests or name in sometimes_dynamic_output_op_test)
            except torch._subclasses.fake_tensor.DataDependentOutputException:
                self.assertTrue(name in data_dependent_op_tests)

    @ops(op_db, dtypes=OpDTypes.any_one)
    def test_fake(self, device, dtype, op):
        self._test_fake_helper(device, dtype, op, contextlib.nullcontext)

    @ops(op_db, dtypes=OpDTypes.any_one)
    def test_fake_autocast(self, device, dtype, op):
        if op.name in fake_autocast_device_skips[device]:
            self.skipTest("Skip failing test")
        context = torch.cuda.amp.autocast if device == "cuda" else torch.cpu.amp.autocast
        self._test_fake_helper(device, dtype, op, context)

    def _test_fake_crossref_helper(self, device, dtype, op, context):
        samples = op.sample_inputs(device, dtype, requires_grad=True)

        for iter, sample in enumerate(samples):
            args = [sample.input] + list(sample.args)
            kwargs = sample.kwargs

            # skip these to speed up tests
            common_skip_ops = (
                aten.detach.default,
                aten.empty_strided.default,
                aten.copy_.default,
                aten.is_same_size.default,
            )

            # TODO: enable check_aliasing, batch norm fails
            with torch._subclasses.CrossRefFakeMode(ignore_op_fn=lambda fn: fn in common_skip_ops, check_aliasing=True):
                with warnings.catch_warnings(), context():
                    composite_compliance.compute_expected_grads(
                        op.get_op(), args, kwargs,
                        sample.output_process_fn_grad,
                        op.gradcheck_wrapper)

    @skipIfRocm
    @onlyCUDA
    @ops([op for op in op_db if op.supports_autograd], allowed_dtypes=(torch.float,))
    @skipOps('TestFakeTensor', 'test_fake_crossref_backward_no_amp', fake_backward_xfails)
    def test_fake_crossref_backward_no_amp(self, device, dtype, op):
        self._test_fake_crossref_helper(device, dtype, op, contextlib.nullcontext)

    @skipIfRocm
    @onlyCUDA
    @ops([op for op in op_db if op.supports_autograd], allowed_dtypes=(torch.float,))
    @skipOps('TestFakeTensor', 'test_fake_crossref_backward_amp', fake_backward_xfails | fake_autocast_backward_xfails)
    def test_fake_crossref_backward_amp(self, device, dtype, op):
        self._test_fake_crossref_helper(device, dtype, op, torch.cuda.amp.autocast)


instantiate_device_type_tests(TestCommon, globals())
instantiate_device_type_tests(TestCompositeCompliance, globals())
instantiate_device_type_tests(TestMathBits, globals())
instantiate_device_type_tests(TestRefsOpsInfo, globals(), only_for="cpu")
instantiate_device_type_tests(TestFakeTensor, globals())
instantiate_device_type_tests(TestTags, globals())

if __name__ == "__main__":
    run_tests()