forked from pytorch/pytorch
-
Notifications
You must be signed in to change notification settings - Fork 0
/
common_methods_invocations.py
1788 lines (1694 loc) · 98.5 KB
/
common_methods_invocations.py
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
from functools import reduce
from operator import mul, itemgetter
import collections
import torch
import numpy as np
from torch._six import inf, istuple
from torch.autograd import Variable
from typing import List, Tuple, Dict, Any
from torch.testing import \
(make_non_contiguous, _dispatch_dtypes,
floating_types, floating_types_and, floating_and_complex_types,
floating_and_complex_types_and, all_types_and_complex_and, all_types_and)
from torch.testing._internal.common_device_type import \
(skipCUDAIfNoMagma, skipCPUIfNoLapack, expectedFailureCUDA,
expectedAlertNondeterministic, precisionOverride)
from torch.testing._internal.common_utils import \
(prod_single_zero, random_square_matrix_of_rank,
random_symmetric_matrix, random_symmetric_psd_matrix,
random_symmetric_pd_matrix, make_nonzero_det,
random_fullrank_matrix_distinct_singular_value, set_rng_seed,
TEST_WITH_ROCM, IS_WINDOWS, IS_MACOS, make_tensor)
class SkipInfo(object):
"""Describes which test, or type of tests, should be skipped when testing
an operator. Any test that matches all provided arguments will be skipped.
The skip will only be checked if the active_if argument is True."""
__slots__ = ['cls_name', 'test_name', 'device_type', 'dtypes', 'active_if']
def __init__(self, cls_name=None, test_name=None, *,
device_type=None, dtypes=None, active_if=True):
self.cls_name = cls_name
self.test_name = test_name
self.device_type = device_type
self.dtypes = dtypes
self.active_if = active_if
class SampleInput(object):
"""Represents sample inputs to a function."""
__slots__ = ['input', 'args', 'kwargs']
def __init__(self, input, *, args=tuple(), kwargs=None):
self.input = input
self.args = args
self.kwargs = kwargs if kwargs is not None else {}
# Classes and methods for the operator database
class OpInfo(object):
"""Operator information and helper functions for acquiring it."""
def __init__(self,
name, # the string name of the function
*,
op=None, # the function variant of the operation, populated as torch.<name> if None
dtypes=floating_types(), # dtypes this function is expected to work with
dtypesIfCPU=None, # dtypes this function is expected to work with on CPU
dtypesIfCUDA=None, # dtypes this function is expected to work with on CUDA
dtypesIfROCM=None, # dtypes this function is expected to work with on ROCM
test_inplace_grad=True, # whether to gradcheck and gradgradcheck the inplace variant
supports_tensor_out=True, # whether the op supports the out kwarg, returning a Tensor
skips=tuple(), # information about which tests to skip
decorators=None): # decorators to apply to generated tests
# Validates the dtypes are generated from the dispatch-related functions
for dtype_list in (dtypes, dtypesIfCPU, dtypesIfCUDA, dtypesIfROCM):
assert isinstance(dtype_list, (_dispatch_dtypes, type(None)))
self.name = name
self.dtypes = dtypes
self.dtypesIfCPU = dtypesIfCPU if dtypesIfCPU is not None else dtypes
self.dtypesIfCUDA = dtypesIfCUDA if dtypesIfCUDA is not None else dtypes
self.dtypesIfROCM = dtypesIfROCM if dtypesIfROCM is not None else dtypes
# NOTE: if the op is unspecified it is assumed to be under the torch namespace
if op is None:
assert hasattr(torch, self.name), f"Can't find torch.{self.name}"
self.op = op if op else getattr(torch, self.name)
self.method_variant = getattr(torch.Tensor, name) if hasattr(torch.Tensor, name) else None
inplace_name = name + "_"
self.inplace_variant = getattr(torch.Tensor, inplace_name) if hasattr(torch.Tensor, name) else None
self.test_inplace_grad = test_inplace_grad
self.supports_tensor_out = supports_tensor_out
self.skips = skips
self.decorators = decorators
def __call__(self, *args, **kwargs):
"""Calls the function variant of the operator."""
return self.op(*args, **kwargs)
def get_op(self):
"""Returns the function variant of the operator, torch.<op_name>."""
return self.op
def get_method(self):
"""Returns the method variant of the operator, torch.Tensor.<op_name>.
Returns None if the operator has no method variant.
"""
return self.method_variant
def get_inplace(self):
"""Returns the inplace variant of the operator, torch.Tensor.<op_name>_.
Returns None if the operator has no inplace variant.
"""
return self.inplace_variant
def sample_inputs(self, device, dtype, requires_grad=False):
"""Returns an iterable of SampleInputs."""
return tuple()
# Returns True if the test should be skipped and False otherwise
def should_skip(self, cls_name, test_name, device_type, dtype):
for si in self.skips:
if not si.active_if:
continue
cls_name_match = si.cls_name is None or cls_name == si.cls_name
name_match = si.test_name is None or test_name == si.test_name
device_type_match = si.device_type is None or device_type == si.device_type
dtype_match = si.dtypes is None or dtype in si.dtypes
if cls_name_match and name_match and device_type_match and dtype_match:
return True
return False
def supports_dtype(self, dtype, device_type):
if device_type == 'cpu':
return dtype in self.dtypesIfCPU
if device_type == 'cuda':
if TEST_WITH_ROCM:
return dtype in self.dtypesIfROCM
return dtype in self.dtypesIfCUDA
return dtype in self.dtypes
L = 20
M = 10
S = 5
# Metadata class for unary "universal functions (ufuncs)" that accept a single
# tensor and have common properties like:
class UnaryUfuncInfo(OpInfo):
"""Operator information for 'universal unary functions (unary ufuncs).'
These are functions of a single tensor with common properties like:
- they are elementwise functions
- the input shape is the output shape
- they typically have method and inplace variants
- they typically support the out kwarg
- they typically have NumPy or SciPy references
See NumPy's universal function documentation
(https://numpy.org/doc/1.18/reference/ufuncs.html) for more details
about the concept of ufuncs.
"""
def __init__(self,
name, # the string name of the function
*,
ref, # a reference function
dtypes=floating_types(),
dtypesIfCPU=floating_and_complex_types_and(torch.bfloat16),
dtypesIfCUDA=floating_and_complex_types_and(torch.half),
dtypesIfROCM=floating_types_and(torch.half),
domain=(None, None), # the [low, high) domain of the function
handles_large_floats=True, # whether the op correctly handles large float values (like 1e20)
handles_extremals=True, # whether the op correctly handles extremal values (like inf)
handles_complex_extremals=True, # whether the op correct handles complex extremals (like inf -infj)
promotes_integers_to_float=False, # whether op promotes unary output to float or not
**kwargs):
super(UnaryUfuncInfo, self).__init__(name,
dtypes=dtypes,
dtypesIfCPU=dtypesIfCPU,
dtypesIfCUDA=dtypesIfCUDA,
dtypesIfROCM=dtypesIfROCM,
**kwargs)
self.ref = ref
self.domain = domain
self.handles_large_floats = handles_large_floats
self.handles_extremals = handles_extremals
self.handles_complex_extremals = handles_complex_extremals
self.promotes_integers_to_float = promotes_integers_to_float
# Epsilon to ensure grad and gradgrad checks don't test values
# outside a function's domain.
self._domain_eps = 1e-5
def sample_inputs(self, device, dtype, requires_grad=False):
low, high = self.domain
low = low if low is None else low + self._domain_eps
high = high if high is None else high - self._domain_eps
return (SampleInput(make_tensor((L,), device, dtype,
low=low, high=high,
requires_grad=requires_grad)),)
# Operator database (sorted alphabetically)
op_db = [
# NOTE: CPU complex acos produces incorrect outputs (https://github.com/pytorch/pytorch/issues/42952)
UnaryUfuncInfo('acos',
ref=np.arccos,
domain=(-1, 1),
handles_complex_extremals=False,
decorators=(precisionOverride({torch.float16: 1e-2,
torch.bfloat16: 1e-1,
torch.complex64: 1e-2}),),
skips=(
SkipInfo('TestUnaryUfuncs', 'test_reference_numerics',
device_type='cpu', dtypes=[torch.cfloat, torch.cdouble]),
SkipInfo('TestUnaryUfuncs', 'test_reference_numerics',
dtypes=[torch.cfloat, torch.cdouble], active_if=IS_WINDOWS),
SkipInfo('TestUnaryUfuncs', 'test_reference_numerics',
device_type='cuda', dtypes=[torch.float16],
active_if=TEST_WITH_ROCM),
SkipInfo('TestGradients', 'test_fn_grad',
dtypes=[torch.cdouble], active_if=IS_WINDOWS),
SkipInfo('TestGradients', 'test_method_grad',
dtypes=[torch.cdouble], active_if=IS_WINDOWS),
SkipInfo('TestGradients', 'test_inplace_grad',
dtypes=[torch.cdouble], active_if=IS_WINDOWS),
)),
# NOTE: the derivative for inplace acosh is not implemented
UnaryUfuncInfo('acosh',
ref=np.arccosh,
domain=(1, float('inf')),
dtypesIfCPU=floating_types(),
dtypesIfCUDA=floating_types_and(torch.half, torch.bfloat16),
decorators=(precisionOverride({torch.bfloat16: 5e-2}),),
test_inplace_grad=False),
UnaryUfuncInfo('asin',
ref=np.arcsin,
domain=(-1, 1),
decorators=(precisionOverride({torch.bfloat16: 1e-2}),),
skips=(
SkipInfo('TestUnaryUfuncs', 'test_reference_numerics',
device_type='cpu', dtypes=[torch.cfloat, torch.cdouble]),
SkipInfo('TestUnaryUfuncs', 'test_reference_numerics',
device_type='cuda', dtypes=[torch.cfloat, torch.cdouble],
active_if=IS_WINDOWS),
)),
# NOTE: derivative for inplace asinh is not implemented
UnaryUfuncInfo('asinh',
ref=np.arcsinh,
dtypesIfCPU=floating_types(),
dtypesIfCUDA=floating_types_and(torch.half, torch.bfloat16),
decorators=(precisionOverride({torch.bfloat16: 5e-2}),),
test_inplace_grad=False),
UnaryUfuncInfo('atan',
ref=np.arctan,
decorators=(precisionOverride({torch.bfloat16: 1e-2}),),
skips=(
SkipInfo('TestUnaryUfuncs', 'test_reference_numerics',
device_type='cpu', dtypes=[torch.cfloat, torch.cdouble]),
SkipInfo('TestUnaryUfuncs', 'test_reference_numerics',
device_type='cuda', dtypes=[torch.cfloat, torch.cdouble],
active_if=IS_WINDOWS),
)),
UnaryUfuncInfo('atanh',
ref=np.arctanh,
domain=(-1, 1),
dtypesIfCPU=floating_types(),
dtypesIfCUDA=floating_types_and(torch.half, torch.bfloat16),
decorators=(precisionOverride({torch.bfloat16: 1e-2}),),
test_inplace_grad=False),
UnaryUfuncInfo('cos',
ref=np.cos,
dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16),
dtypesIfCPU=all_types_and_complex_and(torch.bool, torch.bfloat16),
dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16),
handles_large_floats=False,
promotes_integers_to_float=True,
decorators=(precisionOverride({torch.bfloat16: 1e-2}),),
skips=(
SkipInfo('TestUnaryUfuncs', 'test_reference_numerics',
dtypes=[torch.cfloat, torch.cdouble], active_if=IS_WINDOWS),
SkipInfo('TestUnaryUfuncs', 'test_reference_numerics', device_type='cpu',
dtypes=[torch.cfloat, torch.cdouble], active_if=IS_MACOS),
SkipInfo('TestUnaryUfuncs', 'test_reference_numerics',
dtypes=[torch.float], active_if=TEST_WITH_ROCM),
)),
UnaryUfuncInfo('cosh',
ref=np.cosh,
dtypesIfCPU=floating_and_complex_types(),
skips=(
SkipInfo('TestUnaryUfuncs', 'test_reference_numerics',
dtypes=[torch.cfloat, torch.cdouble], active_if=IS_WINDOWS),
SkipInfo('TestUnaryUfuncs', 'test_reference_numerics', device_type='cpu',
dtypes=[torch.cfloat, torch.cdouble], active_if=IS_MACOS),
)),
UnaryUfuncInfo('log',
ref=np.log,
domain=(0, float('inf')),
dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16),
dtypesIfCPU=all_types_and_complex_and(torch.bool, torch.bfloat16),
dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16),
promotes_integers_to_float=True,
decorators=(precisionOverride({torch.bfloat16: 5e-2}),),
skips=(
SkipInfo('TestUnaryUfuncs', 'test_reference_numerics',
device_type='cpu', dtypes=[torch.bfloat16]),
SkipInfo('TestUnaryUfuncs', 'test_reference_numerics',
device_type='cuda', dtypes=[torch.cfloat, torch.cdouble]),
SkipInfo('TestUnaryUfuncs', 'test_reference_numerics',
device_type='cpu', dtypes=[torch.cfloat, torch.cdouble],
active_if=IS_WINDOWS),
)),
UnaryUfuncInfo('log10',
ref=np.log10,
domain=(0, float('inf')),
decorators=(precisionOverride({torch.bfloat16: 5e-2}),),
dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16),
dtypesIfCPU=all_types_and_complex_and(torch.bool, torch.bfloat16),
dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16),
promotes_integers_to_float=True,
skips=(
SkipInfo('TestUnaryUfuncs', 'test_reference_numerics',
device_type='cuda', dtypes=[torch.cfloat, torch.cdouble]),
SkipInfo('TestUnaryUfuncs', 'test_reference_numerics',
device_type='cpu', dtypes=[torch.cfloat, torch.cdouble],
active_if=IS_WINDOWS),
)),
UnaryUfuncInfo('log1p',
ref=np.log1p,
domain=(-1, float('inf')),
dtypesIfCPU=floating_types_and(torch.bfloat16),
dtypesIfCUDA=floating_types_and(torch.half, torch.bfloat16),
decorators=(precisionOverride({torch.bfloat16: 1e-1}),)),
UnaryUfuncInfo('log2',
ref=np.log2,
domain=(0, float('inf')),
dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16),
dtypesIfCPU=all_types_and_complex_and(torch.bool, torch.bfloat16),
dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16),
promotes_integers_to_float=True,
decorators=(precisionOverride({torch.bfloat16: 1e-1}),),
skips=(
SkipInfo('TestUnaryUfuncs', 'test_reference_numerics',
device_type='cpu', dtypes=[torch.bfloat16]),
SkipInfo('TestUnaryUfuncs', 'test_reference_numerics',
dtypes=[torch.cfloat, torch.cdouble]),
)),
UnaryUfuncInfo('neg',
ref=np.negative,
dtypes=all_types_and_complex_and(torch.half, torch.bfloat16),
dtypesIfCPU=all_types_and_complex_and(torch.half, torch.bfloat16),
dtypesIfCUDA=all_types_and_complex_and(torch.half, torch.bfloat16)),
UnaryUfuncInfo('sin',
ref=np.sin,
dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16),
dtypesIfCPU=all_types_and_complex_and(torch.bool, torch.bfloat16),
dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.half),
handles_large_floats=False,
handles_complex_extremals=False,
promotes_integers_to_float=True,
decorators=(precisionOverride({torch.bfloat16: 1e-2}),),
skips=(
SkipInfo('TestUnaryUfuncs', 'test_reference_numerics',
dtypes=[torch.cfloat, torch.cdouble], active_if=IS_WINDOWS),
SkipInfo('TestUnaryUfuncs', 'test_reference_numerics',
dtypes=[torch.float], active_if=TEST_WITH_ROCM),
)),
UnaryUfuncInfo('sinh',
ref=np.sinh,
dtypesIfCPU=floating_and_complex_types(),
decorators=(precisionOverride({torch.float16: 1e-2}),),
skips=(
SkipInfo('TestUnaryUfuncs', 'test_reference_numerics',
device_type='cpu', dtypes=[torch.cfloat, torch.cdouble],
active_if=(IS_MACOS or IS_WINDOWS)),
SkipInfo('TestUnaryUfuncs', 'test_reference_numerics',
device_type='cuda', dtypes=[torch.cfloat, torch.cdouble],
active_if=IS_WINDOWS),
)),
UnaryUfuncInfo('tan',
ref=np.tan,
dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16),
dtypesIfCPU=all_types_and_complex_and(torch.bool, torch.bfloat16),
dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.half),
promotes_integers_to_float=True,
skips=(
SkipInfo('TestUnaryUfuncs', 'test_reference_numerics',
device_type='cuda', dtypes=[torch.cfloat, torch.cdouble]),
SkipInfo('TestUnaryUfuncs', 'test_reference_numerics',
device_type='cpu', dtypes=[torch.bfloat16]),
SkipInfo('TestUnaryUfuncs', 'test_reference_numerics',
device_type='cpu', dtypes=[torch.cfloat, torch.cdouble],
active_if=(IS_MACOS or IS_WINDOWS)),
SkipInfo('TestUnaryUfuncs', 'test_reference_numerics',
device_type='cuda', dtypes=[torch.float64],
active_if=TEST_WITH_ROCM),
)),
UnaryUfuncInfo('tanh',
ref=np.tanh,
decorators=(precisionOverride({torch.bfloat16: 1e-2}),),
dtypesIfCUDA=floating_and_complex_types_and(torch.half, torch.bfloat16),
skips=(
SkipInfo('TestUnaryUfuncs', 'test_reference_numerics',
device_type='cuda', dtypes=[torch.cfloat, torch.cdouble]),
SkipInfo('TestUnaryUfuncs', 'test_reference_numerics',
device_type='cpu', dtypes=[torch.cfloat, torch.cdouble],
active_if=(IS_MACOS or IS_WINDOWS)),
)),
UnaryUfuncInfo('exp2',
ref=np.exp2,
dtypes=floating_types_and(torch.half),
dtypesIfCPU=None,
dtypesIfCUDA=None),
UnaryUfuncInfo('nan_to_num',
ref=np.nan_to_num,
dtypes=all_types_and(torch.half, torch.bool),
dtypesIfCPU=None,
dtypesIfCUDA=None),
UnaryUfuncInfo('sqrt',
ref=np.sqrt,
domain=(0, float('inf')),
dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16),
dtypesIfCPU=all_types_and_complex_and(torch.bool, torch.bfloat16),
dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16),
decorators=(precisionOverride({torch.bfloat16: 7e-2}),),
skips=(
# Reference: https://github.com/pytorch/pytorch/issues/47358
SkipInfo('TestUnaryUfuncs', 'test_reference_numerics',
device_type='cpu', dtypes=[torch.cfloat, torch.cdouble]),
# Reference: https://github.com/pytorch/pytorch/pull/47293#issuecomment-721774436
SkipInfo('TestUnaryUfuncs', 'test_reference_numerics',
dtypes=[torch.bfloat16]),
# RuntimeError: sqrt does not support automatic differentiation for outputs with complex dtype.
SkipInfo('TestGradients', 'test_fn_grad',
dtypes=[torch.cdouble]),
SkipInfo('TestGradients', 'test_fn_gradgrad',
dtypes=[torch.cdouble]),
SkipInfo('TestGradients', 'test_method_grad',
dtypes=[torch.cdouble]),
SkipInfo('TestGradients', 'test_method_gradgrad',
dtypes=[torch.cdouble]),
SkipInfo('TestGradients', 'test_inplace_grad',
dtypes=[torch.cdouble]),
SkipInfo('TestGradients', 'test_inplace_gradgrad',
dtypes=[torch.cdouble]),),
promotes_integers_to_float=True,
handles_complex_extremals=False),
]
# Common operator groupings
unary_ufuncs = [op for op in op_db if isinstance(op, UnaryUfuncInfo)]
def index_variable(shape, max_indices):
if not isinstance(shape, tuple):
shape = (shape,)
index = torch.rand(*shape).mul_(max_indices).floor_().long()
return index
def index_perm_variable(shape, max_indices):
if not isinstance(shape, tuple):
shape = (shape,)
index = torch.randperm(max_indices).narrow(0, 0, reduce(mul, shape)).view(shape)
return index
def gather_variable(shape, index_dim, max_indices, duplicate=False):
assert len(shape) == 2
assert index_dim < 2
batch_dim = 1 - index_dim
index = torch.LongTensor(*shape)
for i in range(shape[index_dim]):
index.select(index_dim, i).copy_(
torch.randperm(max_indices)[:shape[batch_dim]])
if duplicate:
index.select(batch_dim, 0).copy_(index.select(batch_dim, 1))
return index
def bernoulli_scalar():
return torch.tensor(0, dtype=torch.bool).bernoulli_()
def mask_not_all_zeros(shape):
assert len(shape) > 0
while True:
result = torch.randn(shape).gt(0)
if result.sum() > 0:
return result
def uniform_scalar(offset=0, requires_grad=False):
v = torch.rand(()) + offset
v.requires_grad = requires_grad
return v
def normal_scalar_clamp(amin, amax, requires_grad=False):
v = torch.randn(()).clamp(amin, amax)
v.requires_grad = requires_grad
return v
def prod_zeros(dim_size, dim_select):
assert len(dim_select) == 2
result = torch.randn(dim_size, dim_size, dim_size)
result.narrow(dim_select[0], 0, 1).narrow(dim_select[1], 1, 1).zero_()
result.narrow(dim_select[0], 2, 1).narrow(dim_select[1], 3, 1).zero_()
result.narrow(dim_select[0], 4, 1).narrow(dim_select[1], 3, 1).zero_()
return result
non_differentiable = collections.namedtuple('non_differentiable', ['tensor'])
class dont_convert(tuple):
pass
class NoArgsClass(object):
def __iter__(self):
return self
def __next__(self):
raise StopIteration()
next = __next__ # Python 2 compatibility
def __len__(self):
return 0
NO_ARGS = NoArgsClass()
def ident(x):
return x
# (
# method name,
# input size/constructing fn,
# args (tuple represents shape of a tensor arg),
# test variant name (will be used at test name suffix), // optional
# (should_check_autodiff[bool], nonfusible_nodes, fusible_nodes) for autodiff, // optional
# indices for possible dim arg, // optional
# fn mapping output to part that should be gradcheck'ed, // optional
# kwargs // optional
# )
# Note: some functions have separate schema for (Tensor other) and (Scalar other),
# and it's possible that we only support AD for Scalar version but not Tensor
# version, and vice versa.
# When writing tests, only scalar(float/int) input triggers the Scalar schema.
# uniform_scalar produces a scalar **Tensor** which won't match Scalar input.
def method_tests():
set_rng_seed(0)
return [
('acosh', torch.rand(S, S, S).add(1), NO_ARGS, ''),
('acosh', torch.rand(tuple()).add(1), NO_ARGS, 'scalar'),
('add', (S, S, S), ((S, S, S),), '', (True,)),
('add', (S, S, S), ((S, S),), 'broadcast_rhs', (True,)),
('add', (S, S), ((S, S, S),), 'broadcast_lhs', (True,)),
('add', (S, 1, S), ((M, S),), 'broadcast_all', (True,)),
('add', (), ((),), 'scalar', (True,)),
('add', (S, S, S), ((),), 'scalar_broadcast_rhs', (True,)),
('add', (), ((S, S, S),), 'scalar_broadcast_lhs', (True,)),
('add', (S, S, S), (3.14,), 'constant', (True,)),
('add', (), (3.14,), 'scalar_constant', (True,)),
('add', (S, S, S), (3.14j,), 'complex_scalar_constant', (True,)),
('asinh', (S, S, S), NO_ARGS, ''),
('asinh', (), NO_ARGS, 'scalar'),
('atanh', torch.rand(S, S, S), NO_ARGS, ''),
('atanh', torch.rand(tuple()), NO_ARGS, 'scalar'),
('__radd__', (S, S, S), (3.14,), 'constant', (True, 'aten::add')),
('__radd__', (), (3.14,), 'scalar_constant', (True, 'aten::add')),
('sub', (S, S, S), ((S, S, S),), '', (True,)),
('sub', (S, S, S), ((S, S),), 'broadcast_rhs', (True,)),
('sub', (S, S), ((S, S, S),), 'broadcast_lhs', (True,)),
('sub', (S, 1, S), ((M, S),), 'broadcast_all', (True,)),
('sub', (S, S, S), ((),), 'scalar_broadcast_rhs', (True,)),
('sub', (), ((S, S, S),), 'scalar_broadcast_lhs', (True,)),
('sub', (S, S, S), (3.14,), 'constant', (True,)),
('sub', (), (3.14,), 'scalar_constant', (True,)),
('sub', (S, S, S), (3.14j,), 'complex_scalar_constant', (True,)),
('__rsub__', (S, S, S), (3.14,), 'constant', (True, 'aten::rsub')),
('__rsub__', (), (3.14,), 'scalar_constant', (True, 'aten::rsub')),
('mul', (S, S, S), ((S, S, S),), '', (True,)),
('mul', (), ((),), 'scalar', (True,)),
('mul', (S, S, S), ((S, S),), 'broadcast_rhs', (True,)),
('mul', (S, S), ((S, S, S),), 'broadcast_lhs', (True,)),
('mul', (S, 1, S), ((M, S),), 'broadcast_all', (True,)),
('mul', (S, S, S), ((),), 'scalar_broadcast_rhs', (True,)),
('mul', (), ((S, S, S),), 'scalar_broadcast_lhs', (True,)),
('mul', (S, S, S), (3.14,), 'constant', (True,)),
('mul', (), (3.14,), 'scalar_constant', (True,)),
# TODO(@anjali411): enable these tests
# ('mul', (S, S, S), (3.14j,), 'imaginary_constant', (True,)),
# ('mul', (), (3.14j,), 'imaginary_scalar_constant', (True,)),
('__rmul__', (S, S, S), (3.14,), 'constant', (True, 'aten::mul')),
('__rmul__', (), (3.14,), 'scalar_constant', (True, 'aten::mul')),
('div', (S, S, S), (torch.rand(S, S, S) + 0.1,), '', (True,)),
('div', (S, S, S), (torch.rand(S, S) + 0.1,), 'broadcast_rhs', (True,)),
('div', (S, S), (torch.rand(S, S, S) + 0.1,), 'broadcast_lhs', (True,)),
('div', (S, 1, S), (torch.rand(M, S) + 0.1,), 'broadcast_all', (True,)),
('div', (), (uniform_scalar(0.1),), 'scalar', (True,)),
('div', (S, S, S), (uniform_scalar(0.1),), 'scalar_broadcast_rhs', (True,)),
('div', (), (uniform_scalar(0.1),), 'scalar_broadcast_lhs', (True,)),
('div', torch.rand(S, S, S) + 1e-1, (3.14,), 'constant', (True,)),
('div', uniform_scalar(1e-1, requires_grad=True), (3.14,), 'scalar_constant', (True,)),
('true_divide', (S, S, S), (torch.rand(S, S, S) + 0.1,), '', (True,)),
('true_divide', (S, S, S), (torch.rand(S, S) + 0.1,), 'broadcast_rhs', (True,)),
('true_divide', (S, S), (torch.rand(S, S, S) + 0.1,), 'broadcast_lhs', (True,)),
('true_divide', (S, 1, S), (torch.rand(M, S) + 0.1,), 'broadcast_all', (True,)),
('true_divide', (), (uniform_scalar(0.1),), 'scalar', (True,)),
('true_divide', (S, S, S), (uniform_scalar(0.1),), 'scalar_broadcast_rhs', (True,)),
('true_divide', (), (uniform_scalar(0.1),), 'scalar_broadcast_lhs', (True,)),
('true_divide', torch.rand(S, S, S) + 1e-1, (3.14,), 'constant', (True,)),
('true_divide', uniform_scalar(1e-1, requires_grad=True), (3.14,), 'scalar_constant', (True,)),
('__rdiv__', torch.rand(S, S, S) + 1e-1, (3.14,), 'constant',
(True, [], ['aten::mul', 'aten::reciprocal'])),
('__rdiv__', uniform_scalar(1e-1, requires_grad=True), (3.14,), 'scalar_constant',
(True, [], ['aten::mul', 'aten::reciprocal'])),
('__rdiv__', torch.rand(S, S, S, dtype=torch.cdouble) + 1e-1, (3.14j,), 'complex_constant',
(True, [], ['aten::mul', 'aten::reciprocal'])),
('__rdiv__', uniform_scalar(1e-1 * (1 + 1j), requires_grad=True), (3.14j,), 'complex_scalar_constant',
(True, [], ['aten::mul', 'aten::reciprocal'])),
('div', (S, S, S), (torch.rand(S, S, S, dtype=torch.cdouble) + 0.1,), 'complex', (True,)),
('div', (S, S, S), (torch.rand(S, S, dtype=torch.cdouble) + 0.1,), 'complex_broadcast_rhs', (True,)),
('div', (S, S), (torch.rand(S, S, S, dtype=torch.cdouble) + 0.1,), 'complex_broadcast_lhs', (True,)),
('div', (S, 1, S), (torch.rand(M, S, dtype=torch.cdouble) + 0.1,), 'complex_broadcast_all', (True,)),
('div', (), (uniform_scalar(0.1j),), 'complex_scalar', (True,)),
('div', (S, S, S), (uniform_scalar(0.1j),), 'complex_scalar_broadcast_rhs', (True,)),
('div', (), (uniform_scalar(0.1j),), 'complex_scalar_broadcast_lhs', (True,)),
('div', torch.rand(S, S, S, dtype=torch.cdouble) + 1e-1, (3.14j,), 'complex_constant', (True,)),
('div', uniform_scalar(1e-1j, requires_grad=True), (3.14j,), 'complex_scalar_constant', (True,)),
('pow', torch.rand(S, S, S) + 1e-3, (torch.rand(S, S, S) + 0.1,), '', (True,)),
('pow', torch.rand(S, S, S) + 1e-3, (torch.rand(1,) + 0.1,), 'broadcast_rhs', (True,)),
('pow', torch.rand(1,) + 1e-3, (torch.rand(S, S, S) + 0.1,), 'broadcast_lhs', (True,)),
('pow', torch.rand(S, 1, S) + 1e-3, (torch.rand(1, S, 1) + 0.1,), 'broadcast_all', (True,)),
('pow', uniform_scalar(1e-3, requires_grad=True), (uniform_scalar(0.1),), 'scalar', (True,)),
('pow', torch.rand(S, S, S) + 1e-3, (uniform_scalar(0.1),), 'scalar_broadcast_rhs', (True,)),
('pow', uniform_scalar(1e-3, requires_grad=True), (torch.rand(S, S, S) + 0.1,), 'scalar_broadcast_lhs', (True,)),
('pow', torch.rand(S, S, S) + 1e-3, (3.14,), 'constant', (True,)),
('pow', torch.rand(S, S, S, dtype=torch.cdouble) + 1e-3 * (1 + 1j), (3.14,), 'complex_constant', (True,)),
('__rpow__', torch.rand(S, S, S) + 1e-3, (3.14,), 'constant', (True, 'aten::pow')),
('pow', uniform_scalar(1e-3, requires_grad=True), (3.14,), 'scalar_constant', (True,)),
('pow', uniform_scalar(1e-3 * (1 + 1j), requires_grad=True), (3.14,), 'complex_scalar_constant', (True,)),
('pow', uniform_scalar(1e-3 * (1 + 1j), requires_grad=True), (3.14j,), 'complex_imaginary_exponent', (True,)),
('__rpow__', uniform_scalar(1e-3, requires_grad=True), (3.14,), 'scalar_constant', (True, 'aten::pow')),
('transpose', (1, 2, 3), (1, 2), 'dim', (False,), [0, 1]),
('transpose', (), (0, 0), 'scalar', (False,)),
('transpose', (1,), (0, 0), '1d', (False,)),
('transpose', (L, L), (0, 1), '2d', (False,)),
('transpose', (S, S, S), (2, 0), '3d', (False,)),
('t', (1, 2), NO_ARGS, '', (False,)),
('view', (S, S, S), (S * S, S), '', (False,)),
('view', (torch.Size([S * S, S]),), (S, S, S), 'size', (False,)),
('view', (S,), (S,), '1d', (False,)),
('view', (), (dont_convert(()),), 'scalar_to_scalar', (False,)),
('view', (), (1,), 'scalar_to_1d', (False,)),
('ravel', (S, S, S), NO_ARGS, '', (False,)),
('reshape', (S, S, S), (S * S, S), '', (False,)),
('reshape', (torch.Size([S * S, S]),), (S, S, S), 'size', (False,)),
('reshape', (S,), (S,), '1d', (False,)),
('reshape', (), (dont_convert(()),), 'scalar_to_scalar', (False,)),
('reshape', (), (1,), 'scalar_to_1d', (False,)),
('reshape_as', (S, S, S), (non_differentiable(torch.rand(S * S, S)),)),
('reshape_as', (), (non_differentiable(torch.tensor(42.)),), 'scalar'),
('reshape_as', (), (non_differentiable(torch.rand(1, 1)),), 'scalar_to_dims'),
('flip', (S, S, S), ([0],), 'd0'),
('flip', (S, S, S), ([0, 1, 2],), 'd012'),
('flip', (S, S, S), ([0, 2],), 'd02'),
('flip', (S, S, S), ([2, 0],), 'd20'),
('flip', (S, S, S), ([-1],), 'neg_d'),
('fliplr', (S, S, S), ()),
('flipud', (S, S, S), ()),
('roll', (S, S, S), (0, 0), 'd0'),
('roll', (S, S, S), (1, 2), 'd12'),
('roll', (S, S, S), (0, 2,), 'd02'),
('roll', (S, S, S), (2, 0,), 'd20'),
('roll', (S, S, S), (-1, 0), 'neg_shift'),
('roll', (S, S, S), (10000, 1), 'loop_shift'),
('roll', (S, S, S), (2,), 'flattened'),
('roll', (S, S, S), ([1, 2, -1], [0, 1, 2]), 'three_dims'),
('rot90', (S, S, S), (1, [0, 1],), 'k1_d01'),
('rot90', (S, S, S), (1, [1, 2],), 'k1_d12'),
('rot90', (S, S, S), (1, [1, -1],), 'k1_neg_d'),
('rot90', (S, S, S), (), 'default'),
('view_as', (S, S, S), (non_differentiable(torch.rand(S * S, S)),)),
('view_as', (), (non_differentiable(torch.tensor(5.5)),), 'scalar'),
('view_as', (), (non_differentiable(torch.rand(1, 1)),), 'scalar_to_dims'),
('expand', (S, 1, 1), (S, S, S), '', (False,)),
('expand', (torch.Size([S, 1, S]),), (S, S, S), 'size', (False,)),
('expand', (S, 1), (S, S, S), 'new_dim', (False,)),
('expand', (1,), (S, S, S), '1_element', (False,)),
('expand', (1, S), (1, 1, S), 'new_dim_front_old_front_1', (False,)),
('expand', (), (dont_convert(()),), 'scalar_to_scalar'),
('expand', (), (1, 3, 2), 'scalar_to_dims', (False,)),
('expand_as', (S, 1, 1), (torch.rand(S, S, S),), '', (False,)),
('exp', (S, S, S), NO_ARGS, '', (True,)),
('exp', (), NO_ARGS, 'scalar', (True,)),
('exp2', (S, S, S), NO_ARGS, '', (False,)),
('exp2', (), NO_ARGS, 'scalar', (False,)),
('expm1', (S, S, S), NO_ARGS, '', (True,)),
('expm1', (), NO_ARGS, 'scalar', (True,)),
('erf', torch.rand(S, S, S), NO_ARGS, '', (True,)),
('erf', uniform_scalar(requires_grad=True), NO_ARGS, 'scalar', (True,)),
('erfc', torch.rand(S, S, S), NO_ARGS, '', (True,)),
('erfc', uniform_scalar(requires_grad=True), NO_ARGS, 'scalar', (True,)),
('erfinv', torch.rand(S, S, S).clamp(-0.9, 0.9), NO_ARGS),
('erfinv', normal_scalar_clamp(-0.9, 0.9, requires_grad=True), NO_ARGS, 'scalar'),
('log', torch.rand(S, S, S) + 1e-2, NO_ARGS, '', (True,)),
('log', uniform_scalar(1e-2, requires_grad=True), NO_ARGS, 'scalar', (True,)),
('log10', torch.rand(S, S, S) + 1e-2, NO_ARGS, '', (True,)),
('log10', uniform_scalar(1e-2, requires_grad=True), NO_ARGS, 'scalar', (True,)),
('log1p', torch.rand(S, S, S), NO_ARGS, '', (True,)),
('log1p', uniform_scalar(requires_grad=True), NO_ARGS, 'scalar', (True,)),
('log2', torch.rand(S, S, S) + 1e-2, NO_ARGS, '', (True,)),
('log2', uniform_scalar(1e-2, requires_grad=True), NO_ARGS, 'scalar', (True,)),
('log', torch.randn(S, S, S, dtype=torch.cdouble) + 1e-2, NO_ARGS, 'complex', (True,)),
('log', uniform_scalar(1e-2j, requires_grad=True), NO_ARGS, 'complex_scalar', (True,)),
('log10', torch.randn(S, S, S, dtype=torch.cdouble) + 1e-2, NO_ARGS, 'complex', (True,)),
('log10', uniform_scalar(1e-2j, requires_grad=True), NO_ARGS, 'complex_scalar', (True,)),
('log2', torch.randn(S, S, S, dtype=torch.cdouble) + 1e-2, NO_ARGS, 'complex', (True,)),
('log2', uniform_scalar(1e-2j, requires_grad=True), NO_ARGS, 'complex_scalar', (True,)),
('tanh', (S, S, S), NO_ARGS, '', (True,)),
('tanh', (), NO_ARGS, 'scalar', (True,)),
('sigmoid', (S, S, S), NO_ARGS, '', (True,)),
('sigmoid', (), NO_ARGS, 'scalar', (True,)),
('logit', torch.randn(S, S, S).clamp(0.1, 0.9).requires_grad_(True), NO_ARGS, ''),
('logit', torch.randn(S, S, S).clamp(0.1, 0.9).requires_grad_(True), (0.2,), 'eps'),
('logit', uniform_scalar().clamp(0.1, 0.9).requires_grad_(True), NO_ARGS, 'scalar'),
('logit', uniform_scalar().clamp(0.1, 0.9).requires_grad_(True), (0.2,), 'scalar_eps'),
('sinh', (S, S, S), NO_ARGS, '', (True,)),
('sinh', (), NO_ARGS, 'scalar', (True,)),
('cosh', (S, S, S), NO_ARGS, '', (True,)),
('cosh', (), NO_ARGS, 'scalar', (True,)),
('conj', (S, S, S), NO_ARGS),
('real', (S, S, S), NO_ARGS, 'complex'),
('imag', (S, S, S), NO_ARGS, 'complex'),
('view_as_real', (S, S, S), NO_ARGS, 'complex'),
('view_as_complex', (S, S, 2), NO_ARGS),
('complex', (S, S, S), ((S, S, S),), ''),
('abs', (S, S, S), NO_ARGS, '', (True,)),
('abs', (), NO_ARGS, 'scalar', (True,)),
('angle', (S, S, S), NO_ARGS, '', (True,)),
('angle', (), NO_ARGS, 'scalar', (True,)),
('clamp', (S, S, S), (0, 1), '', (True,)),
('clamp', (S, S, S), (None, 0.5), 'min', (True,)),
('clamp', (S, S, S), (0.5, None), 'max', (True,)),
('clamp', (), (0, 1), 'scalar', (True,)),
('clamp', (), (None, 0.5), 'min_scalar', (True,)),
('clamp', (), (0.5, None), 'max_scalar', (True,)),
('clamp', (S, S), (), 'max_scalar_kwarg', (True,), (), (), ident, {'max': 1}),
('sqrt', torch.rand(S, S, S) + 5e-4, NO_ARGS, '', (True,)),
('sqrt', uniform_scalar(5e-4, requires_grad=True), NO_ARGS, 'scalar', (True,)),
('sin', (S, S, S), NO_ARGS, '', (True,)),
('sin', (), NO_ARGS, 'scalar', (True,)),
('cos', (S, S, S), NO_ARGS, '', (True,)),
('cos', (), NO_ARGS, 'scalar', (True,)),
('tan', torch.randn(S, S, S).clamp(-1, 1), NO_ARGS, '', (True,)),
('tan', (S, S, S), NO_ARGS, 'complex', (True,)),
('asin', torch.randn(S, S, S).clamp(-0.9, 0.9), NO_ARGS, '', (True,)),
('acos', torch.randn(S, S, S).clamp(-0.9, 0.9), NO_ARGS, '', (True,)),
('atan', (S, S, S), NO_ARGS, '', (True,)),
('atan', (), NO_ARGS, 'scalar', (True,)),
('atan2', (S, S, S), ((S, S, S),)),
('atan2', (), ((),), 'scalar'),
('atan2', (S, S, S), ((S,),), 'broadcast_rhs'),
('atan2', (S,), ((S, S, S),), 'broadcast_lhs'),
('atan2', (S, 1, S), ((S, S),), 'broadcast_all'),
('reciprocal', torch.rand(S, S, S) + 0.1, NO_ARGS, '', (True,)),
('reciprocal', uniform_scalar(0.1, requires_grad=True), NO_ARGS, 'scalar', (True,)),
('reciprocal', torch.randn(S, S, S, dtype=torch.cdouble) + 0.1, NO_ARGS, 'complex', (True,)),
('reciprocal', uniform_scalar(0.1j), NO_ARGS, 'complex_scalar', (True,)),
('round', (S, S, S), NO_ARGS, '', (True,)),
('round', (), NO_ARGS, 'scalar', (True,)),
('sign', (S, S, S), NO_ARGS),
('sign', (), NO_ARGS, 'scalar'),
('sgn', (S, S, S), NO_ARGS),
('sgn', (), NO_ARGS, 'scalar'),
('trunc', (S, S, S), NO_ARGS, '', (True,)),
('trunc', (), NO_ARGS, 'scalar', (True,)),
('floor', (S, S, S), NO_ARGS, '', (True,)),
('floor', (), NO_ARGS, 'scalar', (True,)),
('ceil', (S, S, S), NO_ARGS, '', (True,)),
('ceil', (), NO_ARGS, 'scalar', (True,)),
('rad2deg', (S, S, S), NO_ARGS),
('deg2rad', (S, S, S), NO_ARGS),
('rsqrt', torch.rand(S, S, S) + 1e-2, NO_ARGS, '', (True,)),
('rsqrt', uniform_scalar(1e-2, requires_grad=True), NO_ARGS, 'scalar', (True,)),
('rsqrt', torch.rand(S, S, S, dtype=torch.cfloat) + 1e-2, NO_ARGS, 'complex', (True,)),
('rsqrt', uniform_scalar(1e-2 * (1 + 1j), requires_grad=True), NO_ARGS, 'complex_scalar', (True,)),
('frac', (S, S, S), NO_ARGS, '', (True,)),
('frac', (), NO_ARGS, 'scalar', (True,)),
('fmod', (S, S, S), (1.5,), '', (True,)),
('fmod', (), (1.5,), 'scalar', (True,)),
('fmod', (S, S, S), (non_differentiable(torch.rand(S, S, S) + 1.5),), 'tensor'),
('fmod', (S,), (non_differentiable(torch.rand(S, S, S) + 1.5),), 'tensor_broadcast_lhs'),
('fmod', (S, S, S), (non_differentiable(torch.rand(S) + 1.5),), 'tensor_broadcast_rhs'),
('fmod', (S, 1, S), (non_differentiable(torch.rand(S, S) + 1.5),), 'tensor_broadcast_all'),
('fmod', (), (non_differentiable(uniform_scalar(1.5)),), 'scalar_tensor'),
('fmod', (), (non_differentiable(torch.rand(S, S, S) + 1.5),), 'scalar_tensor_broadcast_lhs'),
('fmod', (S, S, S), (non_differentiable(uniform_scalar(1.5)),), 'scalar_tensor_broadcast_rhs'),
('hypot', (S, S), ((S, S),)),
('remainder', (S, S, S), (1.5,), '', (True,)),
('remainder', (), (1.5,), 'scalar', (True,)),
('remainder', (S, S, S), (non_differentiable(torch.rand(S, S, S) + 1.5),), 'tensor'),
('remainder', (S,), (non_differentiable(torch.rand(S, S, S) + 1.5),), 'tensor_broadcast_lhs'),
('remainder', (S, 1, S), (non_differentiable(torch.rand(S, S) + 1.5),), 'tensor_broadcast_all'),
('remainder', (), (non_differentiable(uniform_scalar(1.5)),), 'scalar_tensor'),
('remainder', (), (non_differentiable(torch.rand(S, S, S) + 1.5),), 'scalar_tensor_broadcast_lhs'),
('lerp', (S, S, S), ((S, S, S), 0.4), 'scalar_no_broadcast', (True,)),
('lerp', (S, S, S), ((S,), 0.4), 'broadcast_rhs', (True,)),
('lerp', (S,), ((S, S, S), 0.4), 'broadcast_lhs', (True,)),
('lerp', (S, 1, S), ((S, S), 0.4), 'broadcast_all', (True,)),
('lerp', (), ((), 0.4), 'scalar', (True,)),
('lerp', (S, S, S), ((), 0.4), 'scalar_broadcast_rhs', (True,)),
('lerp', (), ((S, S, S), 0.4), 'scalar_broadcast_lhs', (True,)),
('max', (S, S, S), NO_ARGS),
('max', (S, S, S), (1,), 'dim', (), [0]),
('max', (S, S, S), (1, True,), 'keepdim_dim', (), [0]),
('max', (), NO_ARGS, 'scalar'),
('max', (), (0,), 'scalar_dim', (), [0]),
('max', (), (0, True,), 'scalar_keepdim_dim', (), [0]),
('max', (S, S, S), ((S, S, S),), 'elementwise', (True,)),
('max', (S, S, S), ((S,),), 'elementwise_broadcast_rhs', (True,)),
('max', (S,), ((S, S, S),), 'elementwise_broadcast_lhs', (True,)),
('max', (S, 1, S), ((S, S),), 'elementwise_broadcast_all', (True,)),
('max', (), ((),), 'scalar_elementwise', (True,)),
('max', (S, S, S), ((),), 'scalar_elementwise_broadcast_rhs', (True,)),
('max', (), ((S, S, S),), 'scalar_elementwise_broadcast_lhs', (True,)),
('min', (S, S, S), NO_ARGS, ),
('min', (S, S, S), (1,), 'dim', (), [0]),
('min', (S, S, S), (1, True,), 'keepdim_dim', (), [0]),
('min', (), NO_ARGS, 'scalar'),
('min', (), (0,), 'scalar_dim', (), [0]),
('min', (), (0, True,), 'scalar_keepdim_dim', (), [0]),
('min', (S, S, S), ((S, S, S),), 'elementwise', (True,)),
('min', (S, S, S), ((S,),), 'elementwise_broadcast_rhs', (True,)),
('min', (S,), ((S, S, S),), 'elementwise_broadcast_lhs', (True,)),
('min', (S, 1, S), ((S, S),), 'elementwise_broadcast_all', (True,)),
('min', (), ((),), 'scalar_elementwise', (True,)),
('min', (S, S, S), ((),), 'scalar_elementwise_broadcast_rhs', (True,)),
('min', (), ((S, S, S),), 'scalar_elementwise_broadcast_lhs', (True,)),
('amax', (S, S, S), NO_ARGS),
('amax', (S, S, S), (1,), 'dim'),
('amax', (S, S, S), ([1, 2],), 'multiple_dim'),
('amax', (S, S, S), (1, True,), 'keepdim_dim'),
('amax', (), NO_ARGS, 'scalar'),
('amax', (), (0,), 'scalar_dim'),
('amax', (), (0, True,), 'scalar_keepdim_dim'),
('amin', (S, S, S), NO_ARGS, ),
('amin', (S, S, S), (1,), 'dim',),
('amin', (S, S, S), ([1, 2],), 'multiple_dim'),
('amin', (S, S, S), (1, True,), 'keepdim_dim'),
('amin', (), NO_ARGS, 'scalar'),
('amin', (), (0,), 'scalar_dim'),
('amin', (), (0, True,), 'scalar_keepdim_dim'),
('mean', (S, S, S), NO_ARGS, '', (True,)),
('mean', (S, S, S), (1,), 'dim', (True,), [0]),
('mean', (S, S, S), (1, True,), 'keepdim_dim', (True,), [0]),
('mean', (), NO_ARGS, 'scalar', (True,)),
('mean', (), (0,), 'scalar_dim', (True,), [0]),
('mean', (), (0, True,), 'scalar_keepdim_dim', (True,), [0]),
('mean', (S, S, S), (), 'dtype', (True,), (), (), ident, {'dtype': torch.float64}),
('kthvalue', (S, S, S), (2,)),
('kthvalue', (S, S, S), (2, 1,), 'dim', (), [1]),
('kthvalue', (S, S, S), (2, 1, True,), 'keepdim_dim', (), [1]),
('kthvalue', (S,), (2, 0,), 'dim_1d', (), [1]),
('kthvalue', (S,), (2, 0, True,), 'keepdim_dim_1d', (), [1]),
# TODO: https://github.com/pytorch/pytorch/issues/30818
('kthvalue', (), (1,), 'scalar', (), (), [expectedFailureCUDA]),
('kthvalue', (), (1, 0,), 'scalar_dim', (), [1], [expectedFailureCUDA]),
('kthvalue', (), (1, 0, True), 'scalar_keepdim_dim', (), [1], [expectedFailureCUDA]),
# END TODO
('quantile', (S, S, S), (0.5,)),
('quantile', (S, S, S), (0.5, 0), 'dim', (), [1]),
('quantile', (S, S, S), (0.5, None, True), 'keepdim'),
('quantile', (S, S, S), (0.5, 0, True), 'keepdim_dim', (), [1]),
('quantile', (), (0.5,), 'scalar'),
('nanquantile', (S, S, S), (0.5,)),
('nanquantile', (S, S, S), (0.5, 0), 'dim', (), [1]),
('nanquantile', (S, S, S), (0.5, None, True), 'keepdim'),
('nanquantile', (S, S, S), (0.5, 0, True), 'keepdim_dim', (), [1]),
('nanquantile', (), (0.5,), 'scalar'),
('median', (S, S, S), NO_ARGS),
('median', (S, S, S), (1,), 'dim', (), [0]),
('median', (S, S, S), (1, True,), 'keepdim_dim', (), [0]),
('median', (), NO_ARGS, 'scalar'),
('median', (), (0,), 'scalar_dim', (), [0]),
('median', (), (0, True,), 'scalar_keepdim_dim', (), [0]),
('nanmedian', (S, S, S), NO_ARGS),
('nanmedian', (S, S, S), (1,), 'dim', (), [0]),
('nanmedian', (S, S, S), (1, True,), 'keepdim_dim', (), [0]),
('nanmedian', (), NO_ARGS, 'scalar'),
('nanmedian', (), (0,), 'scalar_dim', (), [0]),
('nanmedian', (), (0, True,), 'scalar_keepdim_dim', (), [0]),
('mode', (S, S, S), NO_ARGS),
('mode', (S, S, S), (1,), 'dim', (), [0]),
('mode', (S, S, S), (1, True,), 'keepdim_dim', (), [0]),
('mode', (), NO_ARGS, 'scalar'),
('mode', (), (0,), 'scalar_dim', (), [0]),
('mode', (), (0, True,), 'scalar_keepdim_dim', (), [0]),
('sum', (S, S, S), NO_ARGS),
('sum', (S, S, S), (1,), 'dim', (), [0]),
('sum', (S, S, S), (1, True,), 'keepdim_dim', (), [0]),
('sum', (), NO_ARGS, 'scalar'),
('sum', (), (0,), 'scalar_dim', (), [0]),
('sum', (), (0, True,), 'scalar_keepdim_dim', (), [0]),
('sum', (S, S, S), ([1, 2],), 'multi_dim'),
('sum', (S, S, S), ([1, 2], True,), 'multi_dim_keepdim'),
('nansum', (S, S, S), NO_ARGS),
('nansum', (S, S, S), (1,), 'dim', (), [0]),
('nansum', (S, S, S), (1, True,), 'keepdim_dim', (), [0]),
('nansum', (), NO_ARGS, 'scalar'),
('nansum', (), (0,), 'scalar_dim', (), [0]),
('nansum', (), (0, True,), 'scalar_keepdim_dim', (), [0]),
('nansum', (S, S, S), ([1, 2],), 'multi_dim'),
('nansum', (S, S, S), ([1, 2], True,), 'multi_dim_keepdim'),
('prod', (S, S, S), NO_ARGS),
('prod', (S, S, S), (1,), 'dim', (), [0]),
('prod', (S, S, S), (1, True,), 'keepdim_dim', (), [0]),
('prod', (), NO_ARGS, 'scalar'),
('prod', (), (0,), 'scalar_dim', (), [0]),
('prod', (), (0, True,), 'scalar_keepdim_dim', (), [0]),
('prod', prod_zeros(S, [0, 1]), NO_ARGS, 'zerodims2'),
('prod', prod_zeros(S, [0, 2]), NO_ARGS, 'zerodims1'),
('prod', prod_zeros(S, [1, 2]), NO_ARGS, 'zerodims0'),
('prod', prod_zeros(S, [0, 1]), (1,), 'zeros_dims2', (), [0]),
('prod', prod_zeros(S, [0, 2]), (1,), 'zeros_dims1', (), [0]),
('prod', prod_zeros(S, [1, 2]), (1,), 'zeros_dims0', (), [0]),
('prod', prod_zeros(S, [0, 1]), (1, True), 'keepdim_zeros_dims2', (), [0]),
('prod', prod_zeros(S, [0, 2]), (1, True), 'keepdim_zeros_dims1', (), [0]),
('prod', prod_zeros(S, [1, 2]), (1, True), 'keepdim_zeros_dims0', (), [0]),
('prod', prod_single_zero(S), NO_ARGS, 'single_zero'),
('prod', (torch.tensor(0., requires_grad=True)), NO_ARGS, 'scalar_zero'),
('prod', (torch.tensor(0., requires_grad=True)), (0,), 'scalar_dim_zero', (), [0]),
('prod', (torch.tensor(0., requires_grad=True)), (0, True,), 'scalar_keepdim_dim_zero', (), [0]),
('var', (S, S, S), NO_ARGS, '', (True,)),
('var', (S, S, S), (1,), 'dim', (True,), [0]),
('var', (S, S, S), (1, True, True), 'keepdim_dim', (True,), [0]),
('var', (S,), (0,), 'dim_1d', (True,), [0]),
('var', (S,), (0, True, True), 'keepdim_dim_1d', (True,), [0]),
('std', (S, S, S), NO_ARGS, '', (True,)),
('std', (S, S, S), (1,), 'dim', (True,), [0]),
('std', (S, S, S), (1, True, True), 'keepdim_dim', (True,), [0]),
('std', (S,), (0,), 'dim_1d', (True,), [0]),
('std', (S,), (0, True, True), 'keepdim_dim_1d', (True,), [0]),
('var_mean', (S, S, S), NO_ARGS, ''),
('var_mean', (S, S, S), (1,), 'dim', [0]),
('var_mean', (S, S, S), (1, True, True), 'keepdim_dim', [0]),
('var_mean', (S,), (0,), 'dim_1d', [0]),
('var_mean', (S,), (0, True, True), 'keepdim_dim_1d', [0]),
('std_mean', (S, S, S), NO_ARGS, ''),
('std_mean', (S, S, S), (1,), 'dim', [0]),
('std_mean', (S, S, S), (1, True, True), 'keepdim_dim', [0]),
('std_mean', (S,), (0,), 'dim_1d', [0]),
('std_mean', (S,), (0, True, True), 'keepdim_dim_1d', [0]),
('renorm', (S, S, S), (2, 1, 0.5), 'dim', (), [1]),
('renorm', (S, S, S), (1, 2, 3), 'norm_1'),
('renorm', (S, S, S), (inf, 2, 0.5), 'norm_inf'),
('repeat', (S,), (2,), 'single_number'),
('repeat', (), (2, 3), 'scalar'),
('repeat', (2, 2), (3, 2)),
('repeat', (2, 2), (1, 3, 1, 2), 'unsqueeze'),
('repeat', (S,), (0, ), 'zero_dim'),
('repeat', (S,), (0, 2), 'zero_dim_multi'),
('logcumsumexp', (S, S, S), (0,), 'dim0', (), [0]),
('logcumsumexp', (S, S, S), (1,), 'dim1', (), [0]),
('logcumsumexp', (), (0,), 'dim0_scalar', (), [0]),
('cummax', (S, S, S), (0,), 'dim0', (), [0]),
('cummax', (S, S, S), (1,), 'dim1', (), [0]),
('cummax', (), (0,), 'dim0_scalar', (), [0]),
('cummin', (S, S, S), (0,), 'dim0', (), [0]),
('cummin', (S, S, S), (1,), 'dim1', (), [0]),
('cummin', (), (0,), 'dim0_scalar', (), [0]),
('cumsum', (S, S, S), (0,), 'dim0', (), [0]),
('cumsum', (S, S, S), (1,), 'dim1', (), [0]),
('cumsum', (S, S, S), (1,), 'dim1_cast', (), [0], (), ident, {'dtype': torch.float64}),
('cumsum', (), (0,), 'dim0_scalar', (), [0]),
('cumprod', (S, S, S), (0,)),
('cumprod', (S, S, S), (1,), 'dim1', (), [0]),
('cumprod', (), (0,), 'scalar'),
('cumprod', (torch.tensor(0., requires_grad=True)), (0,), 'scalar_zeros'),
('cumprod', prod_zeros(S, [0, 1]), (1,), 'zeros_dim2', (), [0]),
('cumprod', prod_zeros(S, [0, 2]), (1,), 'zeros_dim1', (), [0]),
('cumprod', prod_zeros(S, [1, 2]), (1,), 'zeros_dim0', (), [0]),
('cumprod', prod_zeros(S, [1, 2]), (1,), 'zeros_dim0_cast', (), [0], (), ident, {'dtype': torch.float64}),
('log_softmax', (S, S, S), (1, torch.float64,), 'kwarg_dtype_would_break_jit_loader', (True,)),
('unfold', (), (0, 1, 1), 'scalar', (), [0]),
('unfold', (S, S, S, S), (0, 3, 1), '4d_dim0_step1', (), [0]),
('unfold', (S, S, S, S), (1, 3, 1), '4d_dim1_step1', (), [0]),
('unfold', (S, S, S, S), (2, 3, 1), '4d_dim2_step1', (), [0]),
('unfold', (S, S, S, S), (3, 3, 1), '4d_dim3_step1', (), [0]),
('unfold', (S, S, S, S), (0, 3, 2), '4d_dim0_step2', (), [0]),
('unfold', (S, S, S, S), (1, 3, 2), '4d_dim1_step2', (), [0]),
('unfold', (S, S, S, S), (2, 3, 2), '4d_dim2_step2', (), [0]),
('unfold', (S, S, S, S), (3, 3, 2), '4d_dim3_step2', (), [0]),