forked from jax-ml/jax
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathlax_numpy_test.py
6674 lines (6047 loc) · 296 KB
/
lax_numpy_test.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
# Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import collections
import copy
import functools
from functools import partial
import inspect
import io
import itertools
import operator
from typing import cast, Iterator, Optional, List, Tuple
import unittest
from unittest import SkipTest
import warnings
from absl.testing import absltest
from absl.testing import parameterized
import numpy as np
try:
import numpy_dispatch
except ImportError:
numpy_dispatch = None
import jax
import jax.ops
from jax import lax
from jax import numpy as jnp
from jax import tree_util
from jax.test_util import check_grads
from jax._src import device_array
from jax._src import dtypes
from jax._src import test_util as jtu
from jax._src.lax import lax as lax_internal
from jax._src.numpy.lax_numpy import _promote_dtypes, _promote_dtypes_inexact
from jax._src.numpy.util import _parse_numpydoc, ParsedDoc, _wraps
from jax._src.util import prod, safe_zip
from jax.config import config
config.parse_flags_with_absl()
FLAGS = config.FLAGS
numpy_version = tuple(map(int, np.__version__.split('.')[:3]))
nonempty_nonscalar_array_shapes = [(4,), (3, 4), (3, 1), (1, 4), (2, 1, 4), (2, 3, 4)]
nonempty_array_shapes = [()] + nonempty_nonscalar_array_shapes
one_dim_array_shapes = [(1,), (6,), (12,)]
empty_array_shapes = [(0,), (0, 4), (3, 0),]
scalar_shapes = [jtu.NUMPY_SCALAR_SHAPE, jtu.PYTHON_SCALAR_SHAPE]
array_shapes = nonempty_array_shapes + empty_array_shapes
nonzerodim_shapes = nonempty_nonscalar_array_shapes + empty_array_shapes
nonempty_shapes = scalar_shapes + nonempty_array_shapes
all_shapes = scalar_shapes + array_shapes
float_dtypes = jtu.dtypes.all_floating
complex_dtypes = jtu.dtypes.complex
int_dtypes = jtu.dtypes.all_integer
unsigned_dtypes = jtu.dtypes.all_unsigned
bool_dtypes = jtu.dtypes.boolean
default_dtypes = float_dtypes + int_dtypes
inexact_dtypes = float_dtypes + complex_dtypes
number_dtypes = float_dtypes + complex_dtypes + int_dtypes + unsigned_dtypes
all_dtypes = number_dtypes + bool_dtypes
python_scalar_dtypes = [jnp.bool_, jnp.int_, jnp.float_, jnp.complex_]
# uint64 is problematic because with any uint type it promotes to float:
int_dtypes_no_uint64 = [d for d in int_dtypes + unsigned_dtypes if d != np.uint64]
def _indexer_with_default_outputs(indexer, use_defaults=True):
"""Like jtu.with_jax_dtype_defaults, but for __getitem__ APIs"""
class Indexer:
@partial(jtu.with_jax_dtype_defaults, use_defaults=use_defaults)
def __getitem__(self, *args):
return indexer.__getitem__(*args)
return Indexer()
def _valid_dtypes_for_shape(shape, dtypes):
# Not all (shape, dtype) pairs are valid. In particular, Python scalars only
# have one type in each category (float, bool, etc.)
if shape is jtu.PYTHON_SCALAR_SHAPE:
return [t for t in dtypes if t in python_scalar_dtypes]
return dtypes
def _shape_and_dtypes(shapes, dtypes):
for shape in shapes:
for dtype in _valid_dtypes_for_shape(shape, dtypes):
yield (shape, dtype)
def _compatible_shapes(shape):
if np.ndim(shape) == 0 or shape in scalar_shapes:
return [shape]
return (shape[n:] for n in range(len(shape) + 1))
def _get_y_shapes(y_dtype, shape, rowvar):
# Helper function for testCov.
if y_dtype is None:
return [None]
if len(shape) == 1:
return [shape]
elif rowvar or shape[0] == 1:
return [(1, shape[-1]), (2, shape[-1]), (5, shape[-1])]
return [(shape[0], 1), (shape[0], 2), (shape[0], 5)]
OpRecord = collections.namedtuple(
"OpRecord",
["name", "nargs", "dtypes", "shapes", "rng_factory", "diff_modes",
"test_name", "check_dtypes", "tolerance", "inexact", "kwargs"])
def op_record(name, nargs, dtypes, shapes, rng_factory, diff_modes,
test_name=None, check_dtypes=True,
tolerance=None, inexact=False, kwargs=None):
test_name = test_name or name
return OpRecord(name, nargs, dtypes, shapes, rng_factory, diff_modes,
test_name, check_dtypes, tolerance, inexact, kwargs)
JAX_ONE_TO_ONE_OP_RECORDS = [
op_record("abs", 1, all_dtypes,
all_shapes, jtu.rand_default, ["rev"]),
op_record("add", 2, all_dtypes, all_shapes, jtu.rand_default, ["rev"]),
op_record("ceil", 1, float_dtypes, all_shapes, jtu.rand_default, []),
op_record("ceil", 1, int_dtypes + unsigned_dtypes, all_shapes,
jtu.rand_default, [], check_dtypes=False),
op_record("conj", 1, number_dtypes, all_shapes, jtu.rand_default, ["rev"]),
op_record("equal", 2, all_dtypes, all_shapes, jtu.rand_some_equal, []),
op_record("exp", 1, number_dtypes, all_shapes, jtu.rand_default, ["rev"],
inexact=True),
op_record("fabs", 1, float_dtypes, all_shapes, jtu.rand_default, ["rev"]),
op_record("float_power", 2, inexact_dtypes, all_shapes,
partial(jtu.rand_default, scale=1), ["rev"],
tolerance={jnp.bfloat16: 1e-2, np.float32: 1e-3,
np.float64: 1e-12, np.complex64: 2e-4,
np.complex128: 1e-12}, check_dtypes=False),
op_record("floor", 1, float_dtypes, all_shapes, jtu.rand_default, []),
op_record("floor", 1, int_dtypes + unsigned_dtypes, all_shapes,
jtu.rand_default, [], check_dtypes=False),
op_record("greater", 2, all_dtypes, all_shapes, jtu.rand_some_equal, []),
op_record("greater_equal", 2, all_dtypes, all_shapes, jtu.rand_some_equal, []),
op_record("i0", 1, float_dtypes, all_shapes, jtu.rand_default, [],
check_dtypes=False),
op_record("ldexp", 2, int_dtypes, all_shapes, jtu.rand_default, [], check_dtypes=False),
op_record("less", 2, all_dtypes, all_shapes, jtu.rand_some_equal, []),
op_record("less_equal", 2, all_dtypes, all_shapes, jtu.rand_some_equal, []),
op_record("log", 1, number_dtypes, all_shapes, jtu.rand_positive, ["rev"],
inexact=True),
op_record("logical_and", 2, all_dtypes, all_shapes, jtu.rand_bool, []),
op_record("logical_not", 1, all_dtypes, all_shapes, jtu.rand_bool, []),
op_record("logical_or", 2, all_dtypes, all_shapes, jtu.rand_bool, []),
op_record("logical_xor", 2, all_dtypes, all_shapes, jtu.rand_bool, []),
op_record("maximum", 2, all_dtypes, all_shapes, jtu.rand_some_inf, []),
op_record("minimum", 2, all_dtypes, all_shapes, jtu.rand_some_inf, []),
op_record("multiply", 2, all_dtypes, all_shapes, jtu.rand_default, ["rev"]),
op_record("negative", 1, number_dtypes, all_shapes, jtu.rand_default, ["rev"]),
op_record("nextafter", 2, [f for f in float_dtypes if f != jnp.bfloat16],
all_shapes, jtu.rand_default, ["rev"], inexact=True, tolerance=0),
op_record("not_equal", 2, all_dtypes, all_shapes, jtu.rand_some_equal, ["rev"]),
op_record("array_equal", 2, number_dtypes, all_shapes, jtu.rand_some_equal, ["rev"]),
op_record("array_equiv", 2, number_dtypes, all_shapes, jtu.rand_some_equal, ["rev"]),
op_record("reciprocal", 1, inexact_dtypes, all_shapes, jtu.rand_default, []),
op_record("subtract", 2, number_dtypes, all_shapes, jtu.rand_default, ["rev"]),
op_record("signbit", 1, default_dtypes + bool_dtypes, all_shapes,
jtu.rand_some_inf_and_nan, ["rev"]),
op_record("trunc", 1, float_dtypes, all_shapes, jtu.rand_some_inf_and_nan, []),
op_record("trunc", 1, int_dtypes + unsigned_dtypes, all_shapes,
jtu.rand_some_inf_and_nan, [], check_dtypes=False),
op_record("sin", 1, number_dtypes, all_shapes, jtu.rand_default, ["rev"],
inexact=True),
op_record("cos", 1, number_dtypes, all_shapes, jtu.rand_default, ["rev"],
inexact=True),
op_record("tan", 1, number_dtypes, all_shapes,
partial(jtu.rand_uniform, low=-1.5, high=1.5), ["rev"],
inexact=True),
op_record("sinh", 1, number_dtypes, all_shapes, jtu.rand_default, ["rev"],
inexact=True),
op_record("cosh", 1, number_dtypes, all_shapes, jtu.rand_default, ["rev"],
inexact=True),
# TODO(b/142975473): on CPU, tanh for complex128 is only accurate to
# ~float32 precision.
# TODO(b/143135720): on GPU, tanh has only ~float32 precision.
op_record("tanh", 1, number_dtypes, all_shapes, jtu.rand_default, ["rev"],
tolerance={np.float64: 1e-7, np.complex128: 1e-7},
inexact=True),
op_record("arcsin", 1, number_dtypes, all_shapes, jtu.rand_small, ["rev"],
inexact=True),
op_record("arccos", 1, number_dtypes, all_shapes, jtu.rand_small, ["rev"],
inexact=True),
op_record("arctan", 1, number_dtypes, all_shapes, jtu.rand_small, ["rev"],
inexact=True),
op_record("arctan2", 2, float_dtypes, all_shapes, jtu.rand_small, ["rev"],
inexact=True),
op_record("arcsinh", 1, number_dtypes, all_shapes, jtu.rand_default, ["rev"],
inexact=True, tolerance={np.complex64: 2E-4, np.complex128: 2E-14}),
op_record("arccosh", 1, number_dtypes, all_shapes, jtu.rand_default, ["rev"],
inexact=True, tolerance={np.complex64: 2E-2, np.complex128: 2E-12}),
op_record("arctanh", 1, number_dtypes, all_shapes, jtu.rand_small, ["rev"],
inexact=True, tolerance={np.float64: 1e-9}),
]
JAX_COMPOUND_OP_RECORDS = [
# angle has inconsistent 32/64-bit return types across numpy versions.
op_record("angle", 1, number_dtypes, all_shapes, jtu.rand_default, [],
check_dtypes=False, inexact=True),
op_record("angle", 1, number_dtypes, all_shapes, jtu.rand_default, [],
check_dtypes=False, inexact=True, test_name="angle_deg", kwargs={'deg': True}),
op_record("atleast_1d", 1, default_dtypes, all_shapes, jtu.rand_default, []),
op_record("atleast_2d", 1, default_dtypes, all_shapes, jtu.rand_default, []),
op_record("atleast_3d", 1, default_dtypes, all_shapes, jtu.rand_default, []),
op_record("cbrt", 1, default_dtypes, all_shapes, jtu.rand_some_inf, ["rev"],
inexact=True),
op_record("conjugate", 1, number_dtypes, all_shapes, jtu.rand_default, ["rev"]),
op_record("deg2rad", 1, float_dtypes, all_shapes, jtu.rand_default, []),
op_record("divide", 2, number_dtypes, all_shapes, jtu.rand_nonzero, ["rev"],
inexact=True),
op_record("divmod", 2, int_dtypes + float_dtypes, all_shapes,
jtu.rand_nonzero, []),
op_record("exp2", 1, number_dtypes, all_shapes, jtu.rand_default, ["rev"],
tolerance={jnp.bfloat16: 4e-2, np.float16: 1e-2}, inexact=True),
# TODO(b/142975473): on CPU, expm1 for float64 is only accurate to ~float32
# precision.
op_record("expm1", 1, number_dtypes, all_shapes, jtu.rand_positive, [],
test_name="expm1_large", tolerance={np.float64: 1e-8}, inexact=True),
op_record("expm1", 1, number_dtypes, all_shapes, jtu.rand_small_positive,
[], tolerance={np.float64: 1e-8}, inexact=True),
op_record("fix", 1, float_dtypes, all_shapes, jtu.rand_default, []),
op_record("fix", 1, int_dtypes + unsigned_dtypes, all_shapes,
jtu.rand_default, [], check_dtypes=False),
op_record("floor_divide", 2, default_dtypes + unsigned_dtypes,
all_shapes, jtu.rand_nonzero, ["rev"]),
op_record("fmin", 2, number_dtypes, all_shapes, jtu.rand_some_nan, []),
op_record("fmax", 2, number_dtypes, all_shapes, jtu.rand_some_nan, []),
op_record("fmod", 2, default_dtypes, all_shapes, jtu.rand_some_nan, []),
op_record("heaviside", 2, default_dtypes, all_shapes, jtu.rand_default, [],
inexact=True),
op_record("hypot", 2, default_dtypes, all_shapes, jtu.rand_default, [],
inexact=True),
op_record("kron", 2, number_dtypes, nonempty_shapes, jtu.rand_default, []),
op_record("outer", 2, number_dtypes, all_shapes, jtu.rand_default, []),
op_record("imag", 1, number_dtypes, all_shapes, jtu.rand_some_inf, []),
op_record("iscomplex", 1, number_dtypes, all_shapes, jtu.rand_some_inf, []),
op_record("isfinite", 1, inexact_dtypes, all_shapes, jtu.rand_some_inf_and_nan, []),
op_record("isinf", 1, inexact_dtypes, all_shapes, jtu.rand_some_inf_and_nan, []),
op_record("isnan", 1, inexact_dtypes, all_shapes, jtu.rand_some_inf_and_nan, []),
op_record("isneginf", 1, float_dtypes, all_shapes, jtu.rand_some_inf_and_nan, []),
op_record("isposinf", 1, float_dtypes, all_shapes, jtu.rand_some_inf_and_nan, []),
op_record("isreal", 1, number_dtypes, all_shapes, jtu.rand_some_inf, []),
op_record("isrealobj", 1, number_dtypes, all_shapes, jtu.rand_some_inf, []),
op_record("log2", 1, number_dtypes, all_shapes, jtu.rand_positive, ["rev"],
inexact=True),
op_record("log10", 1, number_dtypes, all_shapes, jtu.rand_positive, ["rev"],
inexact=True),
op_record("log1p", 1, number_dtypes, all_shapes, jtu.rand_positive, [],
test_name="log1p_large", tolerance={np.float64: 1e-12},
inexact=True),
op_record("log1p", 1, number_dtypes, all_shapes, jtu.rand_small_positive, [],
tolerance={np.float64: 1e-12}, inexact=True),
op_record("logaddexp", 2, float_dtypes, all_shapes,
jtu.rand_some_inf_and_nan, ["rev"],
tolerance={np.float64: 1e-12}, inexact=True),
op_record("logaddexp2", 2, float_dtypes, all_shapes,
jtu.rand_some_inf_and_nan, ["rev"],
tolerance={np.float16: 1e-2, np.float64: 2e-14}, inexact=True),
op_record("polyval", 2, number_dtypes, nonempty_nonscalar_array_shapes,
jtu.rand_default, [], check_dtypes=False,
tolerance={dtypes.bfloat16: 4e-2, np.float16: 1e-2,
np.float64: 1e-12}),
op_record("positive", 1, number_dtypes, all_shapes, jtu.rand_default, ["rev"]),
op_record("power", 2, number_dtypes, all_shapes, jtu.rand_positive, ["rev"],
tolerance={np.complex128: 1e-14}, check_dtypes=False),
op_record("rad2deg", 1, float_dtypes, all_shapes, jtu.rand_default, []),
op_record("ravel", 1, all_dtypes, all_shapes, jtu.rand_default, ["rev"]),
op_record("real", 1, number_dtypes, all_shapes, jtu.rand_some_inf, []),
op_record("remainder", 2, default_dtypes, all_shapes, jtu.rand_nonzero, [],
tolerance={np.float16: 1e-2}),
op_record("mod", 2, default_dtypes, all_shapes, jtu.rand_nonzero, []),
op_record("modf", 1, float_dtypes, all_shapes, jtu.rand_default, []),
op_record("modf", 1, int_dtypes + unsigned_dtypes, all_shapes,
jtu.rand_default, [], check_dtypes=False),
op_record("rint", 1, inexact_dtypes, all_shapes, jtu.rand_some_inf_and_nan,
[]),
op_record("rint", 1, int_dtypes + unsigned_dtypes, all_shapes,
jtu.rand_default, [], check_dtypes=False),
op_record("sign", 1, number_dtypes, all_shapes, jtu.rand_some_inf_and_nan, []),
# numpy 1.16 has trouble mixing uint and bfloat16, so we test these separately.
op_record("copysign", 2, default_dtypes + unsigned_dtypes,
all_shapes, jtu.rand_some_inf_and_nan, [], check_dtypes=False),
op_record("sinc", 1, [t for t in number_dtypes if t != jnp.bfloat16],
all_shapes, jtu.rand_default, ["rev"],
tolerance={np.complex64: 1e-5}, inexact=True,
check_dtypes=False),
op_record("square", 1, number_dtypes, all_shapes, jtu.rand_default, ["rev"]),
op_record("sqrt", 1, number_dtypes, all_shapes, jtu.rand_positive, ["rev"],
inexact=True),
op_record("transpose", 1, all_dtypes, all_shapes, jtu.rand_default, ["rev"],
check_dtypes=False),
op_record("true_divide", 2, all_dtypes, all_shapes, jtu.rand_nonzero,
["rev"], inexact=True),
op_record("ediff1d", 3, [np.int32], all_shapes, jtu.rand_default, [], check_dtypes=False),
# TODO(phawkins): np.unwrap does not correctly promote its default period
# argument under NumPy 1.21 for bfloat16 inputs. It works fine if we
# explicitly pass a bfloat16 value that does not need promition. We should
# probably add a custom test harness for unwrap that tests the period
# argument anyway.
op_record("unwrap", 1, [t for t in float_dtypes if t != dtypes.bfloat16],
nonempty_nonscalar_array_shapes,
jtu.rand_default, ["rev"],
# numpy.unwrap always returns float64
check_dtypes=False,
# numpy cumsum is inaccurate, see issue #3517
tolerance={dtypes.bfloat16: 1e-1, np.float16: 1e-1}),
op_record("isclose", 2, [t for t in all_dtypes if t != jnp.bfloat16],
all_shapes, jtu.rand_small_positive, []),
op_record("gcd", 2, int_dtypes_no_uint64, all_shapes, jtu.rand_default, []),
op_record("lcm", 2, int_dtypes_no_uint64, all_shapes, jtu.rand_default, []),
]
JAX_BITWISE_OP_RECORDS = [
op_record("bitwise_and", 2, int_dtypes + unsigned_dtypes, all_shapes,
jtu.rand_fullrange, []),
op_record("bitwise_not", 1, int_dtypes + unsigned_dtypes, all_shapes,
jtu.rand_fullrange, []),
op_record("invert", 1, int_dtypes + unsigned_dtypes, all_shapes,
jtu.rand_fullrange, []),
op_record("bitwise_or", 2, int_dtypes + unsigned_dtypes, all_shapes,
jtu.rand_fullrange, []),
op_record("bitwise_xor", 2, int_dtypes + unsigned_dtypes, all_shapes,
jtu.rand_fullrange, []),
]
JAX_REDUCER_RECORDS = [
op_record("mean", 1, number_dtypes, nonempty_shapes, jtu.rand_default, [],
inexact=True),
op_record("prod", 1, all_dtypes, all_shapes, jtu.rand_small_positive, []),
op_record("sum", 1, all_dtypes, all_shapes, jtu.rand_default, []),
op_record("nanmean", 1, inexact_dtypes, nonempty_shapes, jtu.rand_some_nan,
[], inexact=True),
op_record("nanprod", 1, all_dtypes, all_shapes, jtu.rand_some_nan, []),
op_record("nansum", 1, number_dtypes, all_shapes, jtu.rand_some_nan, []),
]
JAX_REDUCER_INITIAL_RECORDS = [
op_record("prod", 1, all_dtypes, all_shapes, jtu.rand_small_positive, []),
op_record("sum", 1, all_dtypes, all_shapes, jtu.rand_default, []),
op_record("max", 1, all_dtypes, all_shapes, jtu.rand_default, []),
op_record("min", 1, all_dtypes, all_shapes, jtu.rand_default, []),
]
if numpy_version >= (1, 22): # initial & where keywords added in numpy 1.22
JAX_REDUCER_INITIAL_RECORDS += [
op_record("nanprod", 1, inexact_dtypes, all_shapes, jtu.rand_small_positive, []),
op_record("nansum", 1, inexact_dtypes, all_shapes, jtu.rand_default, []),
op_record("nanmax", 1, inexact_dtypes, all_shapes, jtu.rand_default, []),
op_record("nanmin", 1, inexact_dtypes, all_shapes, jtu.rand_default, []),
]
JAX_REDUCER_WHERE_NO_INITIAL_RECORDS = [
op_record("all", 1, bool_dtypes, all_shapes, jtu.rand_some_zero, []),
op_record("any", 1, bool_dtypes, all_shapes, jtu.rand_some_zero, []),
op_record("mean", 1, all_dtypes, nonempty_shapes, jtu.rand_default, [],
inexact=True),
op_record("var", 1, all_dtypes, nonempty_shapes, jtu.rand_default, [],
inexact=True),
op_record("std", 1, all_dtypes, nonempty_shapes, jtu.rand_default, [],
inexact=True),
]
if numpy_version >= (1, 22): # where keyword added in numpy 1.22
JAX_REDUCER_WHERE_NO_INITIAL_RECORDS += [
op_record("nanmean", 1, inexact_dtypes, nonempty_shapes, jtu.rand_default, [],
inexact=True),
op_record("nanvar", 1, inexact_dtypes, nonempty_shapes, jtu.rand_default, [],
inexact=True),
op_record("nanstd", 1, inexact_dtypes, nonempty_shapes, jtu.rand_default, [],
inexact=True),
]
JAX_REDUCER_NO_DTYPE_RECORDS = [
op_record("all", 1, all_dtypes, all_shapes, jtu.rand_some_zero, []),
op_record("any", 1, all_dtypes, all_shapes, jtu.rand_some_zero, []),
op_record("max", 1, all_dtypes, nonempty_shapes, jtu.rand_default, []),
op_record("min", 1, all_dtypes, nonempty_shapes, jtu.rand_default, []),
op_record("var", 1, all_dtypes, nonempty_shapes, jtu.rand_default, [],
inexact=True),
op_record("std", 1, all_dtypes, nonempty_shapes, jtu.rand_default, [],
inexact=True),
op_record("nanmax", 1, all_dtypes, nonempty_shapes, jtu.rand_some_nan, []),
op_record("nanmin", 1, all_dtypes, nonempty_shapes, jtu.rand_some_nan, []),
op_record("nanvar", 1, all_dtypes, nonempty_shapes, jtu.rand_some_nan,
[], inexact=True),
op_record("nanstd", 1, all_dtypes, nonempty_shapes, jtu.rand_some_nan,
[], inexact=True),
op_record("ptp", 1, number_dtypes, nonempty_shapes, jtu.rand_default, []),
]
JAX_ARGMINMAX_RECORDS = [
op_record("argmin", 1, default_dtypes, nonempty_shapes, jtu.rand_some_equal, []),
op_record("argmax", 1, default_dtypes, nonempty_shapes, jtu.rand_some_equal, []),
op_record("nanargmin", 1, default_dtypes, nonempty_shapes, jtu.rand_some_nan, []),
op_record("nanargmax", 1, default_dtypes, nonempty_shapes, jtu.rand_some_nan, []),
]
JAX_OPERATOR_OVERLOADS = [
op_record("__add__", 2, number_dtypes, all_shapes, jtu.rand_default, []),
op_record("__sub__", 2, number_dtypes, all_shapes, jtu.rand_default, []),
op_record("__mul__", 2, number_dtypes, all_shapes, jtu.rand_default, []),
op_record("__eq__", 2, number_dtypes, all_shapes, jtu.rand_default, []),
op_record("__ne__", 2, number_dtypes, all_shapes, jtu.rand_default, []),
op_record("__lt__", 2, default_dtypes, all_shapes, jtu.rand_default, []),
op_record("__le__", 2, default_dtypes, all_shapes, jtu.rand_default, []),
op_record("__gt__", 2, default_dtypes, all_shapes, jtu.rand_default, []),
op_record("__ge__", 2, default_dtypes, all_shapes, jtu.rand_default, []),
op_record("__pos__", 1, number_dtypes, all_shapes, jtu.rand_default, []),
op_record("__neg__", 1, number_dtypes, all_shapes, jtu.rand_default, []),
op_record("__pow__", 2, inexact_dtypes, all_shapes, jtu.rand_positive, [],
tolerance={np.float32: 2e-4, np.complex64: 2e-4, np.complex128: 1e-14}),
op_record("__mod__", 2, default_dtypes, all_shapes, jtu.rand_nonzero, [],
tolerance={np.float16: 1e-1}),
op_record("__floordiv__", 2, default_dtypes, all_shapes,
jtu.rand_nonzero, []),
op_record("__truediv__", 2, number_dtypes, all_shapes, jtu.rand_nonzero, [],
inexact=True),
op_record("__abs__", 1, number_dtypes, all_shapes, jtu.rand_default, []),
# TODO(mattjj): __invert__ fails on bool dtypes because ~True == -2
op_record("__invert__", 1, int_dtypes, all_shapes, jtu.rand_default, []),
# TODO(mattjj): investigate these failures
# op_record("__or__", 2, number_dtypes, all_shapes, jtu.rand_bool, []),
# op_record("__and__", 2, number_dtypes, all_shapes, jtu.rand_default, []),
# op_record("__xor__", 2, number_dtypes, all_shapes, jtu.rand_bool, []),
# op_record("__divmod__", 2, number_dtypes, all_shapes, jtu.rand_nonzero, []),
op_record("__lshift__", 2, int_dtypes_no_uint64, all_shapes, partial(jtu.rand_int, high=8), []),
op_record("__rshift__", 2, int_dtypes_no_uint64, all_shapes, partial(jtu.rand_int, high=8), []),
]
JAX_RIGHT_OPERATOR_OVERLOADS = [
op_record("__radd__", 2, number_dtypes, all_shapes, jtu.rand_default, []),
op_record("__rsub__", 2, number_dtypes, all_shapes, jtu.rand_default, []),
op_record("__rmul__", 2, number_dtypes, all_shapes, jtu.rand_default, []),
op_record("__rpow__", 2, inexact_dtypes, all_shapes, jtu.rand_positive, [],
tolerance={np.float32: 2e-4, np.complex64: 1e-3}),
op_record("__rmod__", 2, default_dtypes, all_shapes, jtu.rand_nonzero, [],
tolerance={np.float16: 1e-1}),
op_record("__rfloordiv__", 2, default_dtypes, all_shapes,
jtu.rand_nonzero, []),
op_record("__rtruediv__", 2, number_dtypes, all_shapes, jtu.rand_nonzero, [],
inexact=True),
# op_record("__ror__", 2, number_dtypes, all_shapes, jtu.rand_bool, []),
# op_record("__rand__", 2, number_dtypes, all_shapes, jtu.rand_default, []),
# op_record("__rxor__", 2, number_dtypes, all_shapes, jtu.rand_bool, []),
# op_record("__rdivmod__", 2, number_dtypes, all_shapes, jtu.rand_nonzero, []),
op_record("__rlshift__", 2, int_dtypes_no_uint64, all_shapes, partial(jtu.rand_int, high=8), []),
op_record("__rrshift__", 2, int_dtypes_no_uint64, all_shapes, partial(jtu.rand_int, high=8), [])
]
class _OverrideEverything:
pass
for rec in JAX_OPERATOR_OVERLOADS + JAX_RIGHT_OPERATOR_OVERLOADS:
if rec.nargs == 2:
setattr(_OverrideEverything, rec.name, lambda self, other: self)
class _OverrideNothing:
pass
for rec in JAX_OPERATOR_OVERLOADS + JAX_RIGHT_OPERATOR_OVERLOADS:
if rec.nargs == 2:
setattr(_OverrideNothing, rec.name, lambda self, other: NotImplemented)
def _dtypes_are_compatible_for_bitwise_ops(args):
if len(args) <= 1:
return True
is_signed = lambda dtype: jnp.issubdtype(dtype, np.signedinteger)
width = lambda dtype: jnp.iinfo(dtype).bits
x, y = args
if width(x) > width(y):
x, y = y, x
# The following condition seems a little ad hoc, but seems to capture what
# numpy actually implements.
return (
is_signed(x) == is_signed(y)
or (width(x) == 32 and width(y) == 32)
or (width(x) == 32 and width(y) == 64 and is_signed(y)))
def _shapes_are_broadcast_compatible(shapes):
try:
lax.broadcast_shapes(*(() if s in scalar_shapes else s for s in shapes))
except ValueError:
return False
else:
return True
def _shapes_are_equal_length(shapes):
return all(len(shape) == len(shapes[0]) for shape in shapes[1:])
def _promote_like_jnp(fun, inexact=False):
"""Decorator that promotes the arguments of `fun` to `jnp.result_type(*args)`.
jnp and np have different type promotion semantics; this decorator allows
tests make an np reference implementation act more like an jnp
implementation.
"""
_promote = _promote_dtypes_inexact if inexact else _promote_dtypes
def wrapper(*args, **kw):
flat_args, tree = tree_util.tree_flatten(args)
args = tree_util.tree_unflatten(tree, _promote(*flat_args))
return fun(*args, **kw)
return wrapper
@jtu.with_config(jax_numpy_dtype_promotion='strict')
class LaxBackedNumpyTests(jtu.JaxTestCase):
"""Tests for LAX-backed Numpy implementation."""
def _GetArgsMaker(self, rng, shapes, dtypes, np_arrays=True):
def f():
out = [rng(shape, dtype or jnp.float_)
for shape, dtype in zip(shapes, dtypes)]
if np_arrays:
return out
return [jnp.asarray(a) if isinstance(a, (np.ndarray, np.generic)) else a
for a in out]
return f
def testNotImplemented(self):
for name in jnp._NOT_IMPLEMENTED:
func = getattr(jnp, name)
with self.assertRaises(NotImplementedError):
func()
@parameterized.named_parameters(
{"testcase_name": f"_{dtype.__name__}", "dtype": dtype}
for dtype in [jnp.bool_, jnp.uint8, jnp.uint16, jnp.uint32, jnp.uint64,
jnp.int8, jnp.int16, jnp.int32, jnp.int64,
jnp.bfloat16, jnp.float16, jnp.float32, jnp.float64,
jnp.complex64, jnp.complex128]
if dtype == dtypes.canonicalize_dtype(dtype))
def testDtypeWrappers(self, dtype):
arr = dtype(0)
self.assertIsInstance(arr, jnp.ndarray)
self.assertEqual(arr.dtype, np.dtype(dtype))
self.assertArraysEqual(arr, 0, check_dtypes=False)
# No copy primitive is generated
jaxpr = jax.make_jaxpr(dtype)(0)
prims = [eqn.primitive for eqn in jaxpr.eqns]
self.assertEqual(prims, [lax.convert_element_type_p]) # No copy generated.
@parameterized.named_parameters(jtu.cases_from_list(
{"testcase_name": f"_{dtype}_allow_picke={allow_pickle}",
"dtype": dtype, "allow_pickle": allow_pickle}
for dtype in float_dtypes + [object]
for allow_pickle in [True, False]))
def testLoad(self, dtype, allow_pickle):
if dtype == object and not allow_pickle:
self.skipTest("dtype=object requires allow_pickle=True")
rng = jtu.rand_default(self.rng())
arr = rng((10), dtype)
with io.BytesIO() as f:
jnp.save(f, arr)
f.seek(0)
arr_out = jnp.load(f, allow_pickle=allow_pickle)
self.assertArraysEqual(arr, arr_out)
@parameterized.named_parameters(itertools.chain.from_iterable(
jtu.cases_from_list(
{"testcase_name": jtu.format_test_name_suffix(rec.test_name, shapes,
dtypes),
"rng_factory": rec.rng_factory, "shapes": shapes, "dtypes": dtypes,
"np_op": getattr(np, rec.name), "jnp_op": getattr(jnp, rec.name),
"check_dtypes": rec.check_dtypes, "tolerance": rec.tolerance,
"inexact": rec.inexact, "kwargs": rec.kwargs or {}}
for shapes in filter(
_shapes_are_broadcast_compatible,
itertools.combinations_with_replacement(rec.shapes, rec.nargs))
for dtypes in itertools.product(
*(_valid_dtypes_for_shape(s, rec.dtypes) for s in shapes)))
for rec in itertools.chain(JAX_ONE_TO_ONE_OP_RECORDS,
JAX_COMPOUND_OP_RECORDS)))
@jax.numpy_rank_promotion('allow') # This test explicitly exercises implicit rank promotion.
def testOp(self, np_op, jnp_op, rng_factory, shapes, dtypes, check_dtypes,
tolerance, inexact, kwargs):
np_op = partial(np_op, **kwargs)
jnp_op = partial(jnp_op, **kwargs)
np_op = jtu.ignore_warning(category=RuntimeWarning,
message="invalid value.*")(np_op)
np_op = jtu.ignore_warning(category=RuntimeWarning,
message="divide by zero.*")(np_op)
rng = rng_factory(self.rng())
args_maker = self._GetArgsMaker(rng, shapes, dtypes, np_arrays=False)
tol = max(jtu.tolerance(dtype, tolerance) for dtype in dtypes)
tol = functools.reduce(jtu.join_tolerance,
[tolerance, tol, jtu.default_tolerance()])
with jtu.strict_promotion_if_dtypes_match(dtypes):
self._CheckAgainstNumpy(_promote_like_jnp(np_op, inexact), jnp_op,
args_maker, check_dtypes=check_dtypes, tol=tol)
self._CompileAndCheck(jnp_op, args_maker, check_dtypes=check_dtypes,
atol=tol, rtol=tol)
@parameterized.named_parameters(itertools.chain.from_iterable(
jtu.cases_from_list(
{"testcase_name": jtu.format_test_name_suffix(rec.test_name, shapes,
dtypes),
"rng_factory": rec.rng_factory, "shapes": shapes, "dtypes": dtypes, "name": rec.name,
"tol": rec.tolerance}
for shapes in filter(
_shapes_are_broadcast_compatible,
itertools.combinations_with_replacement(rec.shapes, rec.nargs))
for dtypes in itertools.product(
*(_valid_dtypes_for_shape(s, rec.dtypes) for s in shapes)))
for rec in JAX_OPERATOR_OVERLOADS))
@jax.numpy_rank_promotion('allow') # This test explicitly exercises implicit rank promotion.
def testOperatorOverload(self, name, rng_factory, shapes, dtypes, tol):
rng = rng_factory(self.rng())
# np and jnp arrays have different type promotion rules; force the use of
# jnp arrays.
args_maker = self._GetArgsMaker(rng, shapes, dtypes, np_arrays=False)
fun = lambda *xs: getattr(operator, name.strip('_'))(*xs)
with jtu.strict_promotion_if_dtypes_match(dtypes):
self._CompileAndCheck(fun, args_maker, atol=tol, rtol=tol)
@parameterized.named_parameters(itertools.chain.from_iterable(
jtu.cases_from_list(
{"testcase_name": jtu.format_test_name_suffix(rec.test_name, shapes,
dtypes),
"rng_factory": rec.rng_factory, "shapes": shapes, "dtypes": dtypes, "name": rec.name,
"op_tolerance": rec.tolerance}
for shapes in filter(
_shapes_are_broadcast_compatible,
itertools.combinations_with_replacement(rec.shapes, rec.nargs))
for dtypes in itertools.product(
*(_valid_dtypes_for_shape(s, rec.dtypes) for s in shapes)))
for rec in JAX_RIGHT_OPERATOR_OVERLOADS))
@jax.numpy_rank_promotion('allow') # This test explicitly exercises implicit rank promotion.
def testRightOperatorOverload(self, name, rng_factory, shapes, dtypes,
op_tolerance):
if shapes[1] is jtu.PYTHON_SCALAR_SHAPE:
raise SkipTest("scalars not implemented") # TODO(mattjj): clean up
rng = rng_factory(self.rng())
args_maker = self._GetArgsMaker(rng, shapes, dtypes, np_arrays=False)
fun = lambda fst, snd: getattr(snd, name)(fst)
tol = max(jtu.tolerance(dtype, op_tolerance) for dtype in dtypes)
with jtu.strict_promotion_if_dtypes_match(dtypes):
self._CompileAndCheck( fun, args_maker, atol=tol, rtol=tol)
@parameterized.named_parameters(jtu.cases_from_list(
{"testcase_name": f"{rec.test_name}_{othertype}", "name": rec.name, "othertype": othertype}
for rec in JAX_OPERATOR_OVERLOADS if rec.nargs == 2
for othertype in [dict, list, tuple, set]))
def testOperatorOverloadErrors(self, name, othertype):
# Test that binary operators with builtin collections raise a TypeError
# and report the types in the correct order.
data = [(1, 2), (2, 3)]
arr = jnp.array(data)
other = othertype(data)
msg = f"unsupported operand type.* 'DeviceArray' and '{othertype.__name__}'"
with self.assertRaisesRegex(TypeError, msg):
getattr(arr, name)(other)
@parameterized.named_parameters(jtu.cases_from_list(
{"testcase_name": f"{rec.test_name}_{othertype}", "name": rec.name, "othertype": othertype}
for rec in JAX_RIGHT_OPERATOR_OVERLOADS if rec.nargs == 2
for othertype in [dict, list, tuple, set]))
def testRightOperatorOverloadErrors(self, name, othertype):
# Test that binary operators with builtin collections raise a TypeError
# and report the types in the correct order.
data = [(1, 2), (2, 3)]
arr = jnp.array(data)
other = othertype(data)
msg = f"unsupported operand type.* '{othertype.__name__}' and 'DeviceArray'"
with self.assertRaisesRegex(TypeError, msg):
getattr(arr, name)(other)
@parameterized.named_parameters(jtu.cases_from_list(
{"testcase_name": rec.test_name + f"_{dtype}",
"rng_factory": rec.rng_factory,
"op_name": rec.name, "dtype": dtype}
for rec in JAX_OPERATOR_OVERLOADS if rec.nargs == 2
for dtype in rec.dtypes))
def testBinaryOperatorDefers(self, op_name, rng_factory, dtype):
rng = rng_factory(self.rng())
arg = jax.device_put(rng((), dtype))
op = getattr(operator, op_name)
other = _OverrideEverything()
assert op(other, arg) is other
assert op(arg, other) is other
other = _OverrideNothing()
if op_name == "__eq__":
assert op(other, arg) is False
assert op(arg, other) is False
elif op_name == "__ne__":
assert op(other, arg) is True
assert op(arg, other) is True
else:
with self.assertRaises(TypeError):
op(other, arg)
with self.assertRaises(TypeError):
op(arg, other)
def testArrayEqualExamples(self):
# examples from the array_equal() docstring.
self.assertTrue(jnp.array_equal([1, 2], [1, 2]))
self.assertTrue(jnp.array_equal(np.array([1, 2]), np.array([1, 2])))
self.assertFalse(jnp.array_equal([1, 2], [1, 2, 3]))
self.assertFalse(jnp.array_equal([1, 2], [1, 4]))
a = np.array([1, np.nan])
self.assertFalse(jnp.array_equal(a, a))
self.assertTrue(jnp.array_equal(a, a, equal_nan=True))
a = np.array([1 + 1j])
b = a.copy()
a.real = np.nan
b.imag = np.nan
self.assertTrue(jnp.array_equal(a, b, equal_nan=True))
def testArrayEquivExamples(self):
# examples from the array_equiv() docstring.
self.assertTrue(jnp.array_equiv([1, 2], [1, 2]))
self.assertFalse(jnp.array_equiv([1, 2], [1, 3]))
with jax.numpy_rank_promotion('allow'):
self.assertTrue(jnp.array_equiv([1, 2], [[1, 2], [1, 2]]))
self.assertFalse(jnp.array_equiv([1, 2], [[1, 2, 1, 2], [1, 2, 1, 2]]))
self.assertFalse(jnp.array_equiv([1, 2], [[1, 2], [1, 3]]))
def testArrayModule(self):
if numpy_dispatch is None:
raise SkipTest('requires https://github.com/seberg/numpy-dispatch')
jnp_array = jnp.array(1.0)
np_array = np.array(1.0)
module = numpy_dispatch.get_array_module(jnp_array)
self.assertIs(module, jnp)
module = numpy_dispatch.get_array_module(jnp_array, np_array)
self.assertIs(module, jnp)
def f(x):
module = numpy_dispatch.get_array_module(x)
self.assertIs(module, jnp)
return x
jax.jit(f)(jnp_array)
jax.grad(f)(jnp_array)
@parameterized.named_parameters(itertools.chain.from_iterable(
jtu.cases_from_list(
{"testcase_name": jtu.format_test_name_suffix(
rec.test_name, shapes, dtypes),
"rng_factory": rec.rng_factory, "shapes": shapes, "dtypes": dtypes,
"np_op": getattr(np, rec.name), "jnp_op": getattr(jnp, rec.name)}
for shapes in filter(
_shapes_are_broadcast_compatible,
itertools.combinations_with_replacement(rec.shapes, rec.nargs))
for dtypes in filter(
_dtypes_are_compatible_for_bitwise_ops,
itertools.combinations_with_replacement(rec.dtypes, rec.nargs)))
for rec in JAX_BITWISE_OP_RECORDS))
@jax.numpy_rank_promotion('allow') # This test explicitly exercises implicit rank promotion.
def testBitwiseOp(self, np_op, jnp_op, rng_factory, shapes, dtypes):
rng = rng_factory(self.rng())
args_maker = self._GetArgsMaker(rng, shapes, dtypes)
with jtu.strict_promotion_if_dtypes_match(dtypes):
self._CheckAgainstNumpy(_promote_like_jnp(np_op), jnp_op, args_maker)
self._CompileAndCheck(jnp_op, args_maker)
@parameterized.named_parameters(jtu.cases_from_list(
{"testcase_name": jtu.format_test_name_suffix(op.__name__, shapes, dtypes),
"op": op, "dtypes": dtypes, "shapes": shapes}
for op in [jnp.left_shift, jnp.right_shift]
for shapes in filter(
_shapes_are_broadcast_compatible,
# TODO numpy always promotes to shift dtype for zero-dim shapes:
itertools.combinations_with_replacement(nonzerodim_shapes, 2))
for dtypes in itertools.product(
*(_valid_dtypes_for_shape(s, int_dtypes_no_uint64) for s in shapes))))
@jax.numpy_rank_promotion('allow') # This test explicitly exercises implicit rank promotion.
def testShiftOpAgainstNumpy(self, op, dtypes, shapes):
dtype, shift_dtype = dtypes
signed_mix = np.issubdtype(dtype, np.signedinteger) != \
np.issubdtype(shift_dtype, np.signedinteger)
has_32 = any(np.iinfo(d).bits == 32 for d in dtypes)
promoting_to_64 = has_32 and signed_mix
if promoting_to_64 and not config.x64_enabled:
self.skipTest("np.right_shift/left_shift promoting to int64"
"differs from jnp in 32 bit mode.")
info, shift_info = map(np.iinfo, dtypes)
x_rng = jtu.rand_int(self.rng(), low=info.min, high=info.max + 1)
# NumPy requires shifts to be non-negative and below the bit width:
shift_rng = jtu.rand_int(self.rng(), high=max(info.bits, shift_info.bits))
args_maker = lambda: (x_rng(shapes[0], dtype), shift_rng(shapes[1], shift_dtype))
np_op = getattr(np, op.__name__)
with jtu.strict_promotion_if_dtypes_match(dtypes):
self._CompileAndCheck(op, args_maker)
self._CheckAgainstNumpy(np_op, op, args_maker)
@parameterized.named_parameters(itertools.chain.from_iterable(
jtu.cases_from_list(
{"testcase_name": "{}_inshape={}_axis={}_dtype={}_keepdims={}".format(
rec.test_name.capitalize(),
jtu.format_shape_dtype_string(shape, dtype), axis,
"None" if out_dtype is None else np.dtype(out_dtype).name, keepdims),
"rng_factory": rec.rng_factory, "shape": shape, "dtype": dtype, "out_dtype": out_dtype,
"np_op": getattr(np, rec.name), "jnp_op": getattr(jnp, rec.name),
"axis": axis, "keepdims": keepdims, "inexact": rec.inexact}
for shape in rec.shapes for dtype in rec.dtypes
for out_dtype in [None] + rec.dtypes if out_dtype not in unsigned_dtypes
for axis in list(range(-len(shape), len(shape))) + [None]
for keepdims in [False, True]
if jtu.is_valid_shape(shape, dtype))
for rec in JAX_REDUCER_RECORDS))
def testReducer(self, np_op, jnp_op, rng_factory, shape, dtype, out_dtype,
axis, keepdims, inexact):
rng = rng_factory(self.rng())
@jtu.ignore_warning(category=np.ComplexWarning)
@jtu.ignore_warning(category=RuntimeWarning,
message="mean of empty slice.*")
@jtu.ignore_warning(category=RuntimeWarning,
message="overflow encountered.*")
def np_fun(x):
x = np.asarray(x)
if inexact:
x = x.astype(dtypes._to_inexact_dtype(x.dtype))
x_cast = x if dtype != jnp.bfloat16 else x.astype(np.float32)
t = out_dtype if out_dtype != jnp.bfloat16 else np.float32
return np_op(x_cast, axis, dtype=t, keepdims=keepdims)
jnp_fun = lambda x: jnp_op(x, axis, dtype=out_dtype, keepdims=keepdims)
jnp_fun = jtu.ignore_warning(category=jnp.ComplexWarning)(jnp_fun)
args_maker = lambda: [rng(shape, dtype)]
tol_spec = {np.float16: 1e-2, np.int32: 1E-3, np.float32: 1e-3,
np.complex64: 1e-3, np.float64: 1e-5, np.complex128: 1e-5}
tol = jtu.tolerance(dtype, tol_spec)
tol = max(tol, jtu.tolerance(out_dtype, tol_spec)) if out_dtype else tol
self._CheckAgainstNumpy(np_fun, jnp_fun, args_maker,
check_dtypes=jnp.bfloat16 not in (dtype, out_dtype),
tol=tol)
self._CompileAndCheck(jnp_fun, args_maker, atol=tol,
rtol=tol)
@parameterized.named_parameters(itertools.chain.from_iterable(
jtu.cases_from_list(
{"testcase_name": "{}_inshape={}_axis={}_keepdims={}".format(
rec.test_name.capitalize(),
jtu.format_shape_dtype_string(shape, dtype), axis, keepdims),
"rng_factory": rec.rng_factory, "shape": shape, "dtype": dtype,
"np_op": getattr(np, rec.name), "jnp_op": getattr(jnp, rec.name),
"axis": axis, "keepdims": keepdims, "inexact": rec.inexact}
for shape in rec.shapes for dtype in rec.dtypes
for axis in list(range(-len(shape), len(shape))) + [None]
for keepdims in [False, True]
if jtu.is_valid_shape(shape, dtype))
for rec in JAX_REDUCER_NO_DTYPE_RECORDS))
def testReducerNoDtype(self, np_op, jnp_op, rng_factory, shape, dtype, axis,
keepdims, inexact):
rng = rng_factory(self.rng())
is_bf16_nan_test = dtype == jnp.bfloat16 and rng_factory.__name__ == 'rand_some_nan'
@jtu.ignore_warning(category=RuntimeWarning,
message="Degrees of freedom <= 0 for slice.*")
@jtu.ignore_warning(category=RuntimeWarning,
message="All-NaN (slice|axis) encountered.*")
def np_fun(x):
x = np.asarray(x)
if inexact:
x = x.astype(dtypes._to_inexact_dtype(x.dtype))
x_cast = x if not is_bf16_nan_test else x.astype(np.float32)
res = np_op(x_cast, axis, keepdims=keepdims)
res = res if not is_bf16_nan_test else res.astype(jnp.bfloat16)
return res
jnp_fun = lambda x: jnp_op(x, axis, keepdims=keepdims)
args_maker = lambda: [rng(shape, dtype)]
tol = {np.float16: 0.002}
self._CheckAgainstNumpy(np_fun, jnp_fun, args_maker, tol=tol)
self._CompileAndCheck(jnp_fun, args_maker, rtol=tol, atol=tol)
@parameterized.named_parameters(itertools.chain.from_iterable(
jtu.cases_from_list(
{"testcase_name": "{}_inshape={}_axis={}_keepdims={}_initial={}".format(
rec.test_name.capitalize(),
jtu.format_shape_dtype_string(shape, dtype), axis, keepdims, initial),
"rng_factory": rec.rng_factory, "shape": shape, "dtype": dtype,
"np_op": getattr(np, rec.name), "jnp_op": getattr(jnp, rec.name),
"initial": initial, "axis": axis, "keepdims": keepdims, "inexact": rec.inexact}
for shape in rec.shapes for dtype in rec.dtypes
for axis in list(range(-len(shape), len(shape))) + [None]
for initial in [0, 1] for keepdims in [False, True]
if jtu.is_valid_shape(shape, dtype))
for rec in JAX_REDUCER_INITIAL_RECORDS))
def testReducerInitial(self, np_op, jnp_op, rng_factory, shape, dtype, axis,
keepdims, initial, inexact):
rng = rng_factory(self.rng())
is_bf16_nan_test = dtype == jnp.bfloat16 and rng_factory.__name__ == 'rand_some_nan'
@jtu.ignore_warning(category=RuntimeWarning,
message="Degrees of freedom <= 0 for slice.*")
@jtu.ignore_warning(category=np.ComplexWarning)
def np_fun(x):
x = np.asarray(x)
if inexact:
x = x.astype(dtypes._to_inexact_dtype(x.dtype))
x_cast = x if not is_bf16_nan_test else x.astype(np.float32)
res = np_op(x_cast, axis, keepdims=keepdims, initial=initial)
res = res if not is_bf16_nan_test else res.astype(jnp.bfloat16)
return res
jnp_fun = lambda x: jnp_op(x, axis, keepdims=keepdims, initial=initial)
jnp_fun = jtu.ignore_warning(category=jnp.ComplexWarning)(jnp_fun)
args_maker = lambda: [rng(shape, dtype)]
tol = {jnp.bfloat16: 3E-2}
self._CheckAgainstNumpy(np_fun, jnp_fun, args_maker, rtol=tol)
self._CompileAndCheck(jnp_fun, args_maker)
@parameterized.named_parameters(itertools.chain.from_iterable(
jtu.cases_from_list(
{"testcase_name": "{}_inshape={}_axis={}_keepdims={}".format(
rec.test_name.capitalize(),
jtu.format_shape_dtype_string(shape, dtype), axis, keepdims),
"rng_factory": rec.rng_factory, "shape": shape, "dtype": dtype,
"np_op": getattr(np, rec.name), "jnp_op": getattr(jnp, rec.name),
"axis": axis, "keepdims": keepdims, "inexact": rec.inexact}
for shape in rec.shapes if np.prod(shape) == 0
for dtype in rec.dtypes
for keepdims in [False, True]
for axis in range(-len(shape), len(shape)) if shape[axis] >= 1)
for rec in JAX_REDUCER_INITIAL_RECORDS))
def testReducerNoInitialZeroDims(self, np_op, jnp_op, rng_factory, shape, dtype, axis,
keepdims, inexact):
rng = rng_factory(self.rng())
is_bf16_nan_test = dtype == jnp.bfloat16 and rng_factory.__name__ == 'rand_some_nan'
@jtu.ignore_warning(category=RuntimeWarning,
message="Degrees of freedom <= 0 for slice.*")
@jtu.ignore_warning(category=np.ComplexWarning)
def np_fun(x):
x = np.asarray(x)
if inexact:
x = x.astype(dtypes._to_inexact_dtype(x.dtype))
x_cast = x if not is_bf16_nan_test else x.astype(np.float32)
res = np_op(x_cast, axis, keepdims=keepdims)
res = res if not is_bf16_nan_test else res.astype(jnp.bfloat16)
return res
jnp_fun = lambda x: jnp_op(x, axis, keepdims=keepdims)
jnp_fun = jtu.ignore_warning(category=jnp.ComplexWarning)(jnp_fun)
args_maker = lambda: [rng(shape, dtype)]
tol = {jnp.bfloat16: 3E-2}
self._CheckAgainstNumpy(np_fun, jnp_fun, args_maker, rtol=tol)
self._CompileAndCheck(jnp_fun, args_maker)
@parameterized.named_parameters(itertools.chain.from_iterable(
jtu.cases_from_list(
{"testcase_name": "{}_inshape={}_axis={}_keepdims={}_initial={}_whereshape={}".format(
rec.test_name.capitalize(),
jtu.format_shape_dtype_string(shape, dtype), axis, keepdims, initial,
jtu.format_shape_dtype_string(whereshape, bool)),
"rng_factory": rec.rng_factory, "shape": shape, "dtype": dtype,
"np_op": getattr(np, rec.name), "jnp_op": getattr(jnp, rec.name), "whereshape": whereshape,
"initial": initial, "axis": axis, "keepdims": keepdims, "inexact": rec.inexact}
for shape in rec.shapes for dtype in rec.dtypes
for whereshape in _compatible_shapes(shape)
for axis in list(range(-len(shape), len(shape))) + [None]
for initial in [0, 1] for keepdims in [False, True]
if jtu.is_valid_shape(shape, dtype))
for rec in JAX_REDUCER_INITIAL_RECORDS))
def testReducerWhere(self, np_op, jnp_op, rng_factory, shape, dtype, axis,
keepdims, initial, inexact, whereshape):
if (shape in [()] + scalar_shapes and
dtype in [jnp.int16, jnp.uint16] and
jnp_op in [jnp.min, jnp.max]):
self.skipTest("Known XLA failure; see https://github.com/google/jax/issues/4971.")
rng = rng_factory(self.rng())
is_bf16_nan_test = dtype == jnp.bfloat16 and rng_factory.__name__ == 'rand_some_nan'
# Do not pass where via args_maker as that is incompatible with _promote_like_jnp.
where = jtu.rand_bool(self.rng())(whereshape, np.bool_)
@jtu.ignore_warning(category=RuntimeWarning,
message="Degrees of freedom <= 0 for slice.*")
@jtu.ignore_warning(category=np.ComplexWarning)
def np_fun(x):