File size: 58,747 Bytes
e882f51
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
1594
1595
1596
1597
1598
1599
1600
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1612
1613
1614
1615
1616
1617
1618
1619
1620
1621
1622
1623
1624
1625
1626
import matplotlib
from torch.nn import DataParallel
from torch.nn.parallel import DistributedDataParallel

matplotlib.use('Agg')
import glob
import itertools
import subprocess
import threading
import traceback

from pytorch_lightning.callbacks import GradientAccumulationScheduler
from pytorch_lightning.callbacks import ModelCheckpoint

from functools import wraps
from torch.cuda._utils import _get_device_index
import numpy as np
import torch.optim
import torch.utils.data
import copy
import logging
import os
import re
import sys
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
import tqdm
from torch.optim.optimizer import Optimizer


def get_a_var(obj):  # pragma: no cover
    if isinstance(obj, torch.Tensor):
        return obj

    if isinstance(obj, list) or isinstance(obj, tuple):
        for result in map(get_a_var, obj):
            if isinstance(result, torch.Tensor):
                return result
    if isinstance(obj, dict):
        for result in map(get_a_var, obj.items()):
            if isinstance(result, torch.Tensor):
                return result
    return None


def data_loader(fn):
    """
    Decorator to make any fx with this use the lazy property
    :param fn:
    :return:
    """

    wraps(fn)
    attr_name = '_lazy_' + fn.__name__

    def _get_data_loader(self):
        try:
            value = getattr(self, attr_name)
        except AttributeError:
            try:
                value = fn(self)  # Lazy evaluation, done only once.
                if (
                        value is not None and
                        not isinstance(value, list) and
                        fn.__name__ in ['test_dataloader', 'val_dataloader']
                ):
                    value = [value]
            except AttributeError as e:
                # Guard against AttributeError suppression. (Issue #142)
                traceback.print_exc()
                error = f'{fn.__name__}: An AttributeError was encountered: ' + str(e)
                raise RuntimeError(error) from e
            setattr(self, attr_name, value)  # Memoize evaluation.
        return value

    return _get_data_loader


def parallel_apply(modules, inputs, kwargs_tup=None, devices=None):  # pragma: no cover
    r"""Applies each `module` in :attr:`modules` in parallel on arguments
    contained in :attr:`inputs` (positional) and :attr:`kwargs_tup` (keyword)
    on each of :attr:`devices`.

    Args:
        modules (Module): modules to be parallelized
        inputs (tensor): inputs to the modules
        devices (list of int or torch.device): CUDA devices

    :attr:`modules`, :attr:`inputs`, :attr:`kwargs_tup` (if given), and
    :attr:`devices` (if given) should all have same length. Moreover, each
    element of :attr:`inputs` can either be a single object as the only argument
    to a module, or a collection of positional arguments.
    """
    assert len(modules) == len(inputs)
    if kwargs_tup is not None:
        assert len(modules) == len(kwargs_tup)
    else:
        kwargs_tup = ({},) * len(modules)
    if devices is not None:
        assert len(modules) == len(devices)
    else:
        devices = [None] * len(modules)
    devices = list(map(lambda x: _get_device_index(x, True), devices))
    lock = threading.Lock()
    results = {}
    grad_enabled = torch.is_grad_enabled()

    def _worker(i, module, input, kwargs, device=None):
        torch.set_grad_enabled(grad_enabled)
        if device is None:
            device = get_a_var(input).get_device()
        try:
            with torch.cuda.device(device):
                # this also avoids accidental slicing of `input` if it is a Tensor
                if not isinstance(input, (list, tuple)):
                    input = (input,)

                # ---------------
                # CHANGE
                if module.training:
                    output = module.training_step(*input, **kwargs)

                elif module.testing:
                    output = module.test_step(*input, **kwargs)

                else:
                    output = module.validation_step(*input, **kwargs)
                # ---------------

            with lock:
                results[i] = output
        except Exception as e:
            with lock:
                results[i] = e

    # make sure each module knows what training state it's in...
    # fixes weird bug where copies are out of sync
    root_m = modules[0]
    for m in modules[1:]:
        m.training = root_m.training
        m.testing = root_m.testing

    if len(modules) > 1:
        threads = [threading.Thread(target=_worker,
                                    args=(i, module, input, kwargs, device))
                   for i, (module, input, kwargs, device) in
                   enumerate(zip(modules, inputs, kwargs_tup, devices))]

        for thread in threads:
            thread.start()
        for thread in threads:
            thread.join()
    else:
        _worker(0, modules[0], inputs[0], kwargs_tup[0], devices[0])

    outputs = []
    for i in range(len(inputs)):
        output = results[i]
        if isinstance(output, Exception):
            raise output
        outputs.append(output)
    return outputs


def _find_tensors(obj):  # pragma: no cover
    r"""
    Recursively find all tensors contained in the specified object.
    """
    if isinstance(obj, torch.Tensor):
        return [obj]
    if isinstance(obj, (list, tuple)):
        return itertools.chain(*map(_find_tensors, obj))
    if isinstance(obj, dict):
        return itertools.chain(*map(_find_tensors, obj.values()))
    return []


class DDP(DistributedDataParallel):
    """
    Override the forward call in lightning so it goes to training and validation step respectively
    """

    def parallel_apply(self, replicas, inputs, kwargs):
        return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])

    def forward(self, *inputs, **kwargs):  # pragma: no cover
        self._sync_params()
        if self.device_ids:
            inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)
            if len(self.device_ids) == 1:
                # --------------
                # LIGHTNING MOD
                # --------------
                # normal
                # output = self.module(*inputs[0], **kwargs[0])
                # lightning
                if self.module.training:
                    output = self.module.training_step(*inputs[0], **kwargs[0])
                elif self.module.testing:
                    output = self.module.test_step(*inputs[0], **kwargs[0])
                else:
                    output = self.module.validation_step(*inputs[0], **kwargs[0])
            else:
                outputs = self.parallel_apply(self._module_copies[:len(inputs)], inputs, kwargs)
                output = self.gather(outputs, self.output_device)
        else:
            # normal
            output = self.module(*inputs, **kwargs)

        if torch.is_grad_enabled():
            # We'll return the output object verbatim since it is a freeform
            # object. We need to find any tensors in this object, though,
            # because we need to figure out which parameters were used during
            # this forward pass, to ensure we short circuit reduction for any
            # unused parameters. Only if `find_unused_parameters` is set.
            if self.find_unused_parameters:
                self.reducer.prepare_for_backward(list(_find_tensors(output)))
            else:
                self.reducer.prepare_for_backward([])
        return output


class DP(DataParallel):
    """
    Override the forward call in lightning so it goes to training and validation step respectively
    """

    def forward(self, *inputs, **kwargs):
        if not self.device_ids:
            return self.module(*inputs, **kwargs)

        for t in itertools.chain(self.module.parameters(), self.module.buffers()):
            if t.device != self.src_device_obj:
                raise RuntimeError("module must have its parameters and buffers "
                                   "on device {} (device_ids[0]) but found one of "
                                   "them on device: {}".format(self.src_device_obj, t.device))

        inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)
        if len(self.device_ids) == 1:
            # lightning
            if self.module.training:
                return self.module.training_step(*inputs[0], **kwargs[0])
            elif self.module.testing:
                return self.module.test_step(*inputs[0], **kwargs[0])
            else:
                return self.module.validation_step(*inputs[0], **kwargs[0])

        replicas = self.replicate(self.module, self.device_ids[:len(inputs)])
        outputs = self.parallel_apply(replicas, inputs, kwargs)
        return self.gather(outputs, self.output_device)

    def parallel_apply(self, replicas, inputs, kwargs):
        return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])


class GradientAccumulationScheduler:
    def __init__(self, scheduling: dict):
        if scheduling == {}:  # empty dict error
            raise TypeError("Empty dict cannot be interpreted correct")

        for key in scheduling.keys():
            if not isinstance(key, int) or not isinstance(scheduling[key], int):
                raise TypeError("All epoches and accumulation factor must be integers")

        minimal_epoch = min(scheduling.keys())
        if minimal_epoch < 1:
            msg = f"Epochs indexing from 1, epoch {minimal_epoch} cannot be interpreted correct"
            raise IndexError(msg)
        elif minimal_epoch != 1:  # if user didnt define first epoch accumulation factor
            scheduling.update({1: 1})

        self.scheduling = scheduling
        self.epochs = sorted(scheduling.keys())

    def on_epoch_begin(self, epoch, trainer):
        epoch += 1  # indexing epochs from 1
        for i in reversed(range(len(self.epochs))):
            if epoch >= self.epochs[i]:
                trainer.accumulate_grad_batches = self.scheduling.get(self.epochs[i])
                break


class LatestModelCheckpoint(ModelCheckpoint):
    def __init__(self, filepath, monitor='val_loss', verbose=0, num_ckpt_keep=5,
                 save_weights_only=False, mode='auto', period=1, prefix='model', save_best=True):
        super(ModelCheckpoint, self).__init__()
        self.monitor = monitor
        self.verbose = verbose
        self.filepath = filepath
        os.makedirs(filepath, exist_ok=True)
        self.num_ckpt_keep = num_ckpt_keep
        self.save_best = save_best
        self.save_weights_only = save_weights_only
        self.period = period
        self.epochs_since_last_check = 0
        self.prefix = prefix
        self.best_k_models = {}
        # {filename: monitor}
        self.kth_best_model = ''
        self.save_top_k = 1
        self.task = None
        if mode == 'min':
            self.monitor_op = np.less
            self.best = np.Inf
            self.mode = 'min'
        elif mode == 'max':
            self.monitor_op = np.greater
            self.best = -np.Inf
            self.mode = 'max'
        else:
            if 'acc' in self.monitor or self.monitor.startswith('fmeasure'):
                self.monitor_op = np.greater
                self.best = -np.Inf
                self.mode = 'max'
            else:
                self.monitor_op = np.less
                self.best = np.Inf
                self.mode = 'min'
        if os.path.exists(f'{self.filepath}/best_valid.npy'):
            self.best = np.load(f'{self.filepath}/best_valid.npy')[0]

    def get_all_ckpts(self):
        return sorted(glob.glob(f'{self.filepath}/{self.prefix}_ckpt_steps_*.ckpt'),
                      key=lambda x: -int(re.findall('.*steps\_(\d+)\.ckpt', x)[0]))

    def on_epoch_end(self, epoch, logs=None):
        logs = logs or {}
        self.epochs_since_last_check += 1
        best_filepath = f'{self.filepath}/{self.prefix}_ckpt_best.pt'
        if self.epochs_since_last_check >= self.period:
            self.epochs_since_last_check = 0
            filepath = f'{self.filepath}/{self.prefix}_ckpt_steps_{self.task.global_step}.ckpt'
            if self.verbose > 0:
                logging.info(f'Epoch {epoch:05d}@{self.task.global_step}: saving model to {filepath}')
            self._save_model(filepath)
            for old_ckpt in self.get_all_ckpts()[self.num_ckpt_keep:]:
                # TODO: test filesystem calls
                os.remove(old_ckpt)
                # subprocess.check_call(f'del "{old_ckpt}"', shell=True)
                if self.verbose > 0:
                    logging.info(f'Delete ckpt: {os.path.basename(old_ckpt)}')
            current = logs.get(self.monitor)
            if current is not None and self.save_best:
                if self.monitor_op(current, self.best):
                    self.best = current
                    if self.verbose > 0:
                        logging.info(
                            f'Epoch {epoch:05d}@{self.task.global_step}: {self.monitor} reached'
                            f' {current:0.5f} (best {self.best:0.5f}), saving model to'
                            f' {best_filepath} as top 1')
                    self._save_model(best_filepath)
                    np.save(f'{self.filepath}/best_valid.npy', [self.best])
    
    def _save_model(self,path):
        return self.save_function(path)



class BaseTrainer:
    def __init__(
            self,
            logger=True,
            checkpoint_callback=True,
            default_save_path=None,
            gradient_clip_val=0,
            process_position=0,
            gpus=-1,
            log_gpu_memory=None,
            show_progress_bar=True,
            track_grad_norm=-1,
            check_val_every_n_epoch=1,
            accumulate_grad_batches=1,
            max_updates=1000,
            min_epochs=1,
            val_check_interval=1.0,
            log_save_interval=100,
            row_log_interval=10,
            print_nan_grads=False,
            weights_summary='full',
            num_sanity_val_steps=5,
            resume_from_checkpoint=None,
    ):
        self.log_gpu_memory = log_gpu_memory
        self.gradient_clip_val = gradient_clip_val
        self.check_val_every_n_epoch = check_val_every_n_epoch
        self.track_grad_norm = track_grad_norm
        self.on_gpu = True if (gpus and torch.cuda.is_available()) else False
        self.process_position = process_position
        self.weights_summary = weights_summary
        self.max_updates = max_updates
        self.min_epochs = min_epochs
        self.num_sanity_val_steps = num_sanity_val_steps
        self.print_nan_grads = print_nan_grads
        self.resume_from_checkpoint = resume_from_checkpoint
        self.default_save_path = default_save_path

        # training bookeeping
        self.total_batch_idx = 0
        self.running_loss = []
        self.avg_loss = 0
        self.batch_idx = 0
        self.tqdm_metrics = {}
        self.callback_metrics = {}
        self.num_val_batches = 0
        self.num_training_batches = 0
        self.num_test_batches = 0
        self.get_train_dataloader = None
        self.get_test_dataloaders = None
        self.get_val_dataloaders = None
        self.is_iterable_train_dataloader = False

        # training state
        self.model = None
        self.testing = False
        self.disable_validation = False
        self.lr_schedulers = []
        self.optimizers = None
        self.global_step = 0
        self.current_epoch = 0
        self.total_batches = 0

        # configure checkpoint callback
        self.checkpoint_callback = checkpoint_callback
        self.checkpoint_callback.save_function = self.save_checkpoint
        self.weights_save_path = self.checkpoint_callback.filepath

        # accumulated grads
        self.configure_accumulated_gradients(accumulate_grad_batches)

        # allow int, string and gpu list
        self.data_parallel_device_ids = [
            int(x) for x in os.environ.get("CUDA_VISIBLE_DEVICES", "").split(",") if x != '']
        if len(self.data_parallel_device_ids) == 0:
            self.root_gpu = None
            self.on_gpu = False
        else:
            self.root_gpu = self.data_parallel_device_ids[0]
            self.on_gpu = True

        # distributed backend choice
        self.use_ddp = False
        self.use_dp = False
        self.single_gpu = False
        self.distributed_backend = 'ddp' if self.num_gpus > 0 else 'dp'
        self.set_distributed_mode(self.distributed_backend)

        self.proc_rank = 0
        self.world_size = 1
        self.node_rank = 0

        # can't init progress bar here because starting a new process
        # means the progress_bar won't survive pickling
        self.show_progress_bar = show_progress_bar

        # logging
        self.log_save_interval = log_save_interval
        self.val_check_interval = val_check_interval
        self.logger = logger
        self.logger.rank = 0
        self.row_log_interval = row_log_interval

    @property
    def num_gpus(self):
        gpus = self.data_parallel_device_ids
        if gpus is None:
            return 0
        else:
            return len(gpus)

    @property
    def data_parallel(self):
        return self.use_dp or self.use_ddp

    def get_model(self):
        is_dp_module = isinstance(self.model, (DDP, DP))
        model = self.model.module if is_dp_module else self.model
        return model

    # -----------------------------
    # MODEL TRAINING
    # -----------------------------
    def fit(self, model):
        if self.use_ddp:
            mp.spawn(self.ddp_train, nprocs=self.num_gpus, args=(model,))
        else:
            model.svc_model = model.build_model()
            if not self.testing:
                self.optimizers, self.lr_schedulers = self.init_optimizers(model.configure_optimizers())
            if self.use_dp:
                model.cuda(self.root_gpu)
                model = DP(model, device_ids=self.data_parallel_device_ids)
            elif self.single_gpu:
                model.cuda(self.root_gpu)
            self.run_pretrain_routine(model)
        return 1

    def init_optimizers(self, optimizers):

        # single optimizer
        if isinstance(optimizers, Optimizer):
            return [optimizers], []

        # two lists
        elif len(optimizers) == 2 and isinstance(optimizers[0], list):
            optimizers, lr_schedulers = optimizers
            return optimizers, lr_schedulers

        # single list or tuple
        elif isinstance(optimizers, list) or isinstance(optimizers, tuple):
            return optimizers, []

    def run_pretrain_routine(self, model):
        """Sanity check a few things before starting actual training.

        :param model:
        """
        ref_model = model
        if self.data_parallel:
            ref_model = model.module

        # give model convenience properties
        ref_model.trainer = self

        # set local properties on the model
        self.copy_trainer_model_properties(ref_model)

        # link up experiment object
        if self.logger is not None:
            ref_model.logger = self.logger
            self.logger.save()

        if self.use_ddp:
            dist.barrier()

        # set up checkpoint callback
        # self.configure_checkpoint_callback()

        # transfer data loaders from model
        self.get_dataloaders(ref_model)

        # track model now.
        # if cluster resets state, the model will update with the saved weights
        self.model = model

        # restore training and model before hpc call
        self.restore_weights(model)

        # when testing requested only run test and return
        if self.testing:
            self.run_evaluation(test=True)
            return

        # check if we should run validation during training
        self.disable_validation = self.num_val_batches == 0

        # run tiny validation (if validation defined)
        # to make sure program won't crash during val
        ref_model.on_sanity_check_start()
        ref_model.on_train_start()
        if not self.disable_validation and self.num_sanity_val_steps > 0:
            # init progress bars for validation sanity check
            pbar = tqdm.tqdm(desc='Validation sanity check',
                             total=self.num_sanity_val_steps * len(self.get_val_dataloaders()),
                             leave=False, position=2 * self.process_position,
                             disable=not self.show_progress_bar, dynamic_ncols=True, unit='batch')
            self.main_progress_bar = pbar
            # dummy validation progress bar
            self.val_progress_bar = tqdm.tqdm(disable=True)

            self.evaluate(model, self.get_val_dataloaders(), self.num_sanity_val_steps, self.testing)

            # close progress bars
            self.main_progress_bar.close()
            self.val_progress_bar.close()

        # init progress bar
        pbar = tqdm.tqdm(leave=True, position=2 * self.process_position,
                         disable=not self.show_progress_bar, dynamic_ncols=True, unit='batch',
                         file=sys.stdout)
        self.main_progress_bar = pbar

        # clear cache before training
        if self.on_gpu:
            torch.cuda.empty_cache()

        # CORE TRAINING LOOP
        self.train()

    def test(self, model):
        self.testing = True
        self.fit(model)

    @property
    def training_tqdm_dict(self):
        tqdm_dict = {
            'step': '{}'.format(self.global_step),
        }
        tqdm_dict.update(self.tqdm_metrics)
        return tqdm_dict

    # --------------------
    # restore ckpt
    # --------------------
    def restore_weights(self, model):
        """
        To restore weights we have two cases.
        First, attempt to restore hpc weights. If successful, don't restore
        other weights.

        Otherwise, try to restore actual weights
        :param model:
        :return:
        """
        # clear cache before restore
        if self.on_gpu:
            torch.cuda.empty_cache()

        if self.resume_from_checkpoint is not None:
            self.restore(self.resume_from_checkpoint, on_gpu=self.on_gpu)
        else:
            # restore weights if same exp version
            self.restore_state_if_checkpoint_exists(model)

        # wait for all models to restore weights
        if self.use_ddp:
            # wait for all processes to catch up
            dist.barrier()

        # clear cache after restore
        if self.on_gpu:
            torch.cuda.empty_cache()

    def restore_state_if_checkpoint_exists(self, model):
        did_restore = False

        # do nothing if there's not dir or callback
        no_ckpt_callback = (self.checkpoint_callback is None) or (not self.checkpoint_callback)
        if no_ckpt_callback or not os.path.exists(self.checkpoint_callback.filepath):
            return did_restore

        # restore trainer state and model if there is a weight for this experiment
        last_steps = -1
        last_ckpt_name = None

        # find last epoch
        checkpoints = os.listdir(self.checkpoint_callback.filepath)
        for name in checkpoints:
            if '.ckpt' in name and not name.endswith('part'):
                if 'steps_' in name:
                    steps = name.split('steps_')[1]
                    steps = int(re.sub('[^0-9]', '', steps))

                    if steps > last_steps:
                        last_steps = steps
                        last_ckpt_name = name

        # restore last checkpoint
        if last_ckpt_name is not None:
            last_ckpt_path = os.path.join(self.checkpoint_callback.filepath, last_ckpt_name)
            self.restore(last_ckpt_path, self.on_gpu)
            logging.info(f'model and trainer restored from checkpoint: {last_ckpt_path}')
            did_restore = True

        return did_restore

    def restore(self, checkpoint_path, on_gpu):
        checkpoint = torch.load(checkpoint_path, map_location='cpu')

        # load model state
        model = self.get_model()

        # load the state_dict on the model automatically
        model.load_state_dict(checkpoint['state_dict'], strict=False)
        if on_gpu:
            model.cuda(self.root_gpu)
        # load training state (affects trainer only)
        self.restore_training_state(checkpoint)
        model.global_step = self.global_step
        del checkpoint

        try:
            if dist.is_initialized() and dist.get_rank() > 0:
                return
        except Exception as e:
            print(e)
            return

    def restore_training_state(self, checkpoint):
        """
        Restore trainer state.
        Model will get its change to update
        :param checkpoint:
        :return:
        """
        if self.checkpoint_callback is not None and self.checkpoint_callback is not False:
            # return allowing checkpoints with meta information (global_step, etc)
            self.checkpoint_callback.best = checkpoint['checkpoint_callback_best']

        self.global_step = checkpoint['global_step']
        self.current_epoch = checkpoint['epoch']

        if self.testing:
            return

        # restore the optimizers
        optimizer_states = checkpoint['optimizer_states']
        for optimizer, opt_state in zip(self.optimizers, optimizer_states):
            if optimizer is None:
                return
            optimizer.load_state_dict(opt_state)

            # move optimizer to GPU 1 weight at a time
            # avoids OOM
            if self.root_gpu is not None:
                for state in optimizer.state.values():
                    for k, v in state.items():
                        if isinstance(v, torch.Tensor):
                            state[k] = v.cuda(self.root_gpu)

        # restore the lr schedulers
        lr_schedulers = checkpoint['lr_schedulers']
        for scheduler, lrs_state in zip(self.lr_schedulers, lr_schedulers):
            scheduler.load_state_dict(lrs_state)

    # --------------------
    # MODEL SAVE CHECKPOINT
    # --------------------
    def _atomic_save(self, checkpoint, filepath):
        """Saves a checkpoint atomically, avoiding the creation of incomplete checkpoints.

        This will create a temporary checkpoint with a suffix of ``.part``, then copy it to the final location once
        saving is finished.

        Args:
            checkpoint (object): The object to save.
                Built to be used with the ``dump_checkpoint`` method, but can deal with anything which ``torch.save``
                accepts.
            filepath (str|pathlib.Path): The path to which the checkpoint will be saved.
                This points to the file that the checkpoint will be stored in.
        """
        tmp_path = str(filepath) + ".part"
        torch.save(checkpoint, tmp_path)
        os.replace(tmp_path, filepath)

    def save_checkpoint(self, filepath):
        checkpoint = self.dump_checkpoint()
        self._atomic_save(checkpoint, filepath)

    def dump_checkpoint(self):

        checkpoint = {
            'epoch': self.current_epoch,
            'global_step': self.global_step
        }

        if self.checkpoint_callback is not None and self.checkpoint_callback is not False:
            checkpoint['checkpoint_callback_best'] = self.checkpoint_callback.best

        # save optimizers
        optimizer_states = []
        for i, optimizer in enumerate(self.optimizers):
            if optimizer is not None:
                optimizer_states.append(optimizer.state_dict())

        checkpoint['optimizer_states'] = optimizer_states

        # save lr schedulers
        lr_schedulers = []
        for i, scheduler in enumerate(self.lr_schedulers):
            lr_schedulers.append(scheduler.state_dict())

        checkpoint['lr_schedulers'] = lr_schedulers

        # add the hparams and state_dict from the model
        model = self.get_model()
        checkpoint['state_dict'] = model.state_dict()
        # give the model a chance to add a few things
        model.on_save_checkpoint(checkpoint)

        return checkpoint

    def copy_trainer_model_properties(self, model):
        if isinstance(model, DP):
            ref_model = model.module
        elif isinstance(model, DDP):
            ref_model = model.module
        else:
            ref_model = model

        for m in [model, ref_model]:
            m.trainer = self
            m.on_gpu = self.on_gpu
            m.use_dp = self.use_dp
            m.use_ddp = self.use_ddp
            m.testing = self.testing
            m.single_gpu = self.single_gpu

    def transfer_batch_to_gpu(self, batch, gpu_id):
        # base case: object can be directly moved using `cuda` or `to`
        if callable(getattr(batch, 'cuda', None)):
            return batch.cuda(gpu_id, non_blocking=True)

        elif callable(getattr(batch, 'to', None)):
            return batch.to(torch.device('cuda', gpu_id), non_blocking=True)

        # when list
        elif isinstance(batch, list):
            for i, x in enumerate(batch):
                batch[i] = self.transfer_batch_to_gpu(x, gpu_id)
            return batch

        # when tuple
        elif isinstance(batch, tuple):
            batch = list(batch)
            for i, x in enumerate(batch):
                batch[i] = self.transfer_batch_to_gpu(x, gpu_id)
            return tuple(batch)

        # when dict
        elif isinstance(batch, dict):
            for k, v in batch.items():
                batch[k] = self.transfer_batch_to_gpu(v, gpu_id)

            return batch

        # nothing matches, return the value as is without transform
        return batch

    def set_distributed_mode(self, distributed_backend):
        # skip for CPU
        if self.num_gpus == 0:
            return

        # single GPU case
        # in single gpu case we allow ddp so we can train on multiple
        # nodes, 1 gpu per node
        elif self.num_gpus == 1:
            self.single_gpu = True
            self.use_dp = False
            self.use_ddp = False
            self.root_gpu = 0
            self.data_parallel_device_ids = [0]
        else:
            if distributed_backend is not None:
                self.use_dp = distributed_backend == 'dp'
                self.use_ddp = distributed_backend == 'ddp'
            elif distributed_backend is None:
                self.use_dp = True
                self.use_ddp = False

        logging.info(f'gpu available: {torch.cuda.is_available()}, used: {self.on_gpu}')

    def ddp_train(self, gpu_idx, model):
        """
        Entry point into a DP thread
        :param gpu_idx:
        :param model:
        :param cluster_obj:
        :return:
        """
        # otherwise default to node rank 0
        self.node_rank = 0

        # show progressbar only on progress_rank 0
        self.show_progress_bar = self.show_progress_bar and self.node_rank == 0 and gpu_idx == 0

        # determine which process we are and world size
        if self.use_ddp:
            self.proc_rank = self.node_rank * self.num_gpus + gpu_idx
            self.world_size = self.num_gpus

        # let the exp know the rank to avoid overwriting logs
        if self.logger is not None:
            self.logger.rank = self.proc_rank

        # set up server using proc 0's ip address
        # try to init for 20 times at max in case ports are taken
        # where to store ip_table
        model.trainer = self
        model.init_ddp_connection(self.proc_rank, self.world_size)

        # CHOOSE OPTIMIZER
        # allow for lr schedulers as well
        model.svc_model = model.build_model()
        if not self.testing:
            self.optimizers, self.lr_schedulers = self.init_optimizers(model.configure_optimizers())

        # MODEL
        # copy model to each gpu
        if self.distributed_backend == 'ddp':
            torch.cuda.set_device(gpu_idx)
        model.cuda(gpu_idx)

        # set model properties before going into wrapper
        self.copy_trainer_model_properties(model)

        # override root GPU
        self.root_gpu = gpu_idx

        if self.distributed_backend == 'ddp':
            device_ids = [gpu_idx]
        else:
            device_ids = None

        # allow user to configure ddp
        model = model.configure_ddp(model, device_ids)

        # continue training routine
        self.run_pretrain_routine(model)

    def resolve_root_node_address(self, root_node):
        if '[' in root_node:
            name = root_node.split('[')[0]
            number = root_node.split(',')[0]
            if '-' in number:
                number = number.split('-')[0]

            number = re.sub('[^0-9]', '', number)
            root_node = name + number

        return root_node

    def log_metrics(self, metrics, grad_norm_dic, step=None):
        """Logs the metric dict passed in.

        :param metrics:
        :param grad_norm_dic:
        """
        # added metrics by Lightning for convenience
        metrics['epoch'] = self.current_epoch

        # add norms
        metrics.update(grad_norm_dic)

        # turn all tensors to scalars
        scalar_metrics = self.metrics_to_scalars(metrics)

        step = step if step is not None else self.global_step
        # log actual metrics
        if self.proc_rank == 0 and self.logger is not None:
            self.logger.log_metrics(scalar_metrics, step=step)
            self.logger.save()

    def add_tqdm_metrics(self, metrics):
        for k, v in metrics.items():
            if type(v) is torch.Tensor:
                v = v.item()

            self.tqdm_metrics[k] = v

    def metrics_to_scalars(self, metrics):
        new_metrics = {}
        for k, v in metrics.items():
            if isinstance(v, torch.Tensor):
                v = v.item()

            if type(v) is dict:
                v = self.metrics_to_scalars(v)

            new_metrics[k] = v

        return new_metrics

    def process_output(self, output, train=False):
        """Reduces output according to the training mode.

        Separates loss from logging and tqdm metrics
        :param output:
        :return:
        """
        # ---------------
        # EXTRACT CALLBACK KEYS
        # ---------------
        # all keys not progress_bar or log are candidates for callbacks
        callback_metrics = {}
        for k, v in output.items():
            if k not in ['progress_bar', 'log', 'hiddens']:
                callback_metrics[k] = v

        if train and self.use_dp:
            num_gpus = self.num_gpus
            callback_metrics = self.reduce_distributed_output(callback_metrics, num_gpus)

        for k, v in callback_metrics.items():
            if isinstance(v, torch.Tensor):
                callback_metrics[k] = v.item()

        # ---------------
        # EXTRACT PROGRESS BAR KEYS
        # ---------------
        try:
            progress_output = output['progress_bar']

            # reduce progress metrics for tqdm when using dp
            if train and self.use_dp:
                num_gpus = self.num_gpus
                progress_output = self.reduce_distributed_output(progress_output, num_gpus)

            progress_bar_metrics = progress_output
        except Exception:
            progress_bar_metrics = {}

        # ---------------
        # EXTRACT LOGGING KEYS
        # ---------------
        # extract metrics to log to experiment
        try:
            log_output = output['log']

            # reduce progress metrics for tqdm when using dp
            if train and self.use_dp:
                num_gpus = self.num_gpus
                log_output = self.reduce_distributed_output(log_output, num_gpus)

            log_metrics = log_output
        except Exception:
            log_metrics = {}

        # ---------------
        # EXTRACT LOSS
        # ---------------
        # if output dict doesn't have the keyword loss
        # then assume the output=loss if scalar
        loss = None
        if train:
            try:
                loss = output['loss']
            except Exception:
                if type(output) is torch.Tensor:
                    loss = output
                else:
                    raise RuntimeError(
                        'No `loss` value in the dictionary returned from `model.training_step()`.'
                    )

            # when using dp need to reduce the loss
            if self.use_dp:
                loss = self.reduce_distributed_output(loss, self.num_gpus)

        # ---------------
        # EXTRACT HIDDEN
        # ---------------
        hiddens = output.get('hiddens')

        # use every metric passed in as a candidate for callback
        callback_metrics.update(progress_bar_metrics)
        callback_metrics.update(log_metrics)

        # convert tensors to numpy
        for k, v in callback_metrics.items():
            if isinstance(v, torch.Tensor):
                callback_metrics[k] = v.item()

        return loss, progress_bar_metrics, log_metrics, callback_metrics, hiddens

    def reduce_distributed_output(self, output, num_gpus):
        if num_gpus <= 1:
            return output

        # when using DP, we get one output per gpu
        # average outputs and return
        if type(output) is torch.Tensor:
            return output.mean()

        for k, v in output.items():
            # recurse on nested dics
            if isinstance(output[k], dict):
                output[k] = self.reduce_distributed_output(output[k], num_gpus)

            # do nothing when there's a scalar
            elif isinstance(output[k], torch.Tensor) and output[k].dim() == 0:
                pass

            # reduce only metrics that have the same number of gpus
            elif output[k].size(0) == num_gpus:
                reduced = torch.mean(output[k])
                output[k] = reduced
        return output

    def clip_gradients(self):
        if self.gradient_clip_val > 0:
            model = self.get_model()
            torch.nn.utils.clip_grad_norm_(model.parameters(), self.gradient_clip_val)

    def print_nan_gradients(self):
        model = self.get_model()
        for param in model.parameters():
            if (param.grad is not None) and torch.isnan(param.grad.float()).any():
                logging.info(param, param.grad)

    def configure_accumulated_gradients(self, accumulate_grad_batches):
        self.accumulate_grad_batches = None

        if isinstance(accumulate_grad_batches, dict):
            self.accumulation_scheduler = GradientAccumulationScheduler(accumulate_grad_batches)
        elif isinstance(accumulate_grad_batches, int):
            schedule = {1: accumulate_grad_batches}
            self.accumulation_scheduler = GradientAccumulationScheduler(schedule)
        else:
            raise TypeError("Gradient accumulation supports only int and dict types")

    def get_dataloaders(self, model):
        if not self.testing:
            self.init_train_dataloader(model)
            self.init_val_dataloader(model)
        else:
            self.init_test_dataloader(model)

        if self.use_ddp:
            dist.barrier()
            if not self.testing:
                self.get_train_dataloader()
                self.get_val_dataloaders()
            else:
                self.get_test_dataloaders()

    def init_train_dataloader(self, model):
        self.fisrt_epoch = True
        self.get_train_dataloader = model.train_dataloader
        if isinstance(self.get_train_dataloader(), torch.utils.data.DataLoader):
            self.num_training_batches = len(self.get_train_dataloader())
            self.num_training_batches = int(self.num_training_batches)
        else:
            self.num_training_batches = float('inf')
            self.is_iterable_train_dataloader = True
        if isinstance(self.val_check_interval, int):
            self.val_check_batch = self.val_check_interval
        else:
            self._percent_range_check('val_check_interval')
            self.val_check_batch = int(self.num_training_batches * self.val_check_interval)
            self.val_check_batch = max(1, self.val_check_batch)

    def init_val_dataloader(self, model):
        self.get_val_dataloaders = model.val_dataloader
        self.num_val_batches = 0
        if self.get_val_dataloaders() is not None:
            if isinstance(self.get_val_dataloaders()[0], torch.utils.data.DataLoader):
                self.num_val_batches = sum(len(dataloader) for dataloader in self.get_val_dataloaders())
                self.num_val_batches = int(self.num_val_batches)
            else:
                self.num_val_batches = float('inf')

    def init_test_dataloader(self, model):
        self.get_test_dataloaders = model.test_dataloader
        if self.get_test_dataloaders() is not None:
            if isinstance(self.get_test_dataloaders()[0], torch.utils.data.DataLoader):
                self.num_test_batches = sum(len(dataloader) for dataloader in self.get_test_dataloaders())
                self.num_test_batches = int(self.num_test_batches)
            else:
                self.num_test_batches = float('inf')

    def evaluate(self, model, dataloaders, max_batches, test=False):
        """Run evaluation code.

        :param model: PT model
        :param dataloaders: list of PT dataloaders
        :param max_batches: Scalar
        :param test: boolean
        :return:
        """
        # enable eval mode
        model.zero_grad()
        model.eval()

        # copy properties for forward overrides
        self.copy_trainer_model_properties(model)

        # disable gradients to save memory
        torch.set_grad_enabled(False)

        if test:
            self.get_model().test_start()
        # bookkeeping
        outputs = []

        # run training
        for dataloader_idx, dataloader in enumerate(dataloaders):
            dl_outputs = []
            for batch_idx, batch in enumerate(dataloader):

                if batch is None:  # pragma: no cover
                    continue

                # stop short when on fast_dev_run (sets max_batch=1)
                if batch_idx >= max_batches:
                    break

                # -----------------
                # RUN EVALUATION STEP
                # -----------------
                output = self.evaluation_forward(model,
                                                 batch,
                                                 batch_idx,
                                                 dataloader_idx,
                                                 test)

                # track outputs for collation
                dl_outputs.append(output)

                # batch done
                if test:
                    self.test_progress_bar.update(1)
                else:
                    self.val_progress_bar.update(1)
            outputs.append(dl_outputs)

        # with a single dataloader don't pass an array
        if len(dataloaders) == 1:
            outputs = outputs[0]

        # give model a chance to do something with the outputs (and method defined)
        model = self.get_model()
        if test:
            eval_results_ = model.test_end(outputs)
        else:
            eval_results_ = model.validation_end(outputs)
        eval_results = eval_results_

        # enable train mode again
        model.train()

        # enable gradients to save memory
        torch.set_grad_enabled(True)

        return eval_results

    def run_evaluation(self, test=False):
        # when testing make sure user defined a test step
        model = self.get_model()
        model.on_pre_performance_check()

        # select dataloaders
        if test:
            dataloaders = self.get_test_dataloaders()
            max_batches = self.num_test_batches
        else:
            # val
            dataloaders = self.get_val_dataloaders()
            max_batches = self.num_val_batches

        # init validation or test progress bar
        # main progress bar will already be closed when testing so initial position is free
        position = 2 * self.process_position + (not test)
        desc = 'Testing' if test else 'Validating'
        pbar = tqdm.tqdm(desc=desc, total=max_batches, leave=test, position=position,
                         disable=not self.show_progress_bar, dynamic_ncols=True,
                         unit='batch', file=sys.stdout)
        setattr(self, f'{"test" if test else "val"}_progress_bar', pbar)

        # run evaluation
        eval_results = self.evaluate(self.model,
                                     dataloaders,
                                     max_batches,
                                     test)
        if eval_results is not None:
            _, prog_bar_metrics, log_metrics, callback_metrics, _ = self.process_output(
                eval_results)

            # add metrics to prog bar
            self.add_tqdm_metrics(prog_bar_metrics)

            # log metrics
            self.log_metrics(log_metrics, {})

            # track metrics for callbacks
            self.callback_metrics.update(callback_metrics)

        # hook
        model.on_post_performance_check()

        # add model specific metrics
        tqdm_metrics = self.training_tqdm_dict
        if not test:
            self.main_progress_bar.set_postfix(**tqdm_metrics)

        # close progress bar
        if test:
            self.test_progress_bar.close()
        else:
            self.val_progress_bar.close()

        # model checkpointing
        if self.proc_rank == 0 and self.checkpoint_callback is not None and not test:
            self.checkpoint_callback.on_epoch_end(epoch=self.current_epoch,
                                                  logs=self.callback_metrics)

    def evaluation_forward(self, model, batch, batch_idx, dataloader_idx, test=False):
        # make dataloader_idx arg in validation_step optional
        args = [batch, batch_idx]
        # print(batch)
        if test and len(self.get_test_dataloaders()) > 1:
            args.append(dataloader_idx)

        elif not test and len(self.get_val_dataloaders()) > 1:
            args.append(dataloader_idx)

        # handle DP, DDP forward
        if self.use_ddp or self.use_dp:
            output = model(*args)
            return output

        # single GPU
        if self.single_gpu:
            # for single GPU put inputs on gpu manually
            root_gpu = 0
            if isinstance(self.data_parallel_device_ids, list):
                root_gpu = self.data_parallel_device_ids[0]
            batch = self.transfer_batch_to_gpu(batch, root_gpu)
            args[0] = batch

        # CPU
        if test:
            output = model.test_step(*args)
        else:
            output = model.validation_step(*args)

        return output

    def train(self):
        model = self.get_model()
        # run all epochs
        for epoch in range(self.current_epoch, 1000000):
            # set seed for distributed sampler (enables shuffling for each epoch)
            if self.use_ddp and hasattr(self.get_train_dataloader().sampler, 'set_epoch'):
                self.get_train_dataloader().sampler.set_epoch(epoch)

            # get model
            model = self.get_model()

            # update training progress in trainer and model
            model.current_epoch = epoch
            self.current_epoch = epoch

            total_val_batches = 0
            if not self.disable_validation:
                # val can be checked multiple times in epoch
                is_val_epoch = (self.current_epoch + 1) % self.check_val_every_n_epoch == 0
                val_checks_per_epoch = self.num_training_batches // self.val_check_batch
                val_checks_per_epoch = val_checks_per_epoch if is_val_epoch else 0
                total_val_batches = self.num_val_batches * val_checks_per_epoch

            # total batches includes multiple val checks
            self.total_batches = self.num_training_batches + total_val_batches
            self.batch_loss_value = 0  # accumulated grads

            if self.is_iterable_train_dataloader:
                # for iterable train loader, the progress bar never ends
                num_iterations = None
            else:
                num_iterations = self.total_batches

            # reset progress bar
            # .reset() doesn't work on disabled progress bar so we should check
            desc = f'Epoch {epoch + 1}' if not self.is_iterable_train_dataloader else ''
            self.main_progress_bar.set_description(desc)

            # changing gradient according accumulation_scheduler
            self.accumulation_scheduler.on_epoch_begin(epoch, self)

            # -----------------
            # RUN TNG EPOCH
            # -----------------
            self.run_training_epoch()

            # update LR schedulers
            if self.lr_schedulers is not None:
                for lr_scheduler in self.lr_schedulers:
                    lr_scheduler.step(epoch=self.current_epoch)

        self.main_progress_bar.close()

        model.on_train_end()

        if self.logger is not None:
            self.logger.finalize("success")

    def run_training_epoch(self):
        # before epoch hook
        if self.is_function_implemented('on_epoch_start'):
            model = self.get_model()
            model.on_epoch_start()

        # run epoch
        for batch_idx, batch in enumerate(self.get_train_dataloader()):
            # stop epoch if we limited the number of training batches
            if batch_idx >= self.num_training_batches:
                break

            self.batch_idx = batch_idx

            model = self.get_model()
            model.global_step = self.global_step

            # ---------------
            # RUN TRAIN STEP
            # ---------------
            output = self.run_training_batch(batch, batch_idx)
            batch_result, grad_norm_dic, batch_step_metrics = output

            # when returning -1 from train_step, we end epoch early
            early_stop_epoch = batch_result == -1

            # ---------------
            # RUN VAL STEP
            # ---------------
            should_check_val = (
                    not self.disable_validation and self.global_step % self.val_check_batch == 0 and not self.fisrt_epoch)
            self.fisrt_epoch = False

            if should_check_val:
                self.run_evaluation(test=self.testing)

            # when logs should be saved
            should_save_log = (batch_idx + 1) % self.log_save_interval == 0 or early_stop_epoch
            if should_save_log:
                if self.proc_rank == 0 and self.logger is not None:
                    self.logger.save()

            # when metrics should be logged
            should_log_metrics = batch_idx % self.row_log_interval == 0 or early_stop_epoch
            if should_log_metrics:
                # logs user requested information to logger
                self.log_metrics(batch_step_metrics, grad_norm_dic)

            self.global_step += 1
            self.total_batch_idx += 1

            # end epoch early
            # stop when the flag is changed or we've gone past the amount
            # requested in the batches
            if early_stop_epoch:
                break
            if self.global_step > self.max_updates:
                print("| Training end..")
                exit()

        # epoch end hook
        if self.is_function_implemented('on_epoch_end'):
            model = self.get_model()
            model.on_epoch_end()

    def run_training_batch(self, batch, batch_idx):
        # track grad norms
        grad_norm_dic = {}

        # track all metrics for callbacks
        all_callback_metrics = []

        # track metrics to log
        all_log_metrics = []

        if batch is None:
            return 0, grad_norm_dic, {}

        # hook
        if self.is_function_implemented('on_batch_start'):
            model_ref = self.get_model()
            response = model_ref.on_batch_start(batch)

            if response == -1:
                return -1, grad_norm_dic, {}

        splits = [batch]
        self.hiddens = None
        for split_idx, split_batch in enumerate(splits):
            self.split_idx = split_idx

            # call training_step once per optimizer
            for opt_idx, optimizer in enumerate(self.optimizers):
                if optimizer is None:
                    continue
                # make sure only the gradients of the current optimizer's paramaters are calculated
                # in the training step to prevent dangling gradients in multiple-optimizer setup.
                if len(self.optimizers) > 1:
                    for param in self.get_model().parameters():
                        param.requires_grad = False
                    for group in optimizer.param_groups:
                        for param in group['params']:
                            param.requires_grad = True

                # wrap the forward step in a closure so second order methods work
                def optimizer_closure():
                    # forward pass
                    output = self.training_forward(
                        split_batch, batch_idx, opt_idx, self.hiddens)

                    closure_loss = output[0]
                    progress_bar_metrics = output[1]
                    log_metrics = output[2]
                    callback_metrics = output[3]
                    self.hiddens = output[4]
                    if closure_loss is None:
                        return None

                    # accumulate loss
                    # (if accumulate_grad_batches = 1 no effect)
                    closure_loss = closure_loss / self.accumulate_grad_batches

                    # backward pass
                    model_ref = self.get_model()
                    if closure_loss.requires_grad:
                        model_ref.backward(closure_loss, optimizer)

                    # track metrics for callbacks
                    all_callback_metrics.append(callback_metrics)

                    # track progress bar metrics
                    self.add_tqdm_metrics(progress_bar_metrics)
                    all_log_metrics.append(log_metrics)

                    # insert after step hook
                    if self.is_function_implemented('on_after_backward'):
                        model_ref = self.get_model()
                        model_ref.on_after_backward()

                    return closure_loss

                # calculate loss
                loss = optimizer_closure()
                if loss is None:
                    continue

                # nan grads
                if self.print_nan_grads:
                    self.print_nan_gradients()

                # track total loss for logging (avoid mem leaks)
                self.batch_loss_value += loss.item()

                # gradient update with accumulated gradients
                if (self.batch_idx + 1) % self.accumulate_grad_batches == 0:

                    # track gradient norms when requested
                    if batch_idx % self.row_log_interval == 0:
                        if self.track_grad_norm > 0:
                            model = self.get_model()
                            grad_norm_dic = model.grad_norm(
                                self.track_grad_norm)

                    # clip gradients
                    self.clip_gradients()

                    # calls .step(), .zero_grad()
                    # override function to modify this behavior
                    model = self.get_model()
                    model.optimizer_step(self.current_epoch, batch_idx, optimizer, opt_idx)

                    # calculate running loss for display
                    self.running_loss.append(self.batch_loss_value)
                    self.batch_loss_value = 0
                    self.avg_loss = np.mean(self.running_loss[-100:])

        # activate batch end hook
        if self.is_function_implemented('on_batch_end'):
            model = self.get_model()
            model.on_batch_end()

        # update progress bar
        self.main_progress_bar.update(1)
        self.main_progress_bar.set_postfix(**self.training_tqdm_dict)

        # collapse all metrics into one dict
        all_log_metrics = {k: v for d in all_log_metrics for k, v in d.items()}

        # track all metrics for callbacks
        self.callback_metrics.update({k: v for d in all_callback_metrics for k, v in d.items()})

        return 0, grad_norm_dic, all_log_metrics

    def training_forward(self, batch, batch_idx, opt_idx, hiddens):
        """
        Handle forward for each training case (distributed, single gpu, etc...)
        :param batch:
        :param batch_idx:
        :return:
        """
        # ---------------
        # FORWARD
        # ---------------
        # enable not needing to add opt_idx to training_step
        args = [batch, batch_idx, opt_idx]

        # distributed forward
        if self.use_ddp or self.use_dp:
            output = self.model(*args)
        # single GPU forward
        elif self.single_gpu:
            gpu_id = 0
            if isinstance(self.data_parallel_device_ids, list):
                gpu_id = self.data_parallel_device_ids[0]
            batch = self.transfer_batch_to_gpu(copy.copy(batch), gpu_id)
            args[0] = batch
            output = self.model.training_step(*args)
        # CPU forward
        else:
            output = self.model.training_step(*args)

        # allow any mode to define training_end
        model_ref = self.get_model()
        output_ = model_ref.training_end(output)
        if output_ is not None:
            output = output_

        # format and reduce outputs accordingly
        output = self.process_output(output, train=True)

        return output

    # ---------------
    # Utils
    # ---------------
    def is_function_implemented(self, f_name):
        model = self.get_model()
        f_op = getattr(model, f_name, None)
        return callable(f_op)

    def _percent_range_check(self, name):
        value = getattr(self, name)
        msg = f"`{name}` must lie in the range [0.0, 1.0], but got {value:.3f}."
        if name == "val_check_interval":
            msg += " If you want to disable validation set `val_percent_check` to 0.0 instead."

        if not 0. <= value <= 1.:
            raise ValueError(msg)