File size: 55,563 Bytes
464f5bf
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
# Copyright (c) Alibaba Cloud.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

import copy
import importlib
import math
import pathlib
from typing import TYPE_CHECKING, Optional, Tuple, Union, Callable, List, Any, Generator

import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import warnings

from torch.nn import CrossEntropyLoss
from transformers import PreTrainedTokenizer, GenerationConfig, StoppingCriteriaList
from transformers.generation.logits_process import LogitsProcessorList

if TYPE_CHECKING:
    from transformers.generation.streamers import BaseStreamer
from transformers.generation.utils import GenerateOutput
from transformers.modeling_outputs import (
    BaseModelOutputWithPast,
    CausalLMOutputWithPast,
)
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging

try:
    from einops import rearrange
except ImportError:
    rearrange = None
from torch import nn

SUPPORT_CUDA = torch.cuda.is_available()
SUPPORT_BF16 = SUPPORT_CUDA and torch.cuda.is_bf16_supported()
SUPPORT_FP16 = SUPPORT_CUDA and torch.cuda.get_device_capability(0)[0] >= 7
SUPPORT_TORCH2 = hasattr(torch, '__version__') and int(torch.__version__.split(".")[0]) >= 2


from .configuration_qwen import QWenConfig
from .qwen_generation_utils import (
    HistoryType,
    make_context,
    decode_tokens,
    get_stop_words_ids,
    StopWordsLogitsProcessor,
)


logger = logging.get_logger(__name__)

_CHECKPOINT_FOR_DOC = "qwen"
_CONFIG_FOR_DOC = "QWenConfig"

QWen_PRETRAINED_MODEL_ARCHIVE_LIST = ["qwen-7b"]

_ERROR_BAD_CHAT_FORMAT = """\
We detect you are probably using the pretrained model (rather than chat model) for chatting, since the chat_format in generation_config is not "chatml".
If you are directly using the model downloaded from Huggingface, please make sure you are using our "Qwen/Qwen-7B-Chat" Huggingface model (rather than "Qwen/Qwen-7B") when you call model.chat().
我们检测到您可能在使用预训练模型(而非chat模型)进行多轮chat,因为您当前在generation_config指定的chat_format,并未设置为我们在对话中所支持的"chatml"格式。
如果您在直接使用我们从Huggingface提供的模型,请确保您在调用model.chat()时,使用的是"Qwen/Qwen-7B-Chat"模型(而非"Qwen/Qwen-7B"预训练模型)。
"""

_SENTINEL = object()
_ERROR_STREAM_IN_CHAT = """\
Pass argument `stream` to model.chat() is buggy, deprecated, and marked for removal. Please use model.chat_stream(...) instead of model.chat(..., stream=True).
向model.chat()传入参数stream的用法可能存在Bug,该用法已被废弃,将在未来被移除。请使用model.chat_stream(...)代替model.chat(..., stream=True)。
"""

_ERROR_INPUT_CPU_QUERY_WITH_FLASH_ATTN_ACTIVATED = """\
We detect you have activated flash attention support, but running model computation on CPU. Please make sure that your input data has been placed on GPU. If you actually want to run CPU computation, please following the readme and set device_map="cpu" to disable flash attention when loading the model (calling AutoModelForCausalLM.from_pretrained).
检测到您的模型已激活了flash attention支持,但正在执行CPU运算任务。如使用flash attention,请您确认模型输入已经传到GPU上。如果您确认要执行CPU运算,请您在载入模型(调用AutoModelForCausalLM.from_pretrained)时,按照readme说法,指定device_map="cpu"以禁用flash attention。
"""

apply_rotary_emb_func = None
rms_norm = None
flash_attn_unpadded_func = None
flash_attn_func = None

def _import_flash_attn():
    global apply_rotary_emb_func, rms_norm, flash_attn_unpadded_func, flash_attn_func
    try:
        from flash_attn.layers.rotary import apply_rotary_emb_func as __apply_rotary_emb_func
        apply_rotary_emb_func = __apply_rotary_emb_func
    except ImportError:
        logger.warn(
            "Warning: import flash_attn rotary fail, please install FlashAttention rotary to get higher efficiency "
            "https://github.com/Dao-AILab/flash-attention/tree/main/csrc/rotary"
        )

    try:
        from flash_attn.ops.rms_norm import rms_norm as __rms_norm
        rms_norm = __rms_norm
    except ImportError:
        logger.warn(
            "Warning: import flash_attn rms_norm fail, please install FlashAttention layer_norm to get higher efficiency "
            "https://github.com/Dao-AILab/flash-attention/tree/main/csrc/layer_norm"
        )

    try:
        import flash_attn
        _flash_attn_func = None
        if not hasattr(flash_attn, '__version__'):
            from flash_attn.flash_attn_interface import flash_attn_unpadded_func as __flash_attn_unpadded_func
        else:
            if int(flash_attn.__version__.split(".")[0]) >= 2:
                if int(flash_attn.__version__.split(".")[1]) >= 1:
                    from flash_attn.flash_attn_interface import flash_attn_func as _flash_attn_func
                from flash_attn.flash_attn_interface import flash_attn_varlen_func as __flash_attn_unpadded_func
            else:
                from flash_attn.flash_attn_interface import flash_attn_unpadded_func as __flash_attn_unpadded_func
        flash_attn_unpadded_func = __flash_attn_unpadded_func
        flash_attn_func = _flash_attn_func
    except ImportError:
        logger.warn(
            "Warning: import flash_attn fail, please install FlashAttention to get higher efficiency "
            "https://github.com/Dao-AILab/flash-attention"
        )

def quantize_cache_v(fdata, bits, qmax, qmin):
    # b, s, head, h-dim->b, head, s, h-dim
    qtype = torch.uint8
    device = fdata.device
    shape = fdata.shape

    fdata_cal = torch.flatten(fdata, 2)
    fmax = torch.amax(fdata_cal, dim=-1, keepdim=True)
    fmin = torch.amin(fdata_cal, dim=-1, keepdim=True)
    # Compute params
    if qmax.device != fmax.device:
        qmax = qmax.to(device)
        qmin = qmin.to(device)
    scale = (fmax - fmin) / (qmax - qmin)
    zero = qmin - fmin / scale
    scale = scale.unsqueeze(-1).repeat(1,1,shape[2],1).contiguous()
    zero = zero.unsqueeze(-1).repeat(1,1,shape[2],1).contiguous()
    # Quantize
    res_data = fdata / scale + zero
    qdata = torch.clamp(res_data, qmin, qmax).to(qtype)
    return qdata.contiguous(), scale, zero

def dequantize_cache_torch(qdata, scale, zero):
    data = scale * (qdata - zero)
    return data

class FlashSelfAttention(torch.nn.Module):
    def __init__(
        self,
        causal=False,
        softmax_scale=None,
        attention_dropout=0.0,
    ):
        super().__init__()
        assert flash_attn_unpadded_func is not None, (
            "Please install FlashAttention first, " "e.g., with pip install flash-attn"
        )
        assert (
            rearrange is not None
        ), "Please install einops first, e.g., with pip install einops"
        self.causal = causal
        self.softmax_scale = softmax_scale
        self.dropout_p = attention_dropout

    def unpad_input(self, hidden_states, attention_mask):
        valid_mask = attention_mask.squeeze(1).squeeze(1).eq(0)
        seqlens_in_batch = valid_mask.sum(dim=-1, dtype=torch.int32)
        indices = torch.nonzero(valid_mask.flatten(), as_tuple=False).flatten()
        max_seqlen_in_batch = seqlens_in_batch.max().item()
        cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
        hidden_states = hidden_states[indices]
        return hidden_states, indices, cu_seqlens, max_seqlen_in_batch

    def pad_input(self, hidden_states, indices, batch, seqlen):
        output = torch.zeros(batch * seqlen, *hidden_states.shape[1:], device=hidden_states.device,
                             dtype=hidden_states.dtype)
        output[indices] = hidden_states
        return rearrange(output, '(b s) ... -> b s ...', b=batch)

    def forward(self, q, k, v, attention_mask=None):
        assert all((i.dtype in [torch.float16, torch.bfloat16] for i in (q, k, v)))
        assert all((i.is_cuda for i in (q, k, v)))
        batch_size, seqlen_q = q.shape[0], q.shape[1]
        seqlen_k = k.shape[1]
        seqlen_out = seqlen_q

        if flash_attn_func is not None and batch_size == 1:
            dropout_p = self.dropout_p if self.training else 0
            output = flash_attn_func(q, k, v, dropout_p, softmax_scale=self.softmax_scale, causal=self.causal)
            return output

        q, k, v = [rearrange(x, "b s ... -> (b s) ...") for x in [q, k, v]]
        cu_seqlens_q = torch.arange(
            0,
            (batch_size + 1) * seqlen_q,
            step=seqlen_q,
            dtype=torch.int32,
            device=q.device,
        )

        if batch_size > 1 and attention_mask is not None:
            k, indices_k, cu_seqlens_k, seqlen_k = self.unpad_input(k, attention_mask)
            if q.size(0) == v.size(0):
                q = q[indices_k]
                cu_seqlens_q = cu_seqlens_k
                seqlen_q = seqlen_k
            v = v[indices_k]
        else:
            cu_seqlens_k = torch.arange(
                0,
                (batch_size + 1) * seqlen_k,
                step=seqlen_k,
                dtype=torch.int32,
                device=q.device,
            )

        if self.training:
            assert seqlen_k == seqlen_q
            is_causal = self.causal
            dropout_p = self.dropout_p
        else:
            is_causal = seqlen_q == seqlen_k
            dropout_p = 0

        output = flash_attn_unpadded_func(
            q,
            k,
            v,
            cu_seqlens_q,
            cu_seqlens_k,
            seqlen_q,
            seqlen_k,
            dropout_p,
            softmax_scale=self.softmax_scale,
            causal=is_causal,
        )
        if batch_size > 1 and attention_mask is not None and seqlen_q == seqlen_k:
            output = self.pad_input(output, indices_k, batch_size, seqlen_out)
        else:
            new_shape = (batch_size, output.shape[0] // batch_size) + output.shape[1:]
            output = output.view(new_shape)
        return output


class QWenAttention(nn.Module):
    def __init__(self, config):
        super().__init__()

        self.register_buffer("masked_bias", torch.tensor(-1e4), persistent=False)
        self.seq_length = config.seq_length

        self.hidden_size = config.hidden_size
        self.split_size = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.head_dim = self.hidden_size // self.num_heads

        self.use_flash_attn = config.use_flash_attn
        self.scale_attn_weights = True

        self.projection_size = config.kv_channels * config.num_attention_heads

        assert self.projection_size % config.num_attention_heads == 0
        self.hidden_size_per_attention_head = (
            self.projection_size // config.num_attention_heads
        )

        self.c_attn = nn.Linear(config.hidden_size, 3 * self.projection_size)

        self.c_proj = nn.Linear(
            config.hidden_size, self.projection_size, bias=not config.no_bias
        )

        self.is_fp32 = not (config.bf16 or config.fp16)
        if (
            self.use_flash_attn
            and flash_attn_unpadded_func is not None
            and not self.is_fp32
        ):
            self.core_attention_flash = FlashSelfAttention(
                causal=True, attention_dropout=config.attn_dropout_prob
            )
        self.bf16 = config.bf16

        self.use_dynamic_ntk = config.use_dynamic_ntk
        self.use_logn_attn = config.use_logn_attn

        logn_list = [
            math.log(i, self.seq_length) if i > self.seq_length else 1
            for i in range(1, 32768)
        ]
        logn_tensor = torch.tensor(logn_list)[None, :, None, None]
        self.register_buffer("logn_tensor", logn_tensor, persistent=False)

        self.attn_dropout = nn.Dropout(config.attn_dropout_prob)
        self.softmax_in_fp32 = config.softmax_in_fp32 if hasattr(config, 'softmax_in_fp32') else False
        self.use_cache_quantization = config.use_cache_quantization if hasattr(config, 'use_cache_quantization') else False
        self.use_cache_kernel = config.use_cache_kernel if hasattr(config,'use_cache_kernel') else False
        cache_dtype = torch.float
        if self.bf16:
            cache_dtype=torch.bfloat16
        elif config.fp16:
            cache_dtype = torch.float16
        self.cache_qmax = torch.tensor(torch.iinfo(torch.uint8).max, dtype=cache_dtype)
        self.cache_qmin = torch.tensor(torch.iinfo(torch.uint8).min, dtype=cache_dtype)

        if config.use_cache_quantization and config.use_cache_kernel:
            # pre check if the support files existing
            module_root = pathlib.Path(__file__).parent
            src_files = ("cache_autogptq_cuda_256.cpp", "cache_autogptq_cuda_kernel_256.cu")
            if any(not (module_root/src).is_file() for src in src_files):
                warnings.warn("KV cache kernel source files (.cpp and .cu) not found.")
                self.cache_kernels = None
            else:
                try:
                    from .cpp_kernels import cache_autogptq_cuda_256
                    self.cache_kernels = cache_autogptq_cuda_256
                except ImportError:
                    warnings.warn("Failed to import KV cache kernels.")
                    self.cache_kernels = None

    def _attn(self, query, key, value, causal_mask=None, attention_mask=None, head_mask=None):
        device = query.device
        if self.use_cache_quantization:
            qk, qk_scale, qk_zero = key
            if self.use_cache_kernel and self.cache_kernels is not None:
                shape = query.shape[:-1] + (qk.shape[-2],)
                attn_weights = torch.zeros(shape, dtype=torch.float16, device=device)
                self.cache_kernels.vecquant8matmul_batched_faster_old(
                    query.contiguous() if query.dtype == torch.float16 else query.to(torch.float16).contiguous(),
                    qk.transpose(-1, -2).contiguous(),
                    attn_weights,
                    qk_scale.contiguous() if qk_scale.dtype == torch.float16 else qk_scale.to(torch.float16).contiguous(),
                    qk_zero.contiguous()if qk_zero.dtype == torch.float16 else qk_zero.to(torch.float16).contiguous())
                # attn_weights = attn_weights.to(query.dtype).contiguous()
            else:
                key = dequantize_cache_torch(qk, qk_scale, qk_zero)
                attn_weights = torch.matmul(query, key.transpose(-1, -2))
        else:
            attn_weights = torch.matmul(query, key.transpose(-1, -2))

        if self.scale_attn_weights:
            if self.use_cache_quantization:
                size_temp = value[0].size(-1)
            else:
                size_temp = value.size(-1)
            attn_weights = attn_weights / (size_temp ** 0.5)

        mask_value = torch.finfo(attn_weights.dtype).min
        if causal_mask is not None:
            attn_weights = torch.where(
                causal_mask, attn_weights.to(attn_weights.dtype), mask_value
            )

        if attention_mask is not None:
            attn_weights = attn_weights + attention_mask

        if self.softmax_in_fp32:
            attn_weights = nn.functional.softmax(attn_weights.float(), dim=-1)
        else:
            attn_weights = nn.functional.softmax(attn_weights, dim=-1)

        attn_weights = attn_weights.type(query.dtype)
        attn_weights = self.attn_dropout(attn_weights)

        if head_mask is not None:
            attn_weights = attn_weights * head_mask

        if self.use_cache_quantization:
            qv, qv_scale, qv_zero = value
            if self.use_cache_kernel and self.cache_kernels is not None:
                shape = attn_weights.shape[:-1] + (query.shape[-1],)
                attn_output = torch.zeros(shape, dtype=torch.float16, device=device)
                self.cache_kernels.vecquant8matmul_batched_column_compression_faster_old(
                    attn_weights.contiguous() if attn_weights.dtype == torch.float16 else attn_weights.to(torch.float16).contiguous(),
                    qv.contiguous(),  # dtype: int32
                    attn_output,
                    qv_scale.contiguous() if qv_scale.dtype == torch.float16 else qv_scale.to(torch.float16).contiguous(),
                    qv_zero.contiguous() if qv_zero.dtype == torch.float16 else qv_zero.to(torch.float16).contiguous())
                if attn_output.dtype != query.dtype:
                    attn_output = attn_output.to(query.dtype)
                    attn_weights = attn_weights.to(query.dtype)
            else:
                value = dequantize_cache_torch(qv, qv_scale, qv_zero)
                attn_output = torch.matmul(attn_weights, value)
        else:
            attn_output = torch.matmul(attn_weights, value)

        attn_output = attn_output.transpose(1, 2)

        return attn_output, attn_weights

    def _split_heads(self, tensor, num_heads, attn_head_size):
        new_shape = tensor.size()[:-1] + (num_heads, attn_head_size)
        tensor = tensor.view(new_shape)
        return tensor

    def _merge_heads(self, tensor, num_heads, attn_head_size):
        tensor = tensor.contiguous()
        new_shape = tensor.size()[:-2] + (num_heads * attn_head_size,)
        return tensor.view(new_shape)

    def forward(
        self,
        hidden_states: Optional[Tuple[torch.FloatTensor]],
        rotary_pos_emb_list: Optional[List[List[torch.Tensor]]] = None,
        layer_past: Optional[Tuple[torch.Tensor]] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        encoder_attention_mask: Optional[torch.FloatTensor] = None,
        output_attentions: Optional[bool] = False,
        use_cache: Optional[bool] = False,
    ):
        mixed_x_layer = self.c_attn(hidden_states)

        query, key, value = mixed_x_layer.split(self.split_size, dim=2)

        query = self._split_heads(query, self.num_heads, self.head_dim)
        key = self._split_heads(key, self.num_heads, self.head_dim)
        value = self._split_heads(value, self.num_heads, self.head_dim)

        if rotary_pos_emb_list is not None:
            cur_len = query.shape[1]
            if len(rotary_pos_emb_list) == 1:
                rotary_pos_emb = rotary_pos_emb_list[0]
                rotary_pos_emb = [i[:, -cur_len:, :, :] for i in rotary_pos_emb]
                rotary_pos_emb = (rotary_pos_emb,) * 2
                q_pos_emb, k_pos_emb = rotary_pos_emb
                # Slice the pos emb for current inference
                query = apply_rotary_pos_emb(query, q_pos_emb)
                key = apply_rotary_pos_emb(key, k_pos_emb)
            else:
                query_list = []
                key_list = []
                for i, rotary_pos_emb in enumerate(rotary_pos_emb_list):
                    rotary_pos_emb = [i[:, -cur_len:, :, :] for i in rotary_pos_emb]
                    rotary_pos_emb = (rotary_pos_emb,) * 2
                    q_pos_emb, k_pos_emb = rotary_pos_emb
                    # Slice the pos emb for current inference
                    query_list += [apply_rotary_pos_emb(query[i:i+1, :, :], q_pos_emb)]
                    key_list += [apply_rotary_pos_emb(key[i:i+1, :, :], k_pos_emb)]
                query = torch.cat(query_list, dim=0)
                key = torch.cat(key_list, dim=0)

        if self.use_cache_quantization:
            key = quantize_cache_v(key.permute(0, 2, 1, 3),
                                       bits=8,
                                       qmin=self.cache_qmin,
                                       qmax=self.cache_qmax)
            value = quantize_cache_v(value.permute(0, 2, 1, 3),
                                         bits=8,
                                         qmin=self.cache_qmin,
                                         qmax=self.cache_qmax)


        if layer_past is not None:
            past_key, past_value = layer_past[0], layer_past[1]
            if self.use_cache_quantization:
                # use_cache_quantization:
                # present=((q_key,key_scale,key_zero_point),
                #          (q_value,value_scale,value_zero_point))
                key = (torch.cat((past_key[0], key[0]), dim=2),
                       torch.cat((past_key[1], key[1]), dim=2),
                       torch.cat((past_key[2], key[2]), dim=2))
                value = (torch.cat((past_value[0], value[0]), dim=2),
                         torch.cat((past_value[1], value[1]), dim=2),
                         torch.cat((past_value[2], value[2]), dim=2))
            else:
                # not use_cache_quantization:
                # present=(key,value)
                key = torch.cat((past_key, key), dim=1)
                value = torch.cat((past_value, value), dim=1)

        if use_cache:
            present = (key, value)
        else:
            present = None

        key_size = key[0].size(2) if self.use_cache_quantization else key.size(1)
        if key_size > self.seq_length and self.use_logn_attn and not self.training:
            if self.use_cache_quantization:
                seq_start = key[0].size(2) - query.size(1)
                seq_end = key[0].size(2)
            else:
                seq_start = key.size(1) - query.size(1)
                seq_end = key.size(1)
            logn_tensor = self.logn_tensor[:, seq_start:seq_end, :, :].type_as(query)
            query = query * logn_tensor.expand_as(query)

        if (
            self.use_flash_attn
            and flash_attn_unpadded_func is not None
            and not self.is_fp32
            and query.is_cuda
        ):
            q, k, v = query, key, value
            attn_output = self.core_attention_flash(q, k, v, attention_mask=attention_mask)
        else:
            key_size = key[0].size(2) if self.use_cache_quantization else key.size(1)
            if query.size(1) == key_size:
                causal_mask = torch.tril(
                    torch.ones((key_size, key_size), dtype=torch.bool, device=query.device)
                ).view(1, 1, key_size, key_size)
            else:
                causal_mask = None
            query = query.permute(0, 2, 1, 3)
            if not self.use_cache_quantization:
                key = key.permute(0, 2, 1, 3)
                value = value.permute(0, 2, 1, 3)
            if (
                causal_mask is None
                and self.use_flash_attn
                and flash_attn_unpadded_func is not None
                and not self.is_fp32
                and not query.is_cuda
            ):
                raise Exception(_ERROR_INPUT_CPU_QUERY_WITH_FLASH_ATTN_ACTIVATED)

            if not self.use_cache_quantization and SUPPORT_TORCH2:
                if attention_mask is not None:
                    attention_mask = attention_mask.expand(-1, -1, query.size(2), -1)
                    if causal_mask is not None:
                        attention_mask = attention_mask.masked_fill(~causal_mask, torch.finfo(query.dtype).min)
                else:
                    attention_mask = causal_mask
                attn_output = F.scaled_dot_product_attention(
                    query, key, value, attn_mask=attention_mask
                ).transpose(1, 2)
                attn_weight = None
            else:
                attn_output, attn_weight = self._attn(
                    query, key, value, causal_mask, attention_mask, head_mask
                )
        context_layer = self._merge_heads(
            attn_output, self.num_heads, self.head_dim
        )

        attn_output = self.c_proj(context_layer)

        outputs = (attn_output, present)
        if output_attentions:
            if (
                self.use_flash_attn
                and flash_attn_unpadded_func is not None
                and not self.is_fp32
            ):
                raise ValueError("Cannot output attentions while using flash-attn")
            elif not self.use_cache_quantization and SUPPORT_TORCH2:
                raise ValueError("Cannot output attentions while using scaled_dot_product_attention")
            else:
                outputs += (attn_weight,)

        return outputs


class QWenMLP(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.w1 = nn.Linear(
            config.hidden_size, config.intermediate_size // 2, bias=not config.no_bias
        )
        self.w2 = nn.Linear(
            config.hidden_size, config.intermediate_size // 2, bias=not config.no_bias
        )
        ff_dim_in = config.intermediate_size // 2
        self.c_proj = nn.Linear(ff_dim_in, config.hidden_size, bias=not config.no_bias)

    def forward(self, hidden_states):
        a1 = self.w1(hidden_states)
        a2 = self.w2(hidden_states)
        intermediate_parallel = a1 * F.silu(a2)
        output = self.c_proj(intermediate_parallel)
        return output


class QWenBlock(nn.Module):
    def __init__(self, config):
        super().__init__()
        hidden_size = config.hidden_size
        self.bf16 = config.bf16

        self.ln_1 = RMSNorm(
            hidden_size,
            eps=config.layer_norm_epsilon,
        )
        self.attn = QWenAttention(config)
        self.ln_2 = RMSNorm(
            hidden_size,
            eps=config.layer_norm_epsilon,
        )

        self.mlp = QWenMLP(config)

    def forward(
        self,
        hidden_states: Optional[Tuple[torch.FloatTensor]],
        rotary_pos_emb_list: Optional[List[List[torch.Tensor]]] = None,
        layer_past: Optional[Tuple[torch.Tensor]] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        encoder_attention_mask: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = False,
        output_attentions: Optional[bool] = False,
    ):
        layernorm_output = self.ln_1(hidden_states)

        attn_outputs = self.attn(
            layernorm_output,
            rotary_pos_emb_list,
            layer_past=layer_past,
            attention_mask=attention_mask,
            head_mask=head_mask,
            use_cache=use_cache,
            output_attentions=output_attentions,
        )
        attn_output = attn_outputs[0]

        outputs = attn_outputs[1:]

        residual = hidden_states
        layernorm_input = attn_output + residual

        layernorm_output = self.ln_2(layernorm_input)

        residual = layernorm_input
        mlp_output = self.mlp(layernorm_output)
        hidden_states = residual + mlp_output

        if use_cache:
            outputs = (hidden_states,) + outputs
        else:
            outputs = (hidden_states,) + outputs[1:]

        return outputs


class QWenPreTrainedModel(PreTrainedModel):
    config_class = QWenConfig
    base_model_prefix = "transformer"
    is_parallelizable = False
    supports_gradient_checkpointing = True
    _no_split_modules = ["QWenBlock"]
    _skip_keys_device_placement = "past_key_values"

    def __init__(self, *inputs, **kwargs):
        super().__init__(*inputs, **kwargs)

    def _init_weights(self, module):
        """Initialize the weights."""
        if isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, RMSNorm):
            module.weight.data.fill_(1.0)

        for name, p in module.named_parameters():
            if name == "c_proj.weight":
                p.data.normal_(
                    mean=0.0,
                    std=(
                        self.config.initializer_range
                        / math.sqrt(2 * self.config.num_hidden_layers)
                    ),
                )

    def _set_gradient_checkpointing(self, module, value=False):
        if isinstance(module, QWenModel):
            module.gradient_checkpointing = value


class QWenModel(QWenPreTrainedModel):
    _keys_to_ignore_on_load_missing = ["attn.masked_bias"]

    def __init__(self, config):
        super().__init__(config)
        self.vocab_size = config.vocab_size
        self.num_hidden_layers = config.num_hidden_layers
        self.embed_dim = config.hidden_size
        self.use_cache_quantization = self.config.use_cache_quantization if hasattr(self.config, 'use_cache_quantization') else False

        self.gradient_checkpointing = False
        self.use_dynamic_ntk = config.use_dynamic_ntk
        self.seq_length = config.seq_length

        self.wte = nn.Embedding(self.vocab_size, self.embed_dim)

        self.drop = nn.Dropout(config.emb_dropout_prob)

        if config.rotary_pct == 1.0:
            self.rotary_ndims = None
        else:
            assert config.rotary_pct < 1
            self.rotary_ndims = int(
                config.kv_channels * config.rotary_pct
            )
        dim = (
            self.rotary_ndims
            if self.rotary_ndims is not None
            else config.kv_channels
        )
        self.rotary_emb = RotaryEmbedding(dim, base=config.rotary_emb_base)

        self.use_flash_attn = config.use_flash_attn
        self.is_fp32 = not (config.bf16 or config.fp16)

        self.h = nn.ModuleList(
            [
                QWenBlock(
                    config
                )
                for i in range(config.num_hidden_layers)
            ]
        )
        self.ln_f = RMSNorm(
            self.embed_dim,
            eps=config.layer_norm_epsilon,
        )

        self.post_init()

    def get_input_embeddings(self):
        return self.wte

    def set_input_embeddings(self, new_embeddings):
        self.wte = new_embeddings

    def get_ntk_alpha(self, true_seq_len):
        context_value = math.log(true_seq_len / self.seq_length, 2) + 1
        ntk_alpha = 2 ** math.ceil(context_value) - 1
        ntk_alpha = max(ntk_alpha, 1)
        return ntk_alpha

    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        encoder_attention_mask: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ):
        output_attentions = (
            output_attentions
            if output_attentions is not None
            else self.config.output_attentions
        )
        output_hidden_states = (
            output_hidden_states
            if output_hidden_states is not None
            else self.config.output_hidden_states
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        return_dict = (
            return_dict if return_dict is not None else self.config.use_return_dict
        )

        if input_ids is not None and inputs_embeds is not None:
            raise ValueError(
                "You cannot specify both input_ids and inputs_embeds at the same time"
            )
        elif input_ids is not None:
            input_shape = input_ids.size()
            input_ids = input_ids.view(-1, input_shape[-1])
            batch_size = input_ids.shape[0]
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
            batch_size = inputs_embeds.shape[0]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        device = input_ids.device if input_ids is not None else inputs_embeds.device

        if token_type_ids is not None:
            token_type_ids = token_type_ids.view(-1, input_shape[-1])
        if position_ids is not None:
            position_ids = position_ids.view(-1, input_shape[-1])

        if past_key_values is None:
            past_length = 0
            past_key_values = tuple([None] * len(self.h))
        else:
            if self.use_cache_quantization:
                past_length = past_key_values[0][0][0].size(2)
            else:
                past_length = past_key_values[0][0].size(-2)
        if position_ids is None:
            position_ids = torch.arange(
                past_length,
                input_shape[-1] + past_length,
                dtype=torch.long,
                device=device,
            )
            position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])

        if attention_mask is not None:
            if batch_size <= 0:
                raise ValueError("batch_size has to be defined and > 0")
            attention_mask = attention_mask.view(batch_size, -1)
            attention_mask = attention_mask[:, None, None, :]
            attention_mask = attention_mask.to(dtype=self.dtype)
            attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min

        encoder_attention_mask = None
        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)

        if inputs_embeds is None:
            inputs_embeds = self.wte(input_ids)
        hidden_states = inputs_embeds

        kv_seq_len = hidden_states.size()[1]
        if past_key_values[0] is not None:
            # past key values[0][0] shape: bs * seq_len * head_num * dim
            if self.use_cache_quantization:
                kv_seq_len += past_key_values[0][0][0].shape[2]
            else:
                kv_seq_len += past_key_values[0][0].shape[1]

        if self.training or not self.use_dynamic_ntk:
            ntk_alpha_list = [1.0]
        elif kv_seq_len != hidden_states.size()[1]:
            ntk_alpha_list = self.rotary_emb._ntk_alpha_cached_list
        else:
            ntk_alpha_list = []
            if attention_mask is not None and kv_seq_len > self.seq_length:
                true_seq_lens = attention_mask.squeeze(1).squeeze(1).eq(0).sum(dim=-1, dtype=torch.int32)
                for i in range(hidden_states.size()[0]):
                    true_seq_len = true_seq_lens[i].item()
                    ntk_alpha = self.get_ntk_alpha(true_seq_len)
                    ntk_alpha_list.append(ntk_alpha)
            else:
                ntk_alpha = self.get_ntk_alpha(kv_seq_len)
                ntk_alpha_list.append(ntk_alpha)
        self.rotary_emb._ntk_alpha_cached_list = ntk_alpha_list
        rotary_pos_emb_list = [
            self.rotary_emb(kv_seq_len, ntk_alpha=ntk_alpha) for ntk_alpha in ntk_alpha_list
        ]

        hidden_states = self.drop(hidden_states)
        output_shape = input_shape + (hidden_states.size(-1),)

        if self.gradient_checkpointing and self.training:
            if use_cache:
                logger.warning_once(
                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                )
                use_cache = False

        presents = () if use_cache else None
        all_self_attentions = () if output_attentions else None
        all_hidden_states = () if output_hidden_states else None
        for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):

            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            if self.gradient_checkpointing and self.training:

                def create_custom_forward(module):
                    def custom_forward(*inputs):
                        # None for past_key_value
                        return module(*inputs, use_cache, output_attentions)

                    return custom_forward

                outputs = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(block),
                    hidden_states,
                    rotary_pos_emb_list,
                    None,
                    attention_mask,
                    head_mask[i],
                    encoder_hidden_states,
                    encoder_attention_mask,
                )
            else:
                outputs = block(
                    hidden_states,
                    layer_past=layer_past,
                    rotary_pos_emb_list=rotary_pos_emb_list,
                    attention_mask=attention_mask,
                    head_mask=head_mask[i],
                    encoder_hidden_states=encoder_hidden_states,
                    encoder_attention_mask=encoder_attention_mask,
                    use_cache=use_cache,
                    output_attentions=output_attentions,
                )

            hidden_states = outputs[0]
            if use_cache is True:
                presents = presents + (outputs[1],)

            if output_attentions:
                all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)

        hidden_states = self.ln_f(hidden_states)
        hidden_states = hidden_states.view(output_shape)
        # Add last hidden state
        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        if not return_dict:
            return tuple(
                v for v in [hidden_states, presents, all_hidden_states] if v is not None
            )

        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=presents,
            hidden_states=all_hidden_states,
            attentions=all_self_attentions,
        )


class QWenLMHeadModel(QWenPreTrainedModel):
    _keys_to_ignore_on_load_missing = [r"h\.\d+\.attn\.rotary_emb\.inv_freq"]
    _keys_to_ignore_on_load_unexpected = [r"h\.\d+\.attn\.masked_bias"]

    def __init__(self, config):
        super().__init__(config)
        assert (
            config.bf16 + config.fp16 + config.fp32 <= 1
        ), "Only one of \"bf16\", \"fp16\", \"fp32\" can be true"

        autoset_precision = config.bf16 + config.fp16 + config.fp32 == 0

        if autoset_precision:
            if SUPPORT_BF16:
                logger.warn(
                    "The model is automatically converting to bf16 for faster inference. "
                    "If you want to disable the automatic precision, please manually add bf16/fp16/fp32=True to \"AutoModelForCausalLM.from_pretrained\"."
                )
                config.bf16 = True
            elif SUPPORT_FP16:
                logger.warn(
                    "The model is automatically converting to fp16 for faster inference. "
                    "If you want to disable the automatic precision, please manually add bf16/fp16/fp32=True to \"AutoModelForCausalLM.from_pretrained\"."
                )
                config.fp16 = True
            else:
                config.fp32 = True

        if config.bf16 and SUPPORT_CUDA and not SUPPORT_BF16:
            logger.warn("Your device does NOT seem to support bf16, you can switch to fp16 or fp32 by by passing fp16/fp32=True in \"AutoModelForCausalLM.from_pretrained\".")
        if config.fp16 and SUPPORT_CUDA and not SUPPORT_FP16:
            logger.warn("Your device does NOT support faster inference with fp16, please switch to fp32 which is likely to be faster")
        if config.fp32:
            if SUPPORT_BF16:
                logger.warn("Your device support faster inference by passing bf16=True in \"AutoModelForCausalLM.from_pretrained\".")
            elif SUPPORT_FP16:
                logger.warn("Your device support faster inference by passing fp16=True in \"AutoModelForCausalLM.from_pretrained\".")

        if config.use_flash_attn == "auto":
            if config.bf16 or config.fp16:
                logger.warn("Try importing flash-attention for faster inference...")
                config.use_flash_attn = True
            else:
                config.use_flash_attn = False
        if config.use_flash_attn and config.fp32:
            logger.warn("Flash attention will be disabled because it does NOT support fp32.")

        if config.use_flash_attn:
            _import_flash_attn()

        self.transformer = QWenModel(config)
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        if config.bf16:
            self.transformer.bfloat16()
            self.lm_head.bfloat16()
        if config.fp16:
            self.transformer.half()
            self.lm_head.half()
        self.post_init()

    def get_output_embeddings(self):
        return self.lm_head

    def set_output_embeddings(self, new_embeddings):
        self.lm_head = new_embeddings

    def prepare_inputs_for_generation(
        self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs
    ):
        if past_key_values:
            input_ids = input_ids[:, -1].unsqueeze(-1)

        if input_ids.size(0) == 1:
            attention_mask = None
        else:
            attention_mask = kwargs.get("attention_mask", None)

        if inputs_embeds is not None and past_key_values is None:
            model_inputs = {"inputs_embeds": inputs_embeds}
        else:
            model_inputs = {"input_ids": input_ids}

        model_inputs.update(
            {
                "past_key_values": past_key_values,
                "use_cache": kwargs.get("use_cache"),
                "attention_mask": attention_mask,
            }
        )
        return model_inputs

    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        encoder_attention_mask: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, CausalLMOutputWithPast]:

        return_dict = (
            return_dict if return_dict is not None else self.config.use_return_dict
        )

        transformer_outputs = self.transformer(
            input_ids,
            past_key_values=past_key_values,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        hidden_states = transformer_outputs[0]

        lm_logits = self.lm_head(hidden_states)

        loss = None
        if labels is not None:
            labels = labels.to(lm_logits.device)
            shift_logits = lm_logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(
                shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)
            )

        if not return_dict:
            output = (lm_logits,) + transformer_outputs[1:]
            return ((loss,) + output) if loss is not None else output

        return CausalLMOutputWithPast(
            loss=loss,
            logits=lm_logits,
            past_key_values=transformer_outputs.past_key_values,
            hidden_states=transformer_outputs.hidden_states,
            attentions=transformer_outputs.attentions,
        )

    @staticmethod
    def _reorder_cache(
        past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor
    ) -> Tuple[Tuple[torch.Tensor]]:

        return tuple(
            tuple(
                past_state.index_select(0, beam_idx.to(past_state.device))
                for past_state in layer_past
            )
            for layer_past in past_key_values
        )

    def chat(
        self,
        tokenizer: PreTrainedTokenizer,
        query: str,
        history: Optional[HistoryType],
        system: str = "You are a helpful assistant.",
        stream: Optional[bool] = _SENTINEL,
        stop_words_ids: Optional[List[List[int]]] = None,
        generation_config: Optional[GenerationConfig] = None,
        **kwargs,
    ) -> Tuple[str, HistoryType]:
        generation_config = generation_config if generation_config is not None else self.generation_config

        assert stream is _SENTINEL, _ERROR_STREAM_IN_CHAT
        assert generation_config.chat_format == 'chatml', _ERROR_BAD_CHAT_FORMAT
        if history is None:
            history = []
        else:
            # make a copy of the user's input such that is is left untouched
            history = copy.deepcopy(history)

        if stop_words_ids is None:
            stop_words_ids = []

        max_window_size = kwargs.get('max_window_size', None)
        if max_window_size is None:
            max_window_size = generation_config.max_window_size
        raw_text, context_tokens = make_context(
            tokenizer,
            query,
            history=history,
            system=system,
            max_window_size=max_window_size,
            chat_format=generation_config.chat_format,
        )

        stop_words_ids.extend(get_stop_words_ids(
            generation_config.chat_format, tokenizer
        ))
        input_ids = torch.tensor([context_tokens]).to(self.device)
        outputs = self.generate(
                    input_ids,
                    stop_words_ids=stop_words_ids,
                    return_dict_in_generate=False,
                    generation_config=generation_config,
                    **kwargs,
                )

        response = decode_tokens(
            outputs[0],
            tokenizer,
            raw_text_len=len(raw_text),
            context_length=len(context_tokens),
            chat_format=generation_config.chat_format,
            verbose=False,
            errors='replace'
        )

        # as history is a copy of the user inputs,
        # we can always return the new turn to the user.
        # separating input history and output history also enables the user
        # to implement more complex history management
        history.append((query, response))

        return response, history

    def chat_stream(
            self,
            tokenizer: PreTrainedTokenizer,
            query: str,
            history: Optional[HistoryType],
            system: str = "You are a helpful assistant.",
            stop_words_ids: Optional[List[List[int]]] = None,
            logits_processor: Optional[LogitsProcessorList] = None,
            generation_config: Optional[GenerationConfig] = None,
            **kwargs,
    ) -> Generator[str, Any, None]:
        generation_config = generation_config if generation_config is not None else self.generation_config
        assert generation_config.chat_format == 'chatml', _ERROR_BAD_CHAT_FORMAT
        if history is None:
            history = []
        if stop_words_ids is None:
            stop_words_ids = []

        max_window_size = kwargs.get('max_window_size', None)
        if max_window_size is None:
            max_window_size = generation_config.max_window_size
        raw_text, context_tokens = make_context(
            tokenizer,
            query,
            history=history,
            system=system,
            max_window_size=max_window_size,
            chat_format=generation_config.chat_format,
        )

        stop_words_ids.extend(get_stop_words_ids(
            generation_config.chat_format, tokenizer
        ))
        if stop_words_ids is not None:
            stop_words_logits_processor = StopWordsLogitsProcessor(
                stop_words_ids=stop_words_ids,
                eos_token_id=generation_config.eos_token_id,
            )
            if logits_processor is None:
                logits_processor = LogitsProcessorList([stop_words_logits_processor])
            else:
                logits_processor.append(stop_words_logits_processor)
        input_ids = torch.tensor([context_tokens]).to(self.device)

        from transformers_stream_generator.main import NewGenerationMixin, StreamGenerationConfig
        self.__class__.generate_stream = NewGenerationMixin.generate
        self.__class__.sample_stream = NewGenerationMixin.sample_stream
        stream_config = StreamGenerationConfig(**generation_config.to_dict(), do_stream=True)

        def stream_generator():
            outputs = []
            for token in self.generate_stream(
                    input_ids,
                    return_dict_in_generate=False,
                    generation_config=stream_config,
                    logits_processor=logits_processor,
                    seed=-1,
                    **kwargs):
                outputs.append(token.item())
                yield tokenizer.decode(outputs, skip_special_tokens=True, errors='ignore')

        return stream_generator()

    def generate(
        self,
        inputs: Optional[torch.Tensor] = None,
        generation_config: Optional[GenerationConfig] = None,
        logits_processor: Optional[LogitsProcessorList] = None,
        stopping_criteria: Optional[StoppingCriteriaList] = None,
        prefix_allowed_tokens_fn: Optional[
            Callable[[int, torch.Tensor], List[int]]
        ] = None,
        synced_gpus: Optional[bool] = None,
        assistant_model: Optional["PreTrainedModel"] = None,
        streamer: Optional["BaseStreamer"] = None,
        **kwargs,
    ) -> Union[GenerateOutput, torch.LongTensor]:
        generation_config = generation_config if generation_config is not None else self.generation_config

        # Process stop_words_ids.
        stop_words_ids = kwargs.pop("stop_words_ids", None)
        if stop_words_ids is None and generation_config is not None:
            stop_words_ids = getattr(generation_config, "stop_words_ids", None)
        if stop_words_ids is None:
            stop_words_ids = getattr(generation_config, "stop_words_ids", None)

        if stop_words_ids is not None:
            stop_words_logits_processor = StopWordsLogitsProcessor(
                stop_words_ids=stop_words_ids,
                eos_token_id=generation_config.eos_token_id,
            )
            if logits_processor is None:
                logits_processor = LogitsProcessorList([stop_words_logits_processor])
            else:
                logits_processor.append(stop_words_logits_processor)

        return super().generate(
            inputs,
            generation_config=generation_config,
            logits_processor=logits_processor,
            stopping_criteria=stopping_criteria,
            prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
            synced_gpus=synced_gpus,
            assistant_model=assistant_model,
            streamer=streamer,
            **kwargs,
        )


class RotaryEmbedding(torch.nn.Module):
    def __init__(self, dim, base=10000):
        super().__init__()
        self.dim = dim
        self.base = base
        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
        self.register_buffer("inv_freq", inv_freq, persistent=False)
        if importlib.util.find_spec("einops") is None:
            raise RuntimeError("einops is required for Rotary Embedding")

        self._rotary_pos_emb_cache = None
        self._seq_len_cached = 0
        self._ntk_alpha_cached = 1.0
        self._ntk_alpha_cached_list = [1.0]

    def update_rotary_pos_emb_cache(self, seqlen, ntk_alpha=1.0):
        if seqlen > self._seq_len_cached or ntk_alpha != self._ntk_alpha_cached:
            base = self.base * ntk_alpha ** (self.dim / (self.dim - 2))
            self.inv_freq = 1.0 / (
                base
                ** (
                    torch.arange(0, self.dim, 2, device=self.inv_freq.device).float()
                    / self.dim
                )
            )
            self._seq_len_cached = max(2 * seqlen, 16)
            self._ntk_alpha_cached = ntk_alpha
            seq = torch.arange(self._seq_len_cached, device=self.inv_freq.device)
            freqs = torch.outer(seq.type_as(self.inv_freq), self.inv_freq)

            emb = torch.cat((freqs, freqs), dim=-1)
            from einops import rearrange

            emb = rearrange(emb, "n d -> 1 n 1 d")

            cos, sin = emb.cos(), emb.sin()
            self._rotary_pos_emb_cache = [cos, sin]

    def forward(self, max_seq_len, ntk_alpha=1.0):
        self.update_rotary_pos_emb_cache(max_seq_len, ntk_alpha)
        cos, sin = self._rotary_pos_emb_cache
        return [cos[:, :max_seq_len], sin[:, :max_seq_len]]


def _rotate_half(x):
    from einops import rearrange

    x = rearrange(x, "... (j d) -> ... j d", j=2)
    x1, x2 = x.unbind(dim=-2)
    return torch.cat((-x2, x1), dim=-1)


def apply_rotary_pos_emb(t, freqs):
    """ Apply rotary embedding to the first rotary_dim of the iput

    Arguments:
      t (tensor(batch_size, seq_len, n_head, head_dim)):
        the input embedding/hidden states
      freqs (list[tensor(1, seq_len, 1, rotary_dim), tensor(1, seq_len, 1, rotary_dim)]):
        the cached cos/sin position embeddings
    """
    rot_dim = freqs[0].shape[-1]
    cos, sin = freqs
    t_float = t.float()
    if apply_rotary_emb_func is not None and t.is_cuda:
        # apply_rotary_emb in flash_attn requires cos/sin to be of
        # shape (seqlen, rotary_dim / 2) and apply rotary embedding
        # to the first rotary_dim of the input
        cos = cos.squeeze(0).squeeze(1)[:, : rot_dim // 2]
        sin = sin.squeeze(0).squeeze(1)[:, : rot_dim // 2]
        return apply_rotary_emb_func(t_float, cos, sin).type_as(t)
    else:
        t_rot, t_pass = t_float[..., :rot_dim], t_float[..., rot_dim:]
        t_rot = (t_rot * cos) + (_rotate_half(t_rot) * sin)
        return torch.cat((t_rot, t_pass), dim=-1).type_as(t)


class RMSNorm(torch.nn.Module):
    def __init__(self, dim: int, eps: float = 1e-6):
        super().__init__()
        self.eps = eps
        self.weight = nn.Parameter(torch.ones(dim))

    def _norm(self, x):
        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)

    def forward(self, x):
        if rms_norm is not None and x.is_cuda:
            return rms_norm(x, self.weight, self.eps)
        else:
            output = self._norm(x.float()).type_as(x)
            return output * self.weight