File size: 47,344 Bytes
b7a0389
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
# Copyright 2022 MosaicML Examples authors
# SPDX-License-Identifier: Apache-2.0

# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018-2021, NVIDIA CORPORATION.  All rights reserved.
# Copyright (c) 2022, Tri Dao.

"""Implements Mosaic BERT, with an eye towards the Hugging Face API.

Mosaic BERT improves performance over Hugging Face BERT through the following:

1. ALiBi. This architectural change removes positional embeddings and instead encodes positional
information through attention biases based on query-key position distance. It improves the effectiveness
of training with shorter sequence lengths by enabling extrapolation to longer sequences.

2. Gated Linear Units (GLU). This architectural change replaces the FFN component of the BERT layer
to improve overall expressiveness, providing better convergence properties.

3. Flash Attention. The Mosaic BERT's self-attention layer makes use of Flash Attention, which dramatically
improves the speed of self-attention. Our implementation utilizes a bleeding edge implementation that
supports attention biases, which allows us to use Flash Attention with ALiBi.

4. Unpadding. Padding is often used to simplify batching across sequences of different lengths. Standard BERT
implementations waste computation on padded tokens. Mosaic BERT internally unpads to reduce unnecessary computation
and improve speed. It does this without changing how the user interfaces with the model, thereby
preserving the simple API of standard implementations.


Currently, Mosaic BERT is available for masked language modeling :class:`BertForMaskedLM` and sequence
classification :class:`BertForSequenceClassification`. We aim to expand this catalogue in future releases.

See :file:`./mosaic_bert.py` for utilities to simplify working with Mosaic BERT in Composer, and for example usage
of the core Mosaic BERT classes.
"""

import copy
import logging
import math
import warnings
from typing import List, Optional, Tuple, Union

import torch
import torch.nn as nn
from einops import rearrange
from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
from transformers.activations import ACT2FN
from transformers.modeling_outputs import (MaskedLMOutput,
                                           SequenceClassifierOutput)
from transformers.models.bert.modeling_bert import BertPreTrainedModel

from .bert_padding import (index_first_axis,
                                            index_put_first_axis, pad_input,
                                            unpad_input, unpad_input_only)

try:
    from .flash_attn_triton import flash_attn_qkvpacked_func
except ImportError as e:
    flash_attn_qkvpacked_func = None

logger = logging.getLogger(__name__)


class BertEmbeddings(nn.Module):
    """Construct the embeddings for words, ignoring position.

    There are no positional embeddings since we use ALiBi and token_type
    embeddings.

    This module is modeled after the Hugging Face BERT's
    :class:`~transformers.model.bert.modeling_bert.BertEmbeddings`, but is
    modified as part of Mosaic BERT's ALiBi implementation. The key change is
    that position embeddings are removed. Position information instead comes
    from attention biases that scale linearly with the position distance
    between query and key tokens.

    This module ignores the `position_ids` input to the `forward` method.
    """

    def __init__(self, config):
        super().__init__()
        self.word_embeddings = nn.Embedding(config.vocab_size,
                                            config.hidden_size,
                                            padding_idx=config.pad_token_id)
        # ALiBi doesn't use position embeddings
        self.token_type_embeddings = nn.Embedding(config.type_vocab_size,
                                                  config.hidden_size)

        # self.LayerNorm is not snake-cased to stick with TensorFlow model
        # variable name and be able to load any TensorFlow checkpoint file
        self.LayerNorm = nn.LayerNorm(config.hidden_size,
                                      eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.register_buffer('token_type_ids',
                             torch.zeros(config.max_position_embeddings,
                                         dtype=torch.long),
                             persistent=False)

    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        past_key_values_length: int = 0,
    ) -> torch.Tensor:
        if (input_ids is not None) == (inputs_embeds is not None):
            raise ValueError('Must specify either input_ids or input_embeds!')
        if input_ids is not None:
            input_shape = input_ids.size()
        else:
            assert inputs_embeds is not None  # just for type checking
            input_shape = inputs_embeds.size()[:-1]

        seq_length = input_shape[1]

        if position_ids is None:
            # great! ALiBi
            pass

        # Setting the token_type_ids to the registered buffer in constructor
        # where it is all zeros, which usually occurs when it's auto-generated;
        # registered buffer helps users when tracing the model without passing
        # token_type_ids, solves issue #5664
        if token_type_ids is None:
            if hasattr(self, 'token_type_ids'):
                assert isinstance(self.token_type_ids, torch.LongTensor)
                buffered_token_type_ids = self.token_type_ids[:, :seq_length]
                buffered_token_type_ids_expanded = buffered_token_type_ids.expand(
                    input_shape[0], seq_length)
                token_type_ids = buffered_token_type_ids_expanded  # type: ignore
            else:
                token_type_ids = torch.zeros(input_shape,  # type: ignore
                                             dtype=torch.long,
                                             device=self.word_embeddings.device) # type: ignore  # yapf: disable

        if inputs_embeds is None:
            inputs_embeds = self.word_embeddings(input_ids)
        token_type_embeddings = self.token_type_embeddings(token_type_ids)

        embeddings = inputs_embeds + token_type_embeddings
        # no position embeddings! ALiBi
        embeddings = self.LayerNorm(embeddings)
        embeddings = self.dropout(embeddings)
        return embeddings


class BertUnpadSelfAttention(nn.Module):
    """Performs multi-headed self attention on a batch of unpadded sequences.

    If Triton is installed, this module uses Flash Attention to greatly improve throughput.
    The Flash Attention implementation used in Mosaic BERT supports arbitrary attention biases (which
    we use to implement ALiBi), but does not support attention dropout. If either Triton is not installed
    or `config.attention_probs_dropout_prob > 0`, the implementation will default to a
    math-equivalent pytorch version, which is much slower.

    See `forward` method for additional detail.
    """

    def __init__(self, config):
        super().__init__()
        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(
                config, 'embedding_size'):
            raise ValueError(
                f'The hidden size ({config.hidden_size}) is not a multiple of the number of attention '
                f'heads ({config.num_attention_heads})')

        self.num_attention_heads = config.num_attention_heads
        self.attention_head_size = int(config.hidden_size /
                                       config.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size
        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
        self.p_dropout = config.attention_probs_dropout_prob
        self.Wqkv = nn.Linear(self.all_head_size, 3 * config.hidden_size)

        # Warn if defaulting to pytorch because of import issues
        if flash_attn_qkvpacked_func is None:
            warnings.warn(
                'Unable to import Triton; defaulting MosaicBERT attention implementation to pytorch (this will reduce throughput when using this model).'
            )

    def forward(self, hidden_states: torch.Tensor, cu_seqlens: torch.Tensor,
                max_seqlen_in_batch: int, indices: torch.Tensor,
                attn_mask: torch.Tensor, bias: torch.Tensor) -> torch.Tensor:
        """Perform self-attention.

        If dropout is zero, then we can use the Triton kernel, so we do that. However, if not, we send through a standard PyTorch
        implementation of self-attention.

        The arguments are unpadded, and our implementations of attention require padded arguments,
        so we first call `pad_input`. Once we compute attention, we re-unpad our outputs for the other layers.
        The pad/unpad operations add overhead, but not sending pad tokens through ffs saves compute.
        It is possible to write an unpadded implementation of attention (in Triton and PyTorch), which we will eventually do.

        Args:
            hidden_states: (total_nnz, dim)
            cu_seqlens: (batch + 1,)
            max_seqlen_in_batch: int
            indices: (total_nnz,)
            attn_mask: (batch, max_seqlen_in_batch)
            bias: (batch, heads, max_seqlen_in_batch, max_seqlen_in_batch)

        Returns:
            attention: (total_nnz, dim)
        """
        qkv = self.Wqkv(hidden_states)
        qkv = pad_input(qkv, indices, cu_seqlens.shape[0] - 1,
                        max_seqlen_in_batch)  # batch, max_seqlen_in_batch, thd
        qkv = rearrange(qkv,
                        'b s (t h d) -> b s t h d',
                        t=3,
                        h=self.num_attention_heads)
        if self.p_dropout or flash_attn_qkvpacked_func is None:
            # if we have nonzero attention dropout (e.g. during fine-tuning) or no Triton, compute attention in PyTorch
            q = qkv[:, :, 0, :, :].permute(0, 2, 1, 3)  # b h s d
            k = qkv[:, :, 1, :, :].permute(0, 2, 3, 1)  # b h d s
            v = qkv[:, :, 2, :, :].permute(0, 2, 1, 3)  # b h s d
            attention_scores = torch.matmul(q, k) / math.sqrt(
                self.attention_head_size)
            attention_scores = attention_scores + bias
            attention_probs = nn.functional.softmax(attention_scores, dim=-1)
            attention_probs = self.dropout(attention_probs)
            attention = torch.matmul(attention_probs, v).permute(0, 2, 1,
                                                                 3)  # b s h d
        else:
            # Triton implementation only supports 0 attention dropout
            convert_dtype = qkv.dtype not in [torch.float16, torch.bfloat16]
            if convert_dtype:
                # Triton implementation only supports fp16 and bf16
                orig_dtype = qkv.dtype
                qkv = qkv.to(torch.float16)
                bias_dtype = bias.dtype
                bias = bias.to(torch.float16)
                attention = flash_attn_qkvpacked_func(qkv, bias)
                attention = attention.to(orig_dtype)
                bias = bias.to(bias_dtype)
            else:
                attention = flash_attn_qkvpacked_func(qkv, bias)

        # attn_mask is 1 for attend and 0 for don't
        attention = unpad_input_only(attention, torch.squeeze(attn_mask) == 1)
        return rearrange(attention, 'nnz h d -> nnz (h d)')


# Copy of transformer's library BertSelfOutput that will not be caught by surgery methods looking for HF BERT modules.
class BertSelfOutput(nn.Module):
    """Computes the output of the attention layer.

    This module is modeled after the Hugging Face BERT's
    :class:`~transformers.model.bert.modeling_bert.BertSelfOutput`.
    The implementation is identical. Rather than use the original module
    directly, we re-implement it here so that Mosaic BERT's modules will not
    be affected by any Composer surgery algorithm that modifies Hugging Face
    BERT modules.
    """

    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size,
                                      eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, hidden_states: torch.Tensor,
                input_tensor: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states


class BertUnpadAttention(nn.Module):
    """Chains attention, Dropout, and LayerNorm for Mosaic BERT."""

    def __init__(self, config):
        super().__init__()
        self.self = BertUnpadSelfAttention(config)
        self.output = BertSelfOutput(config)

    def forward(
        self,
        input_tensor: torch.Tensor,
        cu_seqlens: torch.Tensor,
        max_s: int,
        subset_idx: Optional[torch.Tensor] = None,
        indices: Optional[torch.Tensor] = None,
        attn_mask: Optional[torch.Tensor] = None,
        bias: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        """Forward pass for scaled self-attention without padding.

        Arguments:
            input_tensor: (total_nnz, dim)
            cu_seqlens: (batch + 1,)
            max_s: int
            subset_idx: () set of indices whose values we care about at the end of the layer
                        (e.g., the masked tokens, if this is the final layer).
            indices: None or (total_nnz,)
            attn_mask: None or (batch, max_seqlen_in_batch)
            bias: None or (batch, heads, max_seqlen_in_batch, max_seqlen_in_batch)
        """
        self_output = self.self(input_tensor, cu_seqlens, max_s, indices,
                                attn_mask, bias)
        if subset_idx is not None:
            return self.output(index_first_axis(self_output, subset_idx),
                               index_first_axis(input_tensor, subset_idx))
        else:
            return self.output(self_output, input_tensor)


class BertGatedLinearUnitMLP(nn.Module):
    """Applies the FFN at the end of each Mosaic BERT layer.

    Compared to the default BERT architecture, this block replaces :class:`~transformers.model.bert.modeling_bert.BertIntermediate`
    and :class:`~transformers.model.bert.modeling_bert.SelfOutput` with a single module that has similar functionality, but
    introduces Gated Linear Units.

    Note: Mosaic BERT adds parameters in order to implement Gated Linear Units. To keep parameter count consistent with that of a
    standard Hugging Face BERT, scale down `config.intermediate_size` by 2/3. For example, a Mosaic BERT constructed with
    `config.intermediate_size=2048` will have the same parameter footprint as its Hugging Face BERT counterpart constructed
    with the `config.intermediate_size=3072`.
    However, in most cases it will not be necessary to adjust `config.intermediate_size` since, despite the increased
    parameter size, Mosaic BERT typically offers a net higher throughput than a Hugging Face BERT built from the same `config`.
    """

    def __init__(self, config):
        super().__init__()
        self.config = config
        self.gated_layers = nn.Linear(config.hidden_size,
                                      config.intermediate_size * 2,
                                      bias=False)
        self.act = nn.GELU(approximate='none')
        self.wo = nn.Linear(config.intermediate_size, config.hidden_size)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.layernorm = nn.LayerNorm(config.hidden_size,
                                      eps=config.layer_norm_eps)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        """Compute new hidden states from current hidden states.

        Args:
            hidden_states (torch.Tensor): The (unpadded) hidden states from
                the attention layer [nnz, dim].
        """
        residual_connection = hidden_states
        # compute the activation
        hidden_states = self.gated_layers(hidden_states)
        gated = hidden_states[:, :self.config.intermediate_size]
        non_gated = hidden_states[:, self.config.intermediate_size:]
        hidden_states = self.act(gated) * non_gated
        hidden_states = self.dropout(hidden_states)
        # multiply by the second matrix
        hidden_states = self.wo(hidden_states)
        # add the residual connection and post-LN
        hidden_states = self.layernorm(hidden_states + residual_connection)
        return hidden_states


class BertLayer(nn.Module):
    """Composes the Mosaic BERT attention and FFN blocks into a single layer."""

    def __init__(self, config):
        super(BertLayer, self).__init__()
        self.attention = BertUnpadAttention(config)
        self.mlp = BertGatedLinearUnitMLP(config)

    def forward(
        self,
        hidden_states: torch.Tensor,
        cu_seqlens: torch.Tensor,
        seqlen: int,
        subset_idx: Optional[torch.Tensor] = None,
        indices: Optional[torch.Tensor] = None,
        attn_mask: Optional[torch.Tensor] = None,
        bias: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        """Forward pass for a BERT layer, including both attention and MLP.

        Args:
            hidden_states: (total_nnz, dim)
            cu_seqlens: (batch + 1,)
            seqlen: int
            subset_idx: () set of indices whose values we care about at the end of the layer
                        (e.g., the masked tokens, if this is the final layer).
            indices: None or (total_nnz,)
            attn_mask: None or (batch, max_seqlen_in_batch)
            bias: None or (batch, heads, max_seqlen_in_batch, max_seqlen_in_batch)
        """
        attention_output = self.attention(hidden_states, cu_seqlens, seqlen,
                                          subset_idx, indices, attn_mask, bias)
        layer_output = self.mlp(attention_output)
        return layer_output


class BertEncoder(nn.Module):
    """A stack of BERT layers providing the backbone of Mosaic BERT.

    This module is modeled after the Hugging Face BERT's :class:`~transformers.model.bert.modeling_bert.BertEncoder`,
    but with substantial modifications to implement unpadding and ALiBi.

    Compared to the analogous Hugging Face BERT module, this module handles unpadding to reduce unnecessary computation
    at padded tokens, and pre-computes attention biases to implement ALiBi.
    """

    def __init__(self, config):
        super().__init__()
        layer = BertLayer(config)
        self.layer = nn.ModuleList(
            [copy.deepcopy(layer) for _ in range(config.num_hidden_layers)])

        self.num_attention_heads = config.num_attention_heads

        # The alibi mask will be dynamically expanded if it is too small for
        # the input the model receives. But it generally helps to initialize it
        # to a reasonably large size to help pre-allocate CUDA memory.
        # The default `alibi_starting_size` is 512.
        self._current_alibi_size = int(config.alibi_starting_size)
        self.alibi = torch.zeros(
            (1, self.num_attention_heads, self._current_alibi_size,
             self._current_alibi_size))
        self.rebuild_alibi_tensor(size=config.alibi_starting_size)

    def rebuild_alibi_tensor(self,
                             size: int,
                             device: Optional[Union[torch.device, str]] = None):
        # Alibi
        # Following https://github.com/ofirpress/attention_with_linear_biases/issues/5 (Implementation 1)
        # In the causal case, you can exploit the fact that softmax is invariant to a uniform translation
        # of the logits, which makes the math work out *after* applying causal masking. If no causal masking
        # will be applied, it is necessary to construct the diagonal mask.
        n_heads = self.num_attention_heads

        def _get_alibi_head_slopes(n_heads: int) -> List[float]:

            def get_slopes_power_of_2(n_heads: int) -> List[float]:
                start = (2**(-2**-(math.log2(n_heads) - 3)))
                ratio = start
                return [start * ratio**i for i in range(n_heads)]

            # In the paper, they only train models that have 2^a heads for some a. This function
            # has some good properties that only occur when the input is a power of 2. To
            # maintain that even when the number of heads is not a power of 2, we use a
            # workaround.
            if math.log2(n_heads).is_integer():
                return get_slopes_power_of_2(n_heads)

            closest_power_of_2 = 2**math.floor(math.log2(n_heads))
            slopes_a = get_slopes_power_of_2(closest_power_of_2)
            slopes_b = _get_alibi_head_slopes(2 * closest_power_of_2)
            slopes_b = slopes_b[0::2][:n_heads - closest_power_of_2]
            return slopes_a + slopes_b

        context_position = torch.arange(size, device=device)[:, None]
        memory_position = torch.arange(size, device=device)[None, :]
        relative_position = torch.abs(memory_position - context_position)
        # [n_heads, max_token_length, max_token_length]
        relative_position = relative_position.unsqueeze(0).expand(
            n_heads, -1, -1)
        slopes = torch.Tensor(_get_alibi_head_slopes(n_heads)).to(device)
        alibi = slopes.unsqueeze(1).unsqueeze(1) * -relative_position
        # [1, n_heads, max_token_length, max_token_length]
        alibi = alibi.unsqueeze(0)
        assert alibi.shape == torch.Size([1, n_heads, size, size])

        self._current_alibi_size = size
        self.alibi = alibi

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor,
        output_all_encoded_layers: Optional[bool] = True,
        subset_mask: Optional[torch.Tensor] = None,
    ) -> List[torch.Tensor]:

        extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
        extended_attention_mask = extended_attention_mask.to(
            dtype=next(self.parameters()).dtype)  # fp16 compatibility
        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0

        attention_mask_bool = attention_mask.bool()
        batch, seqlen = hidden_states.shape[:2]
        # Unpad inputs and mask. It will remove tokens that are padded.
        # Assume ntokens is total number of tokens (padded and non-padded)
        # and ntokens_unpad is total number of non-padded tokens.
        # Then unpadding performs the following compression of the inputs:
        # hidden_states[ntokens,hidden] -> hidden_states[ntokens_unpad,hidden]
        hidden_states, indices, cu_seqlens, _ = unpad_input(
            hidden_states, attention_mask_bool)

        # Add alibi matrix to extended_attention_mask
        if self._current_alibi_size < seqlen:
            # Rebuild the alibi tensor when needed
            warnings.warn(
                f'Increasing alibi size from {self._current_alibi_size} to {seqlen}'
            )
            self.rebuild_alibi_tensor(size=seqlen, device=hidden_states.device)
        elif self.alibi.device != hidden_states.device:
            # Device catch-up
            self.alibi = self.alibi.to(hidden_states.device)
        alibi_bias = self.alibi[:, :, :seqlen, :seqlen]
        attn_bias = extended_attention_mask[:, :, :seqlen, :seqlen]
        alibi_attn_mask = attn_bias + alibi_bias

        all_encoder_layers = []
        if subset_mask is None:
            for layer_module in self.layer:
                hidden_states = layer_module(hidden_states,
                                             cu_seqlens,
                                             seqlen,
                                             None,
                                             indices,
                                             attn_mask=attention_mask,
                                             bias=alibi_attn_mask)
                if output_all_encoded_layers:
                    all_encoder_layers.append(hidden_states)
            # Pad inputs and mask. It will insert back zero-padded tokens.
            # Assume ntokens is total number of tokens (padded and non-padded)
            # and ntokens_unpad is total number of non-padded tokens.
            # Then padding performs the following de-compression:
            #     hidden_states[ntokens_unpad,hidden] -> hidden_states[ntokens,hidden]
            hidden_states = pad_input(hidden_states, indices, batch, seqlen)
        else:
            for i in range(len(self.layer) - 1):
                layer_module = self.layer[i]
                hidden_states = layer_module(hidden_states,
                                             cu_seqlens,
                                             seqlen,
                                             None,
                                             indices,
                                             attn_mask=attention_mask,
                                             bias=alibi_attn_mask)
                if output_all_encoded_layers:
                    all_encoder_layers.append(hidden_states)
            subset_idx = torch.nonzero(subset_mask[attention_mask_bool],
                                       as_tuple=False).flatten()
            hidden_states = self.layer[-1](hidden_states,
                                           cu_seqlens,
                                           seqlen,
                                           subset_idx=subset_idx,
                                           indices=indices,
                                           attn_mask=attention_mask,
                                           bias=alibi_attn_mask)

        if not output_all_encoded_layers:
            all_encoder_layers.append(hidden_states)
        return all_encoder_layers


class BertPooler(nn.Module):

    def __init__(self, config):
        super(BertPooler, self).__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.activation = nn.Tanh()

    def forward(self,
                hidden_states: torch.Tensor,
                pool: Optional[bool] = True) -> torch.Tensor:
        # We "pool" the model by simply taking the hidden state corresponding
        # to the first token.
        first_token_tensor = hidden_states[:, 0] if pool else hidden_states
        pooled_output = self.dense(first_token_tensor)
        pooled_output = self.activation(pooled_output)
        return pooled_output


class BertPredictionHeadTransform(nn.Module):

    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        if isinstance(config.hidden_act, str):
            self.transform_act_fn = ACT2FN[config.hidden_act]
        else:
            self.transform_act_fn = config.hidden_act
        self.LayerNorm = torch.nn.LayerNorm(config.hidden_size, eps=1e-12)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.transform_act_fn(hidden_states)
        hidden_states = self.LayerNorm(hidden_states)
        return hidden_states


class BertModel(BertPreTrainedModel):
    """Overall BERT model.

    Args:
        config: a BertConfig class instance with the configuration to build a new model

    Inputs:
        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
            with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
            a `sentence B` token (see BERT paper for more details).
        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
            input sequence length in the current batch. It's the mask that we typically use for attention when
            a batch has varying length sentences.
        `output_all_encoded_layers`: boolean which controls the content of the `encoded_layers` output as described below. Default: `True`.

    Outputs: Tuple of (encoded_layers, pooled_output)
        `encoded_layers`: controlled by `output_all_encoded_layers` argument:
            - `output_all_encoded_layers=True`: outputs a list of the full sequences of encoded-hidden-states at the end
                of each attention block (i.e. 12 full sequences for BERT-base, 24 for BERT-large), each
                encoded-hidden-state is a torch.FloatTensor of size [batch_size, sequence_length, hidden_size],
            - `output_all_encoded_layers=False`: outputs only the full sequence of hidden-states corresponding
                to the last attention block of shape [batch_size, sequence_length, hidden_size],
        `pooled_output`: a torch.FloatTensor of size [batch_size, hidden_size] which is the output of a
            classifier pretrained on top of the hidden state associated to the first character of the
            input (`CLS`) to train on the Next-Sentence task (see BERT's paper).

    Example usage:
    ```python
    # Already been converted into WordPiece token ids
    input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
    input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
    config = modeling.BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
    model = BertModel(config=config)
    all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
    ```
    """

    def __init__(self, config, add_pooling_layer=True):
        super(BertModel, self).__init__(config)
        self.embeddings = BertEmbeddings(config)
        self.encoder = BertEncoder(config)
        self.pooler = BertPooler(config) if add_pooling_layer else None
        self.post_init()

    def get_input_embeddings(self):
        return self.embeddings.word_embeddings

    def set_input_embeddings(self, value):
        self.embeddings.word_embeddings = value

    def forward(
        self,
        input_ids: torch.Tensor,
        token_type_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        output_all_encoded_layers: Optional[bool] = False,
        masked_tokens_mask: Optional[torch.Tensor] = None,
        **kwargs
    ) -> Tuple[Union[List[torch.Tensor], torch.Tensor], Optional[torch.Tensor]]:
        if attention_mask is None:
            attention_mask = torch.ones_like(input_ids)
        if token_type_ids is None:
            token_type_ids = torch.zeros_like(input_ids)

        embedding_output = self.embeddings(input_ids, token_type_ids,
                                           position_ids)

        subset_mask = []
        first_col_mask = []

        if masked_tokens_mask is None:
            subset_mask = None
        else:
            first_col_mask = torch.zeros_like(masked_tokens_mask)
            first_col_mask[:, 0] = True
            subset_mask = masked_tokens_mask | first_col_mask

        encoder_outputs = self.encoder(
            embedding_output,
            attention_mask,
            output_all_encoded_layers=output_all_encoded_layers,
            subset_mask=subset_mask)

        if masked_tokens_mask is None:
            sequence_output = encoder_outputs[-1]
            pooled_output = self.pooler(
                sequence_output) if self.pooler is not None else None
        else:
            # TD [2022-03-01]: the indexing here is very tricky.
            attention_mask_bool = attention_mask.bool()
            subset_idx = subset_mask[attention_mask_bool]  # type: ignore
            sequence_output = encoder_outputs[-1][
                masked_tokens_mask[attention_mask_bool][subset_idx]]
            if self.pooler is not None:
                pool_input = encoder_outputs[-1][
                    first_col_mask[attention_mask_bool][subset_idx]]
                pooled_output = self.pooler(pool_input, pool=False)
            else:
                pooled_output = None

        if not output_all_encoded_layers:
            encoder_outputs = sequence_output

        if self.pooler is not None:
            return encoder_outputs, pooled_output

        return encoder_outputs, None


###################
# Bert Heads
###################
class BertLMPredictionHead(nn.Module):

    def __init__(self, config, bert_model_embedding_weights):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)
        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(bert_model_embedding_weights.size(1),
                                 bert_model_embedding_weights.size(0))
        self.decoder.weight = bert_model_embedding_weights

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states


class BertOnlyMLMHead(nn.Module):

    def __init__(self, config, bert_model_embedding_weights):
        super().__init__()
        self.predictions = BertLMPredictionHead(config,
                                                bert_model_embedding_weights)

    def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
        prediction_scores = self.predictions(sequence_output)
        return prediction_scores


class BertOnlyNSPHead(nn.Module):

    def __init__(self, config):
        super().__init__()
        self.seq_relationship = nn.Linear(config.hidden_size, 2)

    def forward(self, pooled_output: torch.Tensor) -> torch.Tensor:
        seq_relationship_score = self.seq_relationship(pooled_output)
        return seq_relationship_score


#####################
# Various Bert models
#####################


class BertForPreTraining(BertPreTrainedModel):
    #TBD: Coming in Future Commit
    pass


class BertLMHeadModel(BertPreTrainedModel):
    #TBD: Coming in Future Commit
    pass


class BertForMaskedLM(BertPreTrainedModel):

    def __init__(self, config):
        super().__init__(config)

        if config.is_decoder:
            warnings.warn(
                'If you want to use `BertForMaskedLM` make sure `config.is_decoder=False` for '
                'bi-directional self-attention.')

        self.bert = BertModel(config, add_pooling_layer=False)
        self.cls = BertOnlyMLMHead(config,
                                   self.bert.embeddings.word_embeddings.weight)

        # Initialize weights and apply final processing
        self.post_init()

    @classmethod
    def from_composer(cls,
                      pretrained_checkpoint,
                      state_dict=None,
                      cache_dir=None,
                      from_tf=False,
                      config=None,
                      *inputs,
                      **kwargs):
        """Load from pre-trained."""
        model = cls(config, *inputs, **kwargs)
        if from_tf:
            raise ValueError(
                'Mosaic BERT does not support loading TensorFlow weights.')

        state_dict = torch.load(pretrained_checkpoint)
        # If the state_dict was saved after wrapping with `composer.HuggingFaceModel`, it takes on the `model` prefix
        consume_prefix_in_state_dict_if_present(state_dict, prefix='model.')
        missing_keys, unexpected_keys = model.load_state_dict(state_dict,
                                                              strict=False)

        if len(missing_keys) > 0:
            logger.warning(
                f"Found these missing keys in the checkpoint: {', '.join(missing_keys)}"
            )
        if len(unexpected_keys) > 0:
            logger.warning(
                f"Found these unexpected keys in the checkpoint: {', '.join(unexpected_keys)}"
            )

        return model

    def get_output_embeddings(self):
        return self.cls.predictions.decoder

    def set_output_embeddings(self, new_embeddings):
        self.cls.predictions.decoder = new_embeddings

    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        token_type_ids: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        encoder_attention_mask: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.Tensor], MaskedLMOutput]:
        # labels should be a `torch.LongTensor` of shape
        # `(batch_size, sequence_length)`. These are used for computing the
        #  masked language modeling loss.
        #
        # Indices should be in `[-100, 0, ..., config.vocab_size]` (see
        # `input_ids` docstring) Tokens with indices set to `-100` are ignored
        # (masked), the loss is only computed for the tokens with labels in `[0,
        # ..., config.vocab_size]`
        #
        # Prediction scores are only computed for masked tokens and the (bs,
        # seqlen) dimensions are flattened
        if (input_ids is not None) == (inputs_embeds is not None):
            raise ValueError('Must specify either input_ids or input_embeds!')

        if labels is None:
            masked_tokens_mask = None
        else:
            masked_tokens_mask = labels > 0

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

        outputs = self.bert(
            input_ids,
            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,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            masked_tokens_mask=masked_tokens_mask,
        )

        sequence_output = outputs[0]
        prediction_scores = self.cls(sequence_output)

        loss = None
        if labels is not None:
            # Compute loss
            loss_fct = nn.CrossEntropyLoss()
            masked_token_idx = torch.nonzero(labels.flatten() > 0,
                                             as_tuple=False).flatten()
            loss = loss_fct(prediction_scores,
                            labels.flatten()[masked_token_idx])

            assert input_ids is not None, 'Coding error; please open an issue'
            batch, seqlen = input_ids.shape[:2]
            prediction_scores = rearrange(index_put_first_axis(
                prediction_scores, masked_token_idx, batch * seqlen),
                                          '(b s) d -> b s d',
                                          b=batch)

        if not return_dict:
            output = (prediction_scores,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return MaskedLMOutput(
            loss=loss,
            logits=prediction_scores,
            hidden_states=None,
            attentions=None,
        )

    def prepare_inputs_for_generation(self, input_ids: torch.Tensor,
                                      attention_mask: torch.Tensor,
                                      **model_kwargs):
        input_shape = input_ids.shape
        effective_batch_size = input_shape[0]

        #  add a dummy token
        if self.config.pad_token_id is None:
            raise ValueError('The PAD token should be defined for generation')

        attention_mask = torch.cat([
            attention_mask,
            attention_mask.new_zeros((attention_mask.shape[0], 1))
        ],
                                   dim=-1)
        dummy_token = torch.full((effective_batch_size, 1),
                                 self.config.pad_token_id,
                                 dtype=torch.long,
                                 device=input_ids.device)
        input_ids = torch.cat([input_ids, dummy_token], dim=1)

        return {'input_ids': input_ids, 'attention_mask': attention_mask}


class BertForNextSentencePrediction(BertPreTrainedModel):
    #TBD: Push in future commit
    pass


class BertForSequenceClassification(BertPreTrainedModel):
    """Bert Model transformer with a sequence classification/regression head.

    This head is just a linear layer on top of the pooled output. Used for,
    e.g., GLUE tasks.
    """

    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.config = config

        self.bert = BertModel(config)
        classifier_dropout = (config.classifier_dropout
                              if config.classifier_dropout is not None else
                              config.hidden_dropout_prob)
        self.dropout = nn.Dropout(classifier_dropout)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)

        # Initialize weights and apply final processing
        self.post_init()

    @classmethod
    def from_composer(cls,
                      pretrained_checkpoint,
                      state_dict=None,
                      cache_dir=None,
                      from_tf=False,
                      config=None,
                      *inputs,
                      **kwargs):
        """Load from pre-trained."""
        model = cls(config, *inputs, **kwargs)
        if from_tf:
            raise ValueError(
                'Mosaic BERT does not support loading TensorFlow weights.')

        state_dict = torch.load(pretrained_checkpoint)
        # If the state_dict was saved after wrapping with `composer.HuggingFaceModel`, it takes on the `model` prefix
        consume_prefix_in_state_dict_if_present(state_dict, prefix='model.')
        missing_keys, unexpected_keys = model.load_state_dict(state_dict,
                                                              strict=False)

        if len(missing_keys) > 0:
            logger.warning(
                f"Found these missing keys in the checkpoint: {', '.join(missing_keys)}"
            )
        if len(unexpected_keys) > 0:
            logger.warning(
                f"Found these unexpected keys in the checkpoint: {', '.join(unexpected_keys)}"
            )

        return model

    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        token_type_ids: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
        # labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
        # Labels for computing the sequence classification/regression loss.
        # Indices should be in `[0, ..., config.num_labels - 1]`.
        # If `config.num_labels == 1` a regression loss is computed
        # (mean-square loss). If `config.num_labels > 1` a classification loss
        # is computed (cross-entropy).

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

        outputs = self.bert(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        pooled_output = outputs[1]

        pooled_output = self.dropout(pooled_output)
        logits = self.classifier(pooled_output)

        loss = None
        if labels is not None:
            # Compute loss
            if self.config.problem_type is None:
                if self.num_labels == 1:
                    self.config.problem_type = 'regression'
                elif self.num_labels > 1 and (labels.dtype == torch.long or
                                              labels.dtype == torch.int):
                    self.config.problem_type = 'single_label_classification'
                else:
                    self.config.problem_type = 'multi_label_classification'

            if self.config.problem_type == 'regression':
                loss_fct = nn.MSELoss()
                if self.num_labels == 1:
                    loss = loss_fct(logits.squeeze(), labels.squeeze())
                else:
                    loss = loss_fct(logits, labels)
            elif self.config.problem_type == 'single_label_classification':
                loss_fct = nn.CrossEntropyLoss()
                loss = loss_fct(logits.view(-1, self.num_labels),
                                labels.view(-1))
            elif self.config.problem_type == 'multi_label_classification':
                loss_fct = nn.BCEWithLogitsLoss()
                loss = loss_fct(logits, labels)

        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return SequenceClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=None,
            attentions=None,
        )


class BertForMultipleChoice(BertPreTrainedModel):
    #TBD: Push in future commit
    pass


class BertForTokenClassification(BertPreTrainedModel):
    #TBD: Push in future commit
    pass


class BertForQuestionAnswering(BertPreTrainedModel):
    """Bert Model with a span classification head.

    This is used for extractive question-answering tasks like SQuAD (a linear
    layers on top of the hidden states' output to compute `span start logits`
    and `span end logits`).
    """
    #TBD: Push in future commit