File size: 39,799 Bytes
cffa6bd
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
# coding=utf-8
# Copyright 2022 shunxing1234 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch GLM model. """

import math

import torch
import torch.utils.checkpoint
import torch.nn.functional as F
from torch.nn import init, LayerNorm, Linear, CrossEntropyLoss

from transformers.activations import gelu
from transformers.utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
)
from transformers.modeling_outputs import (
    BaseModelOutputWithPastAndCrossAttentions,
    ModelOutput,
    SequenceClassifierOutput,
)

from transformers.modeling_utils import (
    PreTrainedModel,
)
from .configuration_glm import GLMConfig
from torch.nn.parameter import Parameter

_CHECKPOINT_FOR_DOC = "shunxing1234/GLM"
_CONFIG_FOR_DOC = "GLMConfig"
_TOKENIZER_FOR_DOC = "GLMTokenizer"

GLM_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "shunxing1234/GLM",
    # See all GLM models at https://huggingface.co/models?filter=glm
]


def unscaled_init_method(sigma):
    """Init method based on N(0, sigma)."""

    def init_(tensor):
        return torch.nn.init.normal_(tensor, mean=0.0, std=sigma)

    return init_


def scaled_init_method(mean, std, num_layers):
    """Init method based on N(0, sigma/sqrt(2*num_layers)."""
    std = std / math.sqrt(2.0 * num_layers)

    def init_(tensor):
        return torch.nn.init.normal_(tensor, mean=mean, std=std)

    return init_


def ensure_divisibility(numerator, denominator):
    """Ensure that numerator is divisible by the denominator."""
    assert numerator % denominator == 0, '{} is not divisible by {}'.format(
        numerator, denominator)


def divide(numerator, denominator):
    """Ensure that numerator is divisible by the denominator and return
    the division value."""
    ensure_divisibility(numerator, denominator)
    return numerator // denominator


def split_tensor_along_last_dim(tensor, num_partitions,
                                contiguous_split_chunks=False):
    """Split a tensor along its last dimension.
    Arguments:
        tensor: input tensor.
        num_partitions: number of partitions to split the tensor
        contiguous_split_chunks: If True, make each chunk contiguous
                                 in memory.
    """
    # Get the size and dimension.
    last_dim = tensor.dim() - 1
    last_dim_size = divide(tensor.size()[last_dim], num_partitions)
    # Split.
    tensor_list = torch.split(tensor, last_dim_size, dim=last_dim)
    # Note: torch.split does not create contiguous tensors by default.
    if contiguous_split_chunks:
        return tuple(chunk.contiguous() for chunk in tensor_list)

    return tensor_list


class MLP(torch.nn.Module):
    """MLP for GPT2.

    MLP will take the input with h hidden state, project it to 4*h
    hidden dimension, perform gelu transformation, and project the
    state back into h hidden dimension. At the end, dropout is also
    applied.

    Arguments:
        hidden_size: The hidden size of the self attention.
        output_dropout_prob: dropout probability for the outputs
                             after self attention and final output.
        init_method: initialization method used for the weights. Note
                     that all biases are initialized to zero and
                     layernorm weight are initialized to one.
        output_layer_init_method: output layer initialization. If None,
                                  use `init_method`.
    """

    def __init__(self, hidden_size, output_dropout_prob, init_method,
                 output_layer_init_method=None):
        super(MLP, self).__init__()
        # Set output layer initialization if not provided.
        if output_layer_init_method is None:
            output_layer_init_method = init_method
        # Project to 4h.
        self.dense_h_to_4h = Linear(hidden_size, 4 * hidden_size)

        # Project back to h.
        self.dense_4h_to_h = Linear(
            4 * hidden_size,
            hidden_size)

        self.dropout = torch.nn.Dropout(output_dropout_prob)

    def forward(self, hidden_states):
        # [b, s, 4hp]
        intermediate_parallel = self.dense_h_to_4h(hidden_states)
        intermediate_parallel = gelu(intermediate_parallel)

        # [b, s, h]
        output = self.dense_4h_to_h(intermediate_parallel)
        output = self.dropout(output)
        return output


class VocabEmbedding(torch.nn.Module):
    """Embedding parallelized in the vocabulary dimension.

    This is mainly adapted from torch.nn.Embedding and all the default
    values are kept.
    Arguments:
        num_embeddings: vocabulary size.
        embedding_dim: size of hidden state.
        init_method: method to initialize weights.
    """

    def __init__(self, config):
        super(VocabEmbedding, self).__init__()
        # Keep the input dimensions.
        self.num_embeddings = config.vocab_size
        self.embedding_dim = config.hidden_size
        # Set the detauls for compatibility.
        self.padding_idx = None
        self.max_norm = None
        self.norm_type = 2.
        self.scale_grad_by_freq = False
        self.sparse = False
        self._weight = None

        self.vocab_start_index = 0
        self.vocab_end_index = self.num_embeddings

        # Allocate weights.
        self.weight = Parameter(torch.Tensor(self.num_embeddings,
                                             self.embedding_dim))
        # And initialize.
        init.xavier_normal_(self.weight)

    def forward(self, input_):
        # Get the embeddings.
        output = F.embedding(input_, self.weight,
                             self.padding_idx, self.max_norm,
                             self.norm_type, self.scale_grad_by_freq,
                             self.sparse)
        return output


class PositionalEmbedding(torch.nn.Module):

    def __init__(self, hidden_size):
        super(PositionalEmbedding, self).__init__()

        self.hidden_size = hidden_size

        inv_freq = 1 / (10000 ** (torch.arange(0.0, hidden_size, 2.0) / hidden_size))
        self.register_buffer('inv_freq', inv_freq)

    def forward(self, pos_seq, bsz=None):
        sinusoid_inp = torch.ger(pos_seq, self.inv_freq)
        pos_emb = torch.cat([sinusoid_inp.sin(), sinusoid_inp.cos()], dim=-1)

        if bsz is not None:
            return pos_emb[None, :, :].expand(bsz, -1, -1)
        else:
            return pos_emb[None, :, :]


class SelfAttention(torch.nn.Module):
    """self-attention layer for GLM.

    Self-attention layer takes input with size [b, s, h] where b is
    the batch size, s is the sequence lenght, and h is the hidden size
    and creates output of the same size.
    Arguments:
        hidden_size: total hidden size of the layer (h).
        num_attention_heads: number of attention heads (n). Note that we
                             require n to be divisible by number of GPUs
                             used to parallelize the model. Also, we
                             require hidden size to be divisible by n.
        attention_dropout_prob: dropout probability for the attention scores.
        init_method: weight initialization.
        output_layer_init_method: output layer initialization. If None, use
                                  `init_method`.
    We use the following notation:
        h: hidden_size
        n: num_attention_heads
        p: number of partitions
        np: n/p
        hp: h/p
        hn: h/n
        b: batch size
        s: sequence length
    """

    def __init__(self, hidden_size, num_attention_heads,
                 attention_dropout_prob, output_dropout_prob,
                 init_method, output_layer_init_method=None,
                 attention_scale=1.0):
        super(SelfAttention, self).__init__()
        # Set output layer initialization if not provided.
        if output_layer_init_method is None:
            output_layer_init_method = init_method
        # Per attention head and per partition values.
        self.hidden_size = hidden_size
        self.hidden_size_per_attention_head = divide(hidden_size,
                                                     num_attention_heads)

        self.num_attention_heads = num_attention_heads
        self.attention_scale = attention_scale
        # Strided linear layer.
        self.query_key_value = Linear(hidden_size, 3 * hidden_size)

        # Dropout. Note that for a single iteration, this layer will generate
        # different outputs on different number of parallel partitions but
        # on average it should not be partition dependent.
        self.attention_dropout = torch.nn.Dropout(attention_dropout_prob)

        # Output.
        self.dense = Linear(hidden_size,
                            hidden_size)
        self.output_dropout = torch.nn.Dropout(output_dropout_prob)

    def _transpose_for_scores(self, tensor):
        """Transpose a 3D tensor [b, s, np*hn] into a 4D tensor with
        size [b, np, s, hn].
        """
        new_tensor_shape = tensor.size()[:-1] + \
                           (self.num_attention_heads,
                            self.hidden_size_per_attention_head)
        tensor = tensor.view(*new_tensor_shape)
        return tensor.permute(0, 2, 1, 3)

    def forward(self, hidden_states, ltor_mask, mem=None):
        # hidden_states: [b, s, h]
        # ltor_mask: [b,1,s,s]

        # Attention heads. [b, s, hp]
        query_length = hidden_states.size(1)
        # self attention
        if mem is None:
            mixed_x_layer = self.query_key_value(hidden_states)
            (mixed_query_layer,
             mixed_key_layer,
             mixed_value_layer) = split_tensor_along_last_dim(mixed_x_layer, 3)
        else:
            cat = torch.cat((mem, hidden_states), 1)
            mixed_x_layer = self.query_key_value(cat)
            (mixed_query_layer,
             mixed_key_layer,
             mixed_value_layer) = split_tensor_along_last_dim(mixed_x_layer, 3)
            mixed_query_layer = mixed_query_layer[:, -query_length:]

        # Reshape and transpose [b, np, s, hn]
        query_layer = self._transpose_for_scores(mixed_query_layer)
        key_layer = self._transpose_for_scores(mixed_key_layer)
        value_layer = self._transpose_for_scores(mixed_value_layer)

        if self.attention_scale > 1.0:
            # Raw attention scores. [b, np, s, s]
            attention_scores = torch.matmul(query_layer / math.sqrt(self.attention_scale),
                                            key_layer.transpose(-1, -2) / math.sqrt(
                                                self.hidden_size_per_attention_head * self.attention_scale))
        else:
            attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2) / math.sqrt(
                self.hidden_size_per_attention_head))

        # Apply the left to right attention mask.
        ltor_mask = ltor_mask.type_as(attention_scores)
        attention_scores = torch.mul(attention_scores, ltor_mask)
        if self.attention_scale > 1.0:
            max_attention_scores = attention_scores.max(dim=-1, keepdim=True)[0]
            attention_scores -= max_attention_scores
            attention_scores *= self.attention_scale

        attention_scores = attention_scores + (-65504.0) * (1.0 - ltor_mask)
        # Attention probabilities. [b, np, s, s]
        attention_probs = torch.nn.Softmax(dim=-1)(attention_scores)
        # This is actually dropping out entire tokens to attend to, which might
        # seem a bit unusual, but is taken from the original Transformer paper.
        # with get_cuda_rng_tracker().fork():
        attention_probs = self.attention_dropout(attention_probs)

        # Context layer.
        # [b, np, s, hn]
        context_layer = torch.matmul(attention_probs, value_layer)
        # [b, s, np, hn]
        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
        new_context_layer_shape = context_layer.size()[:-2] + \
                                  (self.hidden_size,)
        # [b, s, hp]
        context_layer = context_layer.view(*new_context_layer_shape)

        # Output. [b, s, h]
        output = self.dense(context_layer)
        output = self.output_dropout(output)

        return output


class GLMBlock(torch.nn.Module):
    """A single layer transformer for GLM.

    We use the following notation:
        h: hidden size
        n: number of attention heads
        b: batch size
        s: sequence length
    Transformore layer takes input with size [b, s, h] and returns an
    output of the same size.

    Arguments:
        hidden_size: The hidden size of the self attention.
        num_attention_heads: number of attention head in the self
                             attention.
        attention_dropout_prob: dropout probability of the attention
                                score in self attention.
        output_dropout_prob: dropout probability for the outputs
                             after self attention and final output.
        layernorm_epsilon: epsilon used in layernorm to avoid
                           division by zero.
        init_method: initialization method used for the weights. Note
                     that all biases are initialized to zero and
                     layernorm weight are initialized to one.
        output_layer_init_method: output layers (attention output and
                                  mlp output) initialization. If None,
                                  use `init_method`.
    """

    def __init__(self,
                 hidden_size,
                 num_attention_heads,
                 attention_dropout_prob,
                 output_dropout_prob,
                 layernorm_epsilon,
                 init_method,
                 output_layer_init_method=None,
                 attention_scale=1.0):
        super(GLMBlock, self).__init__()
        # Set output layer initialization if not provided.
        if output_layer_init_method is None:
            output_layer_init_method = init_method

        # Layernorm on the input data.
        self.input_layernorm = LayerNorm(hidden_size, eps=layernorm_epsilon)

        # Self attention.
        self.attention = SelfAttention(
            hidden_size,
            num_attention_heads,
            attention_dropout_prob,
            output_dropout_prob,
            init_method,
            output_layer_init_method=output_layer_init_method,
            attention_scale=attention_scale)

        # Layernorm on the input data.
        self.post_attention_layernorm = LayerNorm(hidden_size,
                                                  eps=layernorm_epsilon)

        # MLP
        self.mlp = MLP(
            hidden_size,
            output_dropout_prob,
            init_method,
            output_layer_init_method=output_layer_init_method)

    def forward(self, hidden_states, ltor_mask, mem=None):
        # hidden_states: [b, s, h]
        # ltor_mask: [b,1, s,s]

        # Layer norm at the begining of the transformer layer.
        layernorm_output = self.input_layernorm(hidden_states)
        mem = self.input_layernorm(mem) if mem is not None else None
        # Self attention.
        attention_output = self.attention(layernorm_output, ltor_mask, mem)
        # Residual connection.
        layernorm_input = hidden_states + attention_output
        # Layer norm post the self attention.
        layernorm_output = self.post_attention_layernorm(layernorm_input)
        # MLP.
        mlp_output = self.mlp(layernorm_output)
        # Second residual connection.
        output = layernorm_input + mlp_output

        return output


class GLMStack(torch.nn.Module):
    """GLM transformer.

    This module takes input from embedding layer and it's output can
    be used directly by a logit layer. It consists of L (num-layers)
    blocks of:
        layer norm
        self attention
        residual connection
        layer norm
        mlp
        residual connection
    followed by a final layer norm.

    Arguments:
        num_layers: Number of transformer layers.
        hidden_size: The hidden size of the self attention.
        num_attention_heads: number of attention head in the self
                             attention.
        attention_dropout_prob: dropout probability of the attention
                                score in self attention.
        output_dropout_prob: dropout probability for the outputs
                             after self attention and final output.
        checkpoint_activations: if True, checkpoint activations.
        checkpoint_num_layers: number of layers to checkpoint. This
                               is basically the chunk size in checkpoitning.
        layernorm_epsilon: epsilon used in layernorm to avoid
                           division by zero.
        init_method_std: standard deviation of the init method which has
                         the form N(0, std).
        use_scaled_init_for_output_weights: If Ture use 1/sqrt(2*num_layers)
                                            scaling for the output weights (
                                            output of self attention and mlp).
    """

    def __init__(self,
                 num_layers,
                 hidden_size,
                 num_attention_heads,
                 max_sequence_length,
                 embedding_dropout_prob,
                 attention_dropout_prob,
                 output_dropout_prob,
                 checkpoint_activations,
                 checkpoint_num_layers=1,
                 layernorm_epsilon=1.0e-5,
                 init_method_std=0.02,
                 use_scaled_init_for_output_weights=True,
                 block_position_encoding=False,
                 attention_scale=1.0,
                 ):
        super(GLMStack, self).__init__()
        self.hidden_size = hidden_size
        # Store activation checkpoiting flag.
        self.checkpoint_activations = checkpoint_activations
        self.checkpoint_num_layers = checkpoint_num_layers

        output_layer_init_method = None
        if use_scaled_init_for_output_weights:
            output_layer_init_method = scaled_init_method(0.0, init_method_std,
                                                          num_layers)
        # Embeddings dropout
        self.embedding_dropout = torch.nn.Dropout(embedding_dropout_prob)
        self.block_position_encoding = block_position_encoding

        # Position embedding (serial).
        if block_position_encoding:
            self.position_embeddings = torch.nn.Embedding(max_sequence_length + 1, hidden_size)
            self.block_position_embeddings = torch.nn.Embedding(max_sequence_length + 1, hidden_size)
            torch.nn.init.normal_(self.block_position_embeddings.weight, mean=0.0, std=init_method_std)
        else:
            self.position_embeddings = torch.nn.Embedding(max_sequence_length, hidden_size)
        # Initialize the position embeddings.
        torch.nn.init.normal_(self.position_embeddings.weight, mean=0.0, std=init_method_std)

        def get_layer():

            return GLMBlock(
                hidden_size,
                num_attention_heads,
                attention_dropout_prob,
                output_dropout_prob,
                layernorm_epsilon,
                unscaled_init_method(init_method_std),
                output_layer_init_method=output_layer_init_method,
                attention_scale=attention_scale)

        # Transformer layers.
        self.layers = torch.nn.ModuleList(
            [get_layer() for _ in range(num_layers)])

        # Final layer norm before output.
        self.final_layernorm = LayerNorm(hidden_size, eps=layernorm_epsilon)

    def forward(self, hidden_states, position_ids, attention_mask, memory_states=None):

        batch_size, query_length = hidden_states.size()[:2]
        memory_length = memory_states[0].size(1) if memory_states else 0
        # attention mask is the beginning postion of B region, \in [0, query_len)
        is_scalar = torch.numel(attention_mask) == 1
        is_sep = is_scalar or torch.numel(attention_mask) == batch_size
        if is_sep:
            sep = attention_mask.item() if is_scalar else attention_mask

            # conventional transformer
            def build_mask_matrix(seq_length, sep, memory_length=0):
                m = hidden_states.new_ones((1, seq_length, seq_length))
                m = torch.tril(m)
                if is_scalar:
                    m[0, :, :int(sep)] = 1
                else:
                    m = m.expand(batch_size, -1, -1)
                    ids = torch.arange(seq_length, device=sep.device, dtype=sep.dtype).view(1, -1)
                    mask = ids < sep.view(-1, 1)
                    m = m.masked_fill(mask.unsqueeze(1).expand_as(m), 1)
                if memory_length > 0:
                    m = m.expand(batch_size, -1, -1)
                    m = torch.cat((hidden_states.new_ones((batch_size, seq_length, memory_length)), m), dim=2)
                m = m.unsqueeze(1)
                return m

            attention_mask = build_mask_matrix(query_length, sep, memory_length=memory_length)
        else:
            if attention_mask.dim() == 2:
                attention_mask = attention_mask.unsqueeze(1).unsqueeze(1)
            attention_mask = attention_mask[:, :, :, -query_length - memory_length:]

        if self.block_position_encoding:
            position_ids, block_position_ids = position_ids[:, 0], position_ids[:, 1]
        position_embeddings = self.position_embeddings(position_ids)

        hidden_states = hidden_states + position_embeddings
        if self.block_position_encoding:
            block_position_embeddings = self.block_position_embeddings(block_position_ids)
            hidden_states = hidden_states + block_position_embeddings
        hidden_states = self.embedding_dropout(hidden_states)

        def check_detach(_hidden_states):
            return _hidden_states.detach()

        mem_layers = [check_detach(hidden_states)]

        for i, layer in enumerate(self.layers):

            args = [hidden_states, attention_mask]

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

                return custom_forward

            mem_i = memory_states[i] if memory_states else None

            if self.checkpoint_activations:
                hidden_states = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(layer),
                    hidden_states,
                    mem=mem_i,
                )
            else:
                hidden_states = layer(*args, mem=mem_i)
            mem_layers.append(check_detach(hidden_states))

        # Final layer norm.
        output = self.final_layernorm(hidden_states)
        mem_layers = self.update_mems(mem_layers, memory_states)
        return (output, mem_layers)

    def update_mems(self, hiddens, mems):
        memory_length = mems[0].size(1) if mems else 0
        query_length = hiddens[0].size(1)
        new_memory_length = memory_length + query_length

        new_mems = []
        # with torch.no_grad():
        for i in range(len(hiddens)):
            if new_memory_length <= query_length:
                new_mems.append(hiddens[i][:, -new_memory_length:])
            else:
                new_mems.append(torch.cat((mems[i][:, -new_memory_length + query_length:], hiddens[i]), dim=1))
        return new_mems


class GLMPreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and
    a simple interface for downloading and loading pretrained models.
    """

    config_class = GLMConfig
    base_model_prefix = "glm"
    supports_gradient_checkpointing = True
    _keys_to_ignore_on_load_missing = [r"position_ids"]

    def _init_weights(self, module):
        """ Initialize the weights """
        if isinstance(module, torch.nn.Linear):
            # Slightly different from the TF version which uses truncated_normal for initialization
            # cf https://github.com/pytorch/pytorch/pull/5617
            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, torch.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, torch.nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)

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


GLM_START_DOCSTRING = r"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general
    usage and behavior.

    Parameters:
        config ([`~GLMConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the configuration.
            Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

GLM_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `({0})`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`GLMTokenizer`].
            See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            [What are attention masks?](../glossary#attention-mask)
        token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`:

            - 0 corresponds to a *sentence A* token,
            - 1 corresponds to a *sentence B* token.

            [What are token type IDs?](../glossary#token-type-ids)
        position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings.
            Selected in the range `[0, config.max_position_embeddings - 1]`.

            [What are position IDs?](../glossary#position-ids)
        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
            This is useful if you want more control over how to convert *input_ids* indices into associated vectors
            than the model's internal embedding lookup matrix.
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""


@add_start_docstrings(
    "The bare GLM Model transformer outputting raw hidden-states without any specific head on top.",
    GLM_START_DOCSTRING,
)
class GLMModel(GLMPreTrainedModel):
    """

    The model can behave as an encoder (with only self-attention) as well
    as a decoder, in which case a layer of cross-attention is added between
    the self-attention layers, following the architecture described in [Attention is
    all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani,
    Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.

    To behave as an decoder the model needs to be initialized with the
    `is_decoder` argument of the configuration set to `True`.
    To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder`
    argument and `add_cross_attention` set to `True`; an
    `encoder_hidden_states` is then expected as an input to the forward pass.
    """

    def __init__(self, config):
        super().__init__(config)
        self.config = config
        self.output_predict = config.output_predict
        # Word embeddings (parallel).
        self.word_embeddings = VocabEmbedding(config)

        # Transformer
        self.transformer = GLMStack(config.num_layers,
                                    config.hidden_size,
                                    config.num_attention_heads,
                                    config.max_sequence_length,
                                    config.embedding_dropout_prob,
                                    config.attention_dropout_prob,
                                    config.output_dropout_prob,
                                    config.checkpoint_activations,
                                    config.checkpoint_num_layers,
                                    attention_scale=config.attention_scale,
                                    block_position_encoding=config.block_position_encoding)

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

    @add_start_docstrings_to_model_forward(GLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=BaseModelOutputWithPastAndCrossAttentions,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
            self,
            input_ids=None,
            position_ids=None,
            attention_mask=None,
            mems=None,
            **kwargs
    ):
        batch_size = input_ids.size(0)
        words_embeddings = self.word_embeddings(input_ids)
        embeddings = words_embeddings

        device = input_ids.device
        input_shape = input_ids.size()

        if position_ids is None:
            position_ids = torch.arange(0, input_shape[-1], dtype=torch.long, device=device)
            block_position_ids = torch.zeros(input_shape[-1], dtype=torch.long, device=device)
            position_ids = torch.stack((position_ids, block_position_ids), dim=0).unsqueeze(0)
        if attention_mask is None:
            attention_mask = torch.zeros(batch_size)
        # Transformer.
        transformer_output = self.transformer(embeddings, position_ids, attention_mask, mems)
        last_hidden_states, mems = transformer_output
        logits = None
        if self.output_predict:
            logits = F.linear(last_hidden_states, self.word_embeddings.weight)

        return ModelOutput(
            last_hidden_states=last_hidden_states,
            logits=logits,
            mems=mems,
        )


@add_start_docstrings(
    """GLM Model transformer for multiple choice classification""",
    GLM_START_DOCSTRING
)
class GLMForMultipleChoice(GLMPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.glm = GLMModel(config)
        self.post_init()

    def forward(
            self,
            input_ids=None,
            position_ids=None,
            attention_mask=None,
            choice_ids=None,
            choice_indices=None,
            labels=None,
            mems=None,
            **kwargs
    ):
        model_output = self.glm(input_ids, position_ids, attention_mask, mems=mems, **kwargs)
        lm_logits = model_output.logits
        log_probs = []
        for output, choices, choice_index in zip(F.log_softmax(lm_logits, dim=-1), choice_ids, choice_indices):
            log_probs_single = []
            for choice, choice_target_id in zip(choices, choice_index):
                tmp = output[choice_target_id, choice]
                log_probs_single.append(tmp.sum())
            log_probs.append(torch.stack(log_probs_single))
        log_probs = torch.stack(log_probs)
        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(log_probs, labels)
        return ModelOutput(
            loss=loss,
            logits=log_probs,
            lm_logits=lm_logits,
            mems=model_output.mems
        )

@add_start_docstrings(
    """GLM Model transformer with a `language modeling` head on top""",
    GLM_START_DOCSTRING,
)
class GLMForConditionalGeneration(GLMPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.glm = GLMModel(config)
        self.post_init()

    def _reorder_cache(self, past, beam_idx):
        # if decoder past is not included in output
        # speedy decoding is disabled and no need to reorder
        if past is None:
            return past
        reordered_decoder_past = ()
        for layer_past_states in past:
            # get the correct batch idx from layer past batch dim
            reordered_decoder_past = reordered_decoder_past + (
                layer_past_states.index_select(0, beam_idx.to(layer_past_states.device)),)
        return reordered_decoder_past

    def prepare_inputs_for_generation(self, input_ids, past=None, position_ids=None, generation_attention_mask=None,
                                      **kwargs):
        # only last token for inputs_ids if past is defined in kwargs
        attention_mask = generation_attention_mask
        seq_length = input_ids.shape[1]
        if past:
            if position_ids is not None:
                position_ids = position_ids[:, :, seq_length - 1].unsqueeze(-1)
            if attention_mask is not None:
                attention_mask = attention_mask[:, :, seq_length - 1, :seq_length].unsqueeze(-2)
            input_ids = input_ids[:, -1].unsqueeze(-1)
        else:
            if position_ids is not None:
                position_ids = position_ids[:, :, :seq_length]
            if attention_mask is not None:
                attention_mask = attention_mask[:, :, :seq_length, :seq_length]
        if position_ids is not None and input_ids.size(0) > position_ids.size(0):
            batch_size = position_ids.size(0)
            num_beams = input_ids.size(0) // batch_size
            position_ids = position_ids.unsqueeze(1).expand(-1, num_beams, -1, -1)
            position_ids = position_ids.reshape(batch_size * num_beams, *position_ids.shape[-2:])
        if attention_mask is not None and input_ids.size(0) > attention_mask.size(0):
            batch_size = attention_mask.size(0)
            num_beams = input_ids.size(0) // batch_size
            attention_mask = attention_mask.unsqueeze(1).expand(-1, num_beams, -1, -1, -1)
            attention_mask = attention_mask.reshape(batch_size * num_beams, *attention_mask.shape[-3:])
        return {
            "input_ids": input_ids,
            "position_ids": position_ids,
            "attention_mask": attention_mask,
            "mems": past,
        }

    def forward(
            self,
            input_ids=None,
            position_ids=None,
            attention_mask=None,
            labels=None,
            mems=None,
            **kwargs
    ):
        model_output = self.glm(input_ids, position_ids, attention_mask, mems=mems, **kwargs)
        lm_logits = model_output.logits
        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss(ignore_index=-100)
            loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1))
        return ModelOutput(
            loss=loss,
            logits=lm_logits,
            mems=model_output.mems
        )


@add_start_docstrings(
    """GLM Model transformer with a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. """,
    GLM_START_DOCSTRING,
)
class GLMForSequenceClassification(GLMPreTrainedModel):
    def __init__(self, config: GLMConfig, hidden_dropout=None, num_class=1):
        super().__init__(config)
        self.pool_token = config.pool_token
        self.glm = GLMModel(config)
        self.glm.output_predict = False
        self.num_class = num_class
        # Multi-choice head.
        self.dense = torch.nn.Linear(config.hidden_size, config.hidden_size)
        classifier_dropout = (
            config.classifier_dropout if config.classifier_dropout is not None else config.output_dropout_prob
        )
        self.dropout = torch.nn.Dropout(classifier_dropout)
        self.out_proj = torch.nn.Linear(config.hidden_size, config.num_labels)

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

    @add_start_docstrings_to_model_forward(GLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=SequenceClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(self,
                input_ids=None,
                position_ids=None,
                attention_mask=None,
                labels=None):

        num_choices = None

        if len(input_ids.shape) == 3:
            batch_size, num_choices = input_ids.shape[:2]
            input_ids = input_ids.reshape(-1, input_ids.size(-1))
            attention_mask = attention_mask.reshape(-1, *attention_mask.size()[2:])
            position_ids = position_ids.reshape(-1, *position_ids.size()[2:])
        model_out = self.glm(input_ids, position_ids, attention_mask)
        outputs, mems = model_out.last_hidden_states, model_out.mems

        output = outputs[:, 0, :]
        output = self.dropout(output)
        output = torch.tanh(self.dense(output))
        output = self.dropout(output)
        logits = self.out_proj(output)
        if num_choices is not None:
            logits = logits.view(-1, num_choices)
        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(logits, labels)
        # loss = F.cross_entropy(logits.contiguous().float(), labels.long())
        return SequenceClassifierOutput(loss=loss,
                                        logits=logits,
                                        hidden_states=outputs)