File size: 32,606 Bytes
9ad81d2
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# timm: https://github.com/rwightman/pytorch-image-models/tree/master/timm
# DeiT: https://github.com/facebookresearch/deit
# --------------------------------------------------------

from functools import partial

import torch
from torch._C import Value
import torch.nn as nn
import numpy as np

from timm.models.vision_transformer import PatchEmbed, Block
from transformers import EncoderDecoderModel, BertTokenizer, AutoTokenizer


from torch import einsum, nn
import torch.nn.functional as F
from einops import rearrange, repeat

import torch
import torch.nn as nn
import torch.nn.functional as F

class FocalLoss(nn.CrossEntropyLoss):
    ''' Focal loss for classification tasks on imbalanced datasets '''

    def __init__(self, gamma=1.0, alpha=None, ignore_index=-100, reduction='none'):
        super().__init__(weight=alpha, ignore_index=ignore_index, reduction='none')
        self.reduction = reduction
        self.gamma = gamma

    def forward(self, input_, target):
        cross_entropy = super().forward(input_, target)
        # Temporarily mask out ignore index to '0' for valid gather-indices input.
        # This won't contribute final loss as the cross_entropy contribution
        # for these would be zero.
        target = target * (target != self.ignore_index).long()
        input_prob = torch.gather(F.softmax(input_, 1), 1, target.unsqueeze(1)).squeeze(1)
        loss = torch.pow(1 - input_prob, self.gamma) * cross_entropy
        return torch.mean(loss) if self.reduction == 'mean' \
               else torch.sum(loss) if self.reduction == 'sum' \
               else loss


# helper functions

import math
from functools import reduce

def prob_mask_like(t, prob):
    return torch.zeros_like(t).float().uniform_(0, 1) < prob

def mask_with_tokens(t, token_ids):
    init_no_mask = torch.full_like(t, False, dtype=torch.bool)
    mask = reduce(lambda acc, el: acc | (t == el), token_ids, init_no_mask)
    return mask

def get_mask_subset_with_prob(mask, prob):
    batch, seq_len, device = *mask.shape, mask.device
    max_masked = math.ceil(prob * seq_len)

    num_tokens = mask.sum(dim=-1, keepdim=True)
    mask_excess = (mask.cumsum(dim=-1) > (num_tokens * prob).ceil())
    mask_excess = mask_excess[:, :max_masked]

    rand = torch.rand((batch, seq_len), device=device).masked_fill(~mask, -1e9)
    _, sampled_indices = rand.topk(max_masked, dim=-1)
    sampled_indices = (sampled_indices + 1).masked_fill_(mask_excess, 0)

    new_mask = torch.zeros((batch, seq_len + 1), device=device)
    new_mask.scatter_(-1, sampled_indices, 1)
    return new_mask[:, 1:].bool()


def exists(val):
    return val is not None

def default(val, d):
    return val if exists(val) else d

# normalization
# they use layernorm without bias, something that pytorch does not offer


class LayerNorm(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.gamma = nn.Parameter(torch.ones(dim))
        self.register_buffer("beta", torch.zeros(dim))

    def forward(self, x):
        return F.layer_norm(x, x.shape[-1:], self.gamma, self.beta)

# residual
class Residual(nn.Module):
    def __init__(self, fn):
        super().__init__()
        self.fn = fn

    def forward(self, x, *args, **kwargs):
        return self.fn(x, *args, **kwargs) + x

# rotary positional embedding
# https://arxiv.org/abs/2104.09864
class RotaryEmbedding(nn.Module):
    def __init__(self, dim):
        super().__init__()
        inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
        self.register_buffer("inv_freq", inv_freq)

    def forward(self, max_seq_len, *, device):
        seq = torch.arange(max_seq_len, device=device, dtype=self.inv_freq.dtype)
        freqs = einsum("i , j -> i j", seq, self.inv_freq)
        return torch.cat((freqs, freqs), dim=-1)


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


def apply_rotary_pos_emb(pos, t):
    return (t * pos.cos()) + (rotate_half(t) * pos.sin())


# classic Noam Shazeer paper, except here they use SwiGLU instead of the more popular GELU for gating the feedforward
# https://arxiv.org/abs/2002.05202
class SwiGLU(nn.Module):
    def forward(self, x):
        x, gate = x.chunk(2, dim=-1)
        return F.silu(gate) * x


# parallel attention and feedforward with residual
# discovered by Wang et al + EleutherAI from GPT-J fame
class ParallelTransformerBlock(nn.Module):
    def __init__(self, dim, dim_head=64, heads=8, ff_mult=4, attn_drop_rate=0.0):
        super().__init__()
        self.norm = LayerNorm(dim)

        attn_inner_dim = dim_head * heads
        ff_inner_dim = dim * ff_mult
        self.fused_dims = (attn_inner_dim, dim_head, dim_head, (ff_inner_dim * 2))

        self.heads = heads
        self.scale = dim_head**-0.5
        self.rotary_emb = RotaryEmbedding(dim_head)

        self.fused_attn_ff_proj = nn.Linear(dim, sum(self.fused_dims), bias=False)
        self.attn_out = nn.Linear(attn_inner_dim, dim, bias=False)

        self.ff_out = nn.Sequential(
            SwiGLU(),
            nn.Linear(ff_inner_dim, dim, bias=False)
        )

        self.attn_drop_rate = attn_drop_rate

        # for caching causal mask and rotary embeddings

        self.register_buffer("mask", None, persistent=False)
        self.register_buffer("pos_emb", None, persistent=False)

    def get_mask(self, n, device):
        if self.mask is not None and self.mask.shape[-1] >= n:
            return self.mask[:n, :n]

        mask = torch.ones((n, n), device=device, dtype=torch.bool).triu(1)
        self.register_buffer("mask", mask, persistent=False)
        return mask

    def get_rotary_embedding(self, n, device):
        if self.pos_emb is not None and self.pos_emb.shape[-2] >= n:
            return self.pos_emb[:n]

        pos_emb = self.rotary_emb(n, device=device)
        self.register_buffer("pos_emb", pos_emb, persistent=False)
        return pos_emb

    def forward(self, x, attn_mask=None):
        """
        Performs self attention and feedforward
        einstein notation
        b - batch
        h - heads
        n, i, j - sequence length (base sequence length, source, target)
        d - feature dimension
        """

        n, device, h = x.shape[1], x.device, self.heads
        # pre layernorm
        x = self.norm(x)
        # attention queries, keys, values, and feedforward inner
        q, k, v, ff = self.fused_attn_ff_proj(x).split(self.fused_dims, dim=-1)

        # split heads
        # they use multi-query single-key-value attention, yet another Noam Shazeer paper
        # they found no performance loss past a certain scale, and more efficient decoding obviously
        # https://arxiv.org/abs/1911.02150
        q = rearrange(q, "b n (h d) -> b h n d", h=h)
        # rotary embeddings
        positions = self.get_rotary_embedding(n, device)
        q, k = map(lambda t: apply_rotary_pos_emb(positions, t), (q, k))
        # scale
        q = q * self.scale
        # similarity
        sim = einsum("b h i d, b j d -> b h i j", q, k)
        # causal mask
        causal_mask = self.get_mask(n, device)
        sim = sim.masked_fill(causal_mask, -torch.finfo(sim.dtype).max)

        # extra attention mask - for masking out attention from text CLS token to padding
        if exists(attn_mask):
            attn_mask = rearrange(attn_mask, 'b i j -> b 1 i j')
            sim = sim.masked_fill(~attn_mask, -torch.finfo(sim.dtype).max)

            if self.attn_drop_rate != 0.:
                # import ipdb; ipdb.set_trace()
                drop_ind = sim != -torch.finfo(sim.dtype).max
                dropout_mask = torch.cuda.FloatTensor(*sim[drop_ind].shape).uniform_() > self.attn_drop_rate
                sim[drop_ind] = sim[drop_ind].masked_fill(~dropout_mask, -torch.finfo(sim.dtype).max)

        # attention
        sim = sim - sim.amax(dim=-1, keepdim=True).detach()
        attn = sim.softmax(dim=-1)
        # aggregate values
        out = einsum("b h i j, b j d -> b h i d", attn, v)
        # merge heads
        out = rearrange(out, "b h n d -> b n (h d)")
        return self.attn_out(out) + self.ff_out(ff)

# cross attention - using multi-query + one-headed key / values as in PaLM w/ optional parallel feedforward
class CrossAttention(nn.Module):
    def __init__(
        self,
        dim,
        *,
        context_dim=None,
        dim_head=64,
        heads=8,
        parallel_ff=False,
        ff_mult=4,
        norm_context=False,
        dropout=0.0,
    ):
        super().__init__()
        self.heads = heads
        self.scale = dim_head ** -0.5
        inner_dim = heads * dim_head
        context_dim = default(context_dim, dim)

        self.norm = LayerNorm(dim)
        self.context_norm = LayerNorm(context_dim) if norm_context else nn.Identity()

        self.to_q = nn.Linear(dim, inner_dim, bias=False)
        self.to_kv = nn.Linear(context_dim, dim_head * 2, bias=False)
        self.to_out = nn.Linear(inner_dim, dim, bias=False)

        self.dropout = dropout

        # whether to have parallel feedforward
        ff_inner_dim = ff_mult * dim

        self.ff = nn.Sequential(
            nn.Linear(dim, ff_inner_dim * 2, bias=False),
            SwiGLU(),
            nn.Linear(ff_inner_dim, dim, bias=False)
        ) if parallel_ff else None

    def forward(self, x, context):
        """
        Use text and query, and image as kv
        einstein notation
        b - batch
        h - heads
        n, i, j - sequence length (base sequence length, source, target)
        d - feature dimension
        """

        # pre-layernorm, for queries and context
        x = self.norm(x)
        context = self.context_norm(context)
        # get queries
        q = self.to_q(x)
        q = rearrange(q, 'b n (h d) -> b h n d', h = self.heads)
        # scale
        q = q * self.scale
        # get key / values
        k, v = self.to_kv(context).chunk(2, dim=-1)
        # query / key similarity
        sim = einsum('b h i d, b j d -> b h i j', q, k)
        
        # dropout
        if self.training:
            dropout_mask = torch.cuda.FloatTensor(*sim.shape).uniform_() > self.dropout
            sim = sim.masked_fill(~dropout_mask, -torch.finfo(sim.dtype).max)

        # attention
        sim = sim - sim.amax(dim=-1, keepdim=True)
        attn = sim.softmax(dim=-1)
        # aggregate
        out = einsum('b h i j, b j d -> b h i d', attn, v)
        # merge and combine heads
        out = rearrange(out, 'b h n d -> b n (h d)')
        out = self.to_out(out)
        # add parallel feedforward (for multimodal layers)
        if exists(self.ff):
            out = out + self.ff(x)
        return out



def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):
    """
    grid_size: int of the grid height and width
    return:
    pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
    """
    grid_h = np.arange(grid_size, dtype=np.float32)
    grid_w = np.arange(grid_size, dtype=np.float32)
    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
    grid = np.stack(grid, axis=0)

    grid = grid.reshape([2, 1, grid_size, grid_size])
    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
    if cls_token:
        pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)
    return pos_embed

def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
    assert embed_dim % 2 == 0

    # use half of dimensions to encode grid_h
    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])  # (H*W, D/2)
    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])  # (H*W, D/2)

    emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
    return emb

def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
    """
    embed_dim: output dimension for each position
    pos: a list of positions to be encoded: size (M,)
    out: (M, D)
    """
    assert embed_dim % 2 == 0
    omega = np.arange(embed_dim // 2, dtype=np.float)
    omega /= embed_dim / 2.
    omega = 1. / 10000**omega  # (D/2,)

    pos = pos.reshape(-1)  # (M,)
    out = np.einsum('m,d->md', pos, omega)  # (M, D/2), outer product

    emb_sin = np.sin(out) # (M, D/2)
    emb_cos = np.cos(out) # (M, D/2)

    emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
    return emb

class MaskedAutoencoderViT(nn.Module):
    """ Masked Autoencoder with VisionTransformer backbone
    """
    def __init__(self, img_size=224, patch_size=16, in_chans=3,
                 embed_dim=1024, depth=24, num_heads=16,
                 decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16,
                 mlp_ratio=4., norm_layer=nn.LayerNorm, norm_pix_loss=True,
                 unimodal_depth=2, multimodal_depth=8, dim_head=64,heads=8,
                 ff_mult=4, extract_multi_level=False, use_focal_loss=False, focal_gamma=1.0,
                 less_u=False, use_weak_negative=False, use_label_smooth=False, ls_coef=0.1,
                 use_maximum_entropy=False, ce_additional=False, use_word_weights=False, use_token_pos=False,
                 use_expect_k=False, use_top_k=False, mae_decoder_caption=False, decoder_slot_depth=2, disable_decoder_vis_token_grad=False,
                 cross_attn_dropout=0.0, predict_next_k_words=False, next_k=3, masked_text=False, masked_text_ratio=0.25, text_length=70,
                 projector_layer=0, uni_dim=1024, uni_dim_head=64, uni_heads=8, uni_ff_mult=4, text_drop_attn=0.):
        super().__init__()

        # --------------------------------------------------------------------------
        # MAE encoder specifics
        self.patch_embed = PatchEmbed(img_size, patch_size, in_chans, embed_dim)
        num_patches = self.patch_embed.num_patches

        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim), requires_grad=False)  # fixed sin-cos embedding

        self.blocks = nn.ModuleList([
            Block(embed_dim, num_heads, mlp_ratio, qkv_bias=True, norm_layer=norm_layer)
            for i in range(depth)])
        self.norm = norm_layer(embed_dim)
        # --------------------------------------------------------------------------

        # --------------------------------------------------------------------------
        # MAE decoder specifics
        self.decoder_embed = nn.Linear(embed_dim, decoder_embed_dim, bias=True)

        self.mask_token = nn.Parameter(torch.zeros(1, 1, decoder_embed_dim))

        self.decoder_pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, decoder_embed_dim), requires_grad=False)  # fixed sin-cos embedding

        self.mae_decoder_depth = decoder_depth
        self.mae_decoder_caption = mae_decoder_caption
        self.decoder_blocks = nn.ModuleList([
            Block(decoder_embed_dim, decoder_num_heads, mlp_ratio, qkv_bias=True, norm_layer=norm_layer)
            for i in range(decoder_depth)])

        if self.mae_decoder_caption:

            self.decoder_slot_layers = nn.ModuleList([])
            for _ in range(decoder_slot_depth):
                self.decoder_slot_layers.append(
                    Residual(CrossAttention(dim=decoder_embed_dim, dim_head=dim_head, heads=heads, parallel_ff=True, ff_mult=ff_mult,)),
                    # Residual(CrossAttention(dim=decoder_embed_dim, dim_head=dim_head, heads=heads, parallel_ff=True, ff_mult=ff_mult,))
                )
            self.decoder_caption_proj = nn.Linear(decoder_embed_dim, embed_dim)
            self.disable_decoder_vis_token_grad = disable_decoder_vis_token_grad

        self.decoder_norm = norm_layer(decoder_embed_dim)
        self.decoder_pred = nn.Linear(decoder_embed_dim, patch_size**2 * in_chans, bias=True) # encoder to decoder
        # --------------------------------------------------------------------------

        self.norm_pix_loss = norm_pix_loss

        # --------------------------------------------------------------------------
        # captioner specifics
        # unimodal layer is for text tokens.
        # multimodal layer is for text to query from image.        
        self.tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased", 
                                                cache_dir='/disk/scratch_fast/bingchen/.cache/torch/hub/checkpoints/bert-base-uncased', local_files_only=True)
        
        # token embeddings
        # NOTE: +1 for mask token used by MLM objective
        # self.token_emb = nn.Embedding(len(self.tokenizer.vocab) + 1, uni_dim)

        self.token_emb = nn.Embedding(len(self.tokenizer.vocab), uni_dim)
        self.text_cls_token = nn.Parameter(torch.randn(uni_dim))

        self.embed_dim = embed_dim
        self.uni_dim = uni_dim
        
        #import ipdb; ipdb.set_trace()
        # unimodal layers
        # TODO: search on the four parameters
        # uni_dim=1024, uni_dim_head=64, uni_heads=8, uni_ff_mult=4
        self.text_drop_attn = text_drop_attn
        self.unimodal_layers = nn.ModuleList([])
        for _ in range(unimodal_depth):
            self.unimodal_layers.append(
                Residual(ParallelTransformerBlock(dim=uni_dim, dim_head=uni_dim_head, 
                        heads=uni_heads, ff_mult=uni_ff_mult, attn_drop_rate=self.text_drop_attn)),
            )

        self.need_uni_2_mul_proj = False
        if uni_dim != embed_dim:
            self.need_uni_2_mul_proj = True
            self.uni_2_mul_proj = nn.Linear(uni_dim, embed_dim)
        
        # multimodal layers
        self.multimodal_layers = nn.ModuleList([])
        self.less_u = less_u
        if less_u:
            for _ in range(multimodal_depth):
                self.multimodal_layers.append(nn.ModuleList([
                    Residual(CrossAttention(dim=embed_dim, dim_head=dim_head, heads=heads, parallel_ff=True, ff_mult=ff_mult, dropout=cross_attn_dropout)),
                    Residual(CrossAttention(dim=embed_dim, dim_head=dim_head, heads=heads, parallel_ff=True, ff_mult=ff_mult, dropout=cross_attn_dropout))
                ]))
        else:
            for _ in range(multimodal_depth):
                self.multimodal_layers.append(nn.ModuleList([
                    Residual(ParallelTransformerBlock(dim=embed_dim, dim_head=dim_head, heads=heads, ff_mult=ff_mult)),
                    Residual(CrossAttention(dim=embed_dim, dim_head=dim_head, heads=heads, parallel_ff=True, ff_mult=ff_mult, dropout=cross_attn_dropout))
                ]))

        # to logits: for softmax caption loss
        self.to_logits = nn.Sequential(
            LayerNorm(embed_dim),
            nn.Linear(embed_dim, len(self.tokenizer.vocab), bias=False)
        )

        self.ce_additional = ce_additional
        if ce_additional:
            # to logits: for other losses
            self.to_logits_1 = nn.Sequential(
                LayerNorm(embed_dim),
                nn.Linear(embed_dim, len(self.tokenizer.vocab), bias=False)
            )
        
        nn.init.normal_(self.token_emb.weight, std=0.02)

        self.pad_id = 0
        self.cls_id = 101
        self.sep_id = 102

        self.logsoftmax = nn.LogSoftmax(dim=1)

        self.extract_multi_level = extract_multi_level
        if self.extract_multi_level:
            self.projectors = nn.ModuleList([nn.Sequential(
                nn.Linear(embed_dim, embed_dim // 2),
                nn.GELU(),
                nn.Linear(embed_dim // 2, embed_dim),
                norm_layer(embed_dim)
            ) for _ in [2, 5, 8,]])
        # --------------------------------------------------------------------------
        
        self.use_focal_loss = use_focal_loss
        
        self.use_weak_negative = use_weak_negative
        self.use_label_smooth = use_label_smooth
        self.ls_coef = ls_coef
        self.use_entropy = use_maximum_entropy
        self.use_word_weights = use_word_weights
        self.use_token_pos = use_token_pos

        self.predict_next_k_words = predict_next_k_words
        self.next_k = next_k
        self.pad = torch.nn.ReplicationPad1d((0, self.next_k-1))

        self.use_expect_k = use_expect_k
        self.use_top_k = use_top_k

        if self.use_word_weights or self.use_token_pos:
            self.focal_loss = FocalLoss(ignore_index=self.pad_id, gamma=focal_gamma, reduction='none')
        else:
            self.focal_loss = FocalLoss(ignore_index=self.pad_id, gamma=focal_gamma, reduction='mean')

        self.masked_text = masked_text
        self.masked_text_ratio = masked_text_ratio
        # self.text_mask_token = nn.Parameter(torch.randn(embed_dim))
        self.mask_token_id = len(self.tokenizer.vocab)

        # self.text_position_embed = nn.Parameter(torch.zeros(1, text_length, embed_dim), requires_grad=False)
        self.text_length = text_length

        self.latent_projector_layer = projector_layer
        if self.latent_projector_layer != 0:
            self.latent_projector = [
                nn.Linear(embed_dim, embed_dim),
                nn.ReLU()
            ] * (self.latent_projector_layer - 1)
            self.latent_projector.append(nn.Linear(embed_dim, embed_dim))

            self.latent_projector = nn.Sequential(*self.latent_projector)


        self.initialize_weights()


    def initialize_weights(self):
        # initialization
        # initialize (and freeze) pos_embed by sin-cos embedding
        pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.patch_embed.num_patches**.5), cls_token=True)
        self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))

        decoder_pos_embed = get_2d_sincos_pos_embed(self.decoder_pos_embed.shape[-1], int(self.patch_embed.num_patches**.5), cls_token=True)
        self.decoder_pos_embed.data.copy_(torch.from_numpy(decoder_pos_embed).float().unsqueeze(0))

        # text_pos_embed = get_1d_sincos_pos_embed_from_grid(self.embed_dim, )
        # torch.nn.init.xavier_normal_(self.text_position_embed) # learnable text position embedding

        # initialize patch_embed like nn.Linear (instead of nn.Conv2d)
        w = self.patch_embed.proj.weight.data
        torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1]))

        # timm's trunc_normal_(std=.02) is effectively normal_(std=0.02) as cutoff is too big (2.)
        torch.nn.init.normal_(self.cls_token, std=.02)
        torch.nn.init.normal_(self.mask_token, std=.02)
        # torch.nn.init.normal_(self.text_mask_token, std=.02)

        # initialize nn.Linear and nn.LayerNorm
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            # we use xavier_uniform following official JAX ViT:
            torch.nn.init.xavier_uniform_(m.weight)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    def patchify(self, imgs):
        """
        imgs: (N, 3, H, W)
        x: (N, L, patch_size**2 *3)
        """
        p = self.patch_embed.patch_size[0]
        assert imgs.shape[2] == imgs.shape[3] and imgs.shape[2] % p == 0

        h = w = imgs.shape[2] // p
        x = imgs.reshape(shape=(imgs.shape[0], 3, h, p, w, p))
        x = torch.einsum('nchpwq->nhwpqc', x)
        x = x.reshape(shape=(imgs.shape[0], h * w, p**2 * 3))
        return x

    def unpatchify(self, x):
        """
        x: (N, L, patch_size**2 *3)
        imgs: (N, 3, H, W)
        """
        p = self.patch_embed.patch_size[0]
        h = w = int(x.shape[1]**.5)
        assert h * w == x.shape[1]
        
        x = x.reshape(shape=(x.shape[0], h, w, p, p, 3))
        x = torch.einsum('nhwpqc->nchpwq', x)
        imgs = x.reshape(shape=(x.shape[0], 3, h * p, h * p))
        return imgs

    def random_masking(self, x, mask_ratio):
        """
        Perform per-sample random masking by per-sample shuffling.
        Per-sample shuffling is done by argsort random noise.
        x: [N, L, D], sequence
        """
        N, L, D = x.shape  # batch, length, dim
        len_keep = int(L * (1 - mask_ratio))
        
        noise = torch.rand(N, L, device=x.device)  # noise in [0, 1]
        
        # sort noise for each sample
        ids_shuffle = torch.argsort(noise, dim=1)  # ascend: small is keep, large is remove
        ids_restore = torch.argsort(ids_shuffle, dim=1)

        # keep the first subset
        ids_keep = ids_shuffle[:, :len_keep]
        x_masked = torch.gather(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D))

        # generate the binary mask: 0 is keep, 1 is remove
        mask = torch.ones([N, L], device=x.device)
        mask[:, :len_keep] = 0
        # unshuffle to get the binary mask
        mask = torch.gather(mask, dim=1, index=ids_restore)

        return x_masked, mask, ids_restore, ids_keep

    def forward_encoder(self, x, mask_ratio):
        # embed patches
        x = self.patch_embed(x)

        # add pos embed w/o cls token
        x = x + self.pos_embed[:, 1:, :]

        # masking: length -> length * mask_ratio
        x, mask, ids_restore, ids_keep = self.random_masking(x, mask_ratio)

        # append cls token
        cls_token = self.cls_token + self.pos_embed[:, :1, :]
        cls_tokens = cls_token.expand(x.shape[0], -1, -1)
        x = torch.cat((cls_tokens, x), dim=1)

        if self.extract_multi_level:
            multi_level_feats = []
            # apply Transformer blocks
            for blk_idx, blk in enumerate(self.blocks):
                x = blk(x)
                if blk_idx in [2, 5, 8]:
                    multi_level_feats.append(self.projectors[[2,5,8].index(blk_idx)](x))
            x = self.norm(x)
            multi_level_feats.append(x)

            return multi_level_feats, mask, ids_restore


        # apply Transformer blocks
        for blk_idx, blk in enumerate(self.blocks):
            x = blk(x)
        x = self.norm(x)
        
        return x, mask, ids_restore, ids_keep

    def forward_decoder(self, x, ids_restore):
        # embed tokens
        x = self.decoder_embed(x)
        # non_mask_token = x

        # append mask tokens to sequence
        mask_tokens = self.mask_token.repeat(x.shape[0], ids_restore.shape[1] + 1 - x.shape[1], 1)
        x_ = torch.cat([x[:, 1:, :], mask_tokens], dim=1)  # no cls token
        x_ = torch.gather(x_, dim=1, index=ids_restore.unsqueeze(-1).repeat(1, 1, x.shape[2]))  # unshuffle
        x = torch.cat([x[:, :1, :], x_], dim=1)  # append cls token

        # add pos embed
        x = x + self.decoder_pos_embed

        # apply Transformer blocks
        decoder_feat = []
        for idx, blk in enumerate(self.decoder_blocks):
            x = blk(x)
            if idx == self.mae_decoder_depth // 2:
                decoder_feat.append(x)

        x = self.decoder_norm(x)

        # use the output from decoder to do captioning

        # predictor projection
        x = self.decoder_pred(x)

        # remove cls token
        x = x[:, 1:, :]

        return x, decoder_feat

    def forward_loss(self, imgs, pred, mask):
        """
        imgs: [N, 3, H, W]
        pred: [N, L, p*p*3]
        mask: [N, L], 0 is keep, 1 is remove, 
        """
        target = self.patchify(imgs)
        if self.norm_pix_loss:
            mean = target.mean(dim=-1, keepdim=True)
            var = target.var(dim=-1, keepdim=True)
            target = (target - mean) / (var + 1.e-6)**.5

        loss = (pred - target) ** 2
        loss = loss.mean(dim=-1)  # [N, L], mean loss per patch

        loss = (loss * mask).sum() / mask.sum()  # mean loss on removed patches
        return loss

    def embed_text(self, text):
        batch, device = text.shape[0], text.device

        seq = text.shape[1]

        text_tokens = self.token_emb(text)

        # append text cls tokens
        text_cls_tokens = repeat(self.text_cls_token, 'd -> b 1 d', b=batch)
        text_tokens = torch.cat((text_tokens, text_cls_tokens), dim=-2)

        # create specific mask for text cls token at the end
        # to prevent it from attending to padding
        cls_mask = rearrange(text != self.pad_id, 'b j -> b 1 j')
        attn_mask = F.pad(cls_mask, (0, 1, seq, 0), value=True)

        # go through unimodal layers
        for attn_ff in self.unimodal_layers:
            text_tokens = attn_ff(text_tokens, attn_mask=attn_mask)

        if self.need_uni_2_mul_proj:
            text_tokens = self.uni_2_mul_proj(text_tokens)

        # get text cls token
        text_tokens, text_cls_tokens = text_tokens[:, :-1], text_tokens[:, -1]
        return text_tokens

        
    
    def forward(self, imgs, caption_ids=None, attention_mask=None, mask_ratio=0.75, 
                    freeze_bert=False, teacher_forcing=False, caption_only=False,
                    encoder_only=False, word_weights=None, syn_count=None):
        latent, mask, ids_restore, ids_keep = self.forward_encoder(imgs, mask_ratio)

        if not caption_only:
            pred, decoder_feat = self.forward_decoder(latent, ids_restore)  # [N, L, p*p*3]
            mae_loss = self.forward_loss(imgs, pred, mask)
        else:
            mae_loss = 0.

        if self.latent_projector_layer != 0:
            latent = self.latent_projector(latent)

        # latent: visual info: N, L, C
        # caption_ids: N, Len
        text, labels = caption_ids[:, :-1], caption_ids[:, 1:]

        seq = text.shape[1]
        text_tokens = self.embed_text(text) # N, Len, C

        # create specific mask for text cls token at the end
        # to prevent it from attending to padding
        cls_mask = rearrange(text != self.pad_id, 'b j -> b 1 j')
        attn_mask = F.pad(cls_mask, (0, 1, seq, 0), value=True)
        unimodal_text_tokens = text_tokens
        if not self.less_u:
            for attn_ff, cross_attn in self.multimodal_layers:
                text_tokens = attn_ff(text_tokens, attn_mask=attn_mask[:, :-1, :-1])
                text_tokens = cross_attn(text_tokens, latent)
        else:
            # dim, num_head, 
            for cross_attn1, cross_attn2 in self.multimodal_layers:
                text_tokens = cross_attn1(text_tokens, latent)
                text_tokens = cross_attn2(text_tokens, latent)

        logits = self.to_logits(text_tokens) # N, Len, NVocab
        logits = logits.reshape(-1, len(self.tokenizer.vocab))
        labels = labels.reshape(-1)

        caption_loss = F.cross_entropy(logits, labels, ignore_index=self.pad_id,)


        return mae_loss, caption_loss, None



def mae_vit_small_patch16_dec512d8b(**kwargs):
    model = MaskedAutoencoderViT(
        patch_size=16, embed_dim=384, depth=12, num_heads=6,
        decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16,
        mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    return model



def mae_vit_base_patch16_dec512d8b(**kwargs):
    model = MaskedAutoencoderViT(
        patch_size=16, embed_dim=768, depth=12, num_heads=12,
        decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16,
        mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    return model

def mae_vit_large_patch16_dec512d8b(**kwargs):
    model = MaskedAutoencoderViT(
        patch_size=16, embed_dim=1024, depth=24, num_heads=16,
        decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16,
        mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    return model


def mae_vit_huge_patch14_dec512d8b(**kwargs):
    model = MaskedAutoencoderViT(
        patch_size=14, embed_dim=1280, depth=32, num_heads=16,
        decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16,
        mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    return model


# set recommended archs
mae_vit_small_patch16 = mae_vit_small_patch16_dec512d8b
mae_vit_base_patch16 = mae_vit_base_patch16_dec512d8b  # decoder: 512 dim, 8 blocks
mae_vit_large_patch16 = mae_vit_large_patch16_dec512d8b  # decoder: 512 dim, 8 blocks
mae_vit_huge_patch14 = mae_vit_huge_patch14_dec512d8b  # decoder: 512 dim, 8 blocks