File size: 27,594 Bytes
b181bc5
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
# --------------------------------------------------------
# WavLM: Large-Scale Self-Supervised  Pre-training  for Full Stack Speech Processing (https://arxiv.org/abs/2110.13900.pdf)
# Github source: https://github.com/microsoft/unilm/tree/master/wavlm
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Based on fairseq code bases
# https://github.com/pytorch/fairseq
# --------------------------------------------------------

import math
import logging
from typing import List, Optional, Tuple

import numpy as np

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import LayerNorm
from vencoder.wavlm.modules import (
    Fp32GroupNorm,
    Fp32LayerNorm,
    GradMultiply,
    MultiheadAttention,
    SamePad,
    init_bert_params,
    get_activation_fn,
    TransposeLast,
    GLU_Linear,
)

logger = logging.getLogger(__name__)


def compute_mask_indices(
    shape: Tuple[int, int],
    padding_mask: Optional[torch.Tensor],
    mask_prob: float,
    mask_length: int,
    mask_type: str = "static",
    mask_other: float = 0.0,
    min_masks: int = 0,
    no_overlap: bool = False,
    min_space: int = 0,
) -> np.ndarray:
    """
    Computes random mask spans for a given shape

    Args:
        shape: the the shape for which to compute masks.
            should be of size 2 where first element is batch size and 2nd is timesteps
        padding_mask: optional padding mask of the same size as shape, which will prevent masking padded elements
        mask_prob: probability for each token to be chosen as start of the span to be masked. this will be multiplied by
            number of timesteps divided by length of mask span to mask approximately this percentage of all elements.
            however due to overlaps, the actual number will be smaller (unless no_overlap is True)
        mask_type: how to compute mask lengths
            static = fixed size
            uniform = sample from uniform distribution [mask_other, mask_length*2]
            normal = sample from normal distribution with mean mask_length and stdev mask_other. mask is min 1 element
            poisson = sample from possion distribution with lambda = mask length
        min_masks: minimum number of masked spans
        no_overlap: if false, will switch to an alternative recursive algorithm that prevents spans from overlapping
        min_space: only used if no_overlap is True, this is how many elements to keep unmasked between spans
    """

    bsz, all_sz = shape
    mask = np.full((bsz, all_sz), False)

    all_num_mask = int(
        # add a random number for probabilistic rounding
        mask_prob * all_sz / float(mask_length)
        + np.random.rand()
    )

    all_num_mask = max(min_masks, all_num_mask)

    mask_idcs = []
    for i in range(bsz):
        if padding_mask is not None:
            sz = all_sz - padding_mask[i].long().sum().item()
            num_mask = int(
                # add a random number for probabilistic rounding
                mask_prob * sz / float(mask_length)
                + np.random.rand()
            )
            num_mask = max(min_masks, num_mask)
        else:
            sz = all_sz
            num_mask = all_num_mask

        if mask_type == "static":
            lengths = np.full(num_mask, mask_length)
        elif mask_type == "uniform":
            lengths = np.random.randint(mask_other, mask_length * 2 + 1, size=num_mask)
        elif mask_type == "normal":
            lengths = np.random.normal(mask_length, mask_other, size=num_mask)
            lengths = [max(1, int(round(x))) for x in lengths]
        elif mask_type == "poisson":
            lengths = np.random.poisson(mask_length, size=num_mask)
            lengths = [int(round(x)) for x in lengths]
        else:
            raise Exception("unknown mask selection " + mask_type)

        if sum(lengths) == 0:
            lengths[0] = min(mask_length, sz - 1)

        if no_overlap:
            mask_idc = []

            def arrange(s, e, length, keep_length):
                span_start = np.random.randint(s, e - length)
                mask_idc.extend(span_start + i for i in range(length))

                new_parts = []
                if span_start - s - min_space >= keep_length:
                    new_parts.append((s, span_start - min_space + 1))
                if e - span_start - keep_length - min_space > keep_length:
                    new_parts.append((span_start + length + min_space, e))
                return new_parts

            parts = [(0, sz)]
            min_length = min(lengths)
            for length in sorted(lengths, reverse=True):
                lens = np.fromiter(
                    (e - s if e - s >= length + min_space else 0 for s, e in parts),
                    np.int,
                )
                l_sum = np.sum(lens)
                if l_sum == 0:
                    break
                probs = lens / np.sum(lens)
                c = np.random.choice(len(parts), p=probs)
                s, e = parts.pop(c)
                parts.extend(arrange(s, e, length, min_length))
            mask_idc = np.asarray(mask_idc)
        else:
            min_len = min(lengths)
            if sz - min_len <= num_mask:
                min_len = sz - num_mask - 1

            mask_idc = np.random.choice(sz - min_len, num_mask, replace=False)

            mask_idc = np.asarray(
                [
                    mask_idc[j] + offset
                    for j in range(len(mask_idc))
                    for offset in range(lengths[j])
                ]
            )

        mask_idcs.append(np.unique(mask_idc[mask_idc < sz]))

    min_len = min([len(m) for m in mask_idcs])
    for i, mask_idc in enumerate(mask_idcs):
        if len(mask_idc) > min_len:
            mask_idc = np.random.choice(mask_idc, min_len, replace=False)
        mask[i, mask_idc] = True

    return mask


class WavLMConfig:
    def __init__(self, cfg=None):
        self.extractor_mode: str = "default"     # mode for feature extractor. default has a single group norm with d groups in the first conv block, whereas layer_norm has layer norms in every block (meant to use with normalize=True)
        self.encoder_layers: int = 12     # num encoder layers in the transformer

        self.encoder_embed_dim: int = 768     # encoder embedding dimension
        self.encoder_ffn_embed_dim: int = 3072     # encoder embedding dimension for FFN
        self.encoder_attention_heads: int = 12     # num encoder attention heads
        self.activation_fn: str = "gelu"     # activation function to use

        self.layer_norm_first: bool = False     # apply layernorm first in the transformer
        self.conv_feature_layers: str = "[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2"     # string describing convolutional feature extraction layers in form of a python list that contains [(dim, kernel_size, stride), ...]
        self.conv_bias: bool = False     # include bias in conv encoder
        self.feature_grad_mult: float = 1.0     # multiply feature extractor var grads by this

        self.normalize: bool = False  # normalize input to have 0 mean and unit variance during training

        # dropouts
        self.dropout: float = 0.1     # dropout probability for the transformer
        self.attention_dropout: float = 0.1     # dropout probability for attention weights
        self.activation_dropout: float = 0.0     # dropout probability after activation in FFN
        self.encoder_layerdrop: float = 0.0     # probability of dropping a tarnsformer layer
        self.dropout_input: float = 0.0     # dropout to apply to the input (after feat extr)
        self.dropout_features: float = 0.0     # dropout to apply to the features (after feat extr)

        # masking
        self.mask_length: int = 10     # mask length
        self.mask_prob: float = 0.65     # probability of replacing a token with mask
        self.mask_selection: str = "static"     # how to choose mask length
        self.mask_other: float = 0     # secondary mask argument (used for more complex distributions), see help in compute_mask_indicesh
        self.no_mask_overlap: bool = False     # whether to allow masks to overlap
        self.mask_min_space: int = 1     # min space between spans (if no overlap is enabled)

        # channel masking
        self.mask_channel_length: int = 10     # length of the mask for features (channels)
        self.mask_channel_prob: float = 0.0     # probability of replacing a feature with 0
        self.mask_channel_selection: str = "static"     # how to choose mask length for channel masking
        self.mask_channel_other: float = 0     # secondary mask argument (used for more complex distributions), see help in compute_mask_indices
        self.no_mask_channel_overlap: bool = False     # whether to allow channel masks to overlap
        self.mask_channel_min_space: int = 1     # min space between spans (if no overlap is enabled)

        # positional embeddings
        self.conv_pos: int = 128     # number of filters for convolutional positional embeddings
        self.conv_pos_groups: int = 16     # number of groups for convolutional positional embedding

        # relative position embedding
        self.relative_position_embedding: bool = False     # apply relative position embedding
        self.num_buckets: int = 320     # number of buckets for relative position embedding
        self.max_distance: int = 1280     # maximum distance for relative position embedding
        self.gru_rel_pos: bool = False     # apply gated relative position embedding

        if cfg is not None:
            self.update(cfg)

    def update(self, cfg: dict):
        self.__dict__.update(cfg)


class WavLM(nn.Module):
    def __init__(
        self,
        cfg: WavLMConfig,
    ) -> None:
        super().__init__()
        logger.info(f"WavLM Config: {cfg.__dict__}")

        self.cfg = cfg
        feature_enc_layers = eval(cfg.conv_feature_layers)
        self.embed = feature_enc_layers[-1][0]

        self.feature_extractor = ConvFeatureExtractionModel(
            conv_layers=feature_enc_layers,
            dropout=0.0,
            mode=cfg.extractor_mode,
            conv_bias=cfg.conv_bias,
        )

        self.post_extract_proj = (
            nn.Linear(self.embed, cfg.encoder_embed_dim)
            if self.embed != cfg.encoder_embed_dim
            else None
        )

        self.mask_prob = cfg.mask_prob
        self.mask_selection = cfg.mask_selection
        self.mask_other = cfg.mask_other
        self.mask_length = cfg.mask_length
        self.no_mask_overlap = cfg.no_mask_overlap
        self.mask_min_space = cfg.mask_min_space

        self.mask_channel_prob = cfg.mask_channel_prob
        self.mask_channel_selection = cfg.mask_channel_selection
        self.mask_channel_other = cfg.mask_channel_other
        self.mask_channel_length = cfg.mask_channel_length
        self.no_mask_channel_overlap = cfg.no_mask_channel_overlap
        self.mask_channel_min_space = cfg.mask_channel_min_space

        self.dropout_input = nn.Dropout(cfg.dropout_input)
        self.dropout_features = nn.Dropout(cfg.dropout_features)

        self.feature_grad_mult = cfg.feature_grad_mult

        self.mask_emb = nn.Parameter(
            torch.FloatTensor(cfg.encoder_embed_dim).uniform_()
        )

        self.encoder = TransformerEncoder(cfg)
        self.layer_norm = LayerNorm(self.embed)

    def apply_mask(self, x, padding_mask):
        B, T, C = x.shape
        if self.mask_prob > 0:
            mask_indices = compute_mask_indices(
                (B, T),
                padding_mask,
                self.mask_prob,
                self.mask_length,
                self.mask_selection,
                self.mask_other,
                min_masks=2,
                no_overlap=self.no_mask_overlap,
                min_space=self.mask_min_space,
            )
            mask_indices = torch.from_numpy(mask_indices).to(x.device)
            x[mask_indices] = self.mask_emb
        else:
            mask_indices = None

        if self.mask_channel_prob > 0:
            mask_channel_indices = compute_mask_indices(
                (B, C),
                None,
                self.mask_channel_prob,
                self.mask_channel_length,
                self.mask_channel_selection,
                self.mask_channel_other,
                no_overlap=self.no_mask_channel_overlap,
                min_space=self.mask_channel_min_space,
            )
            mask_channel_indices = (
                torch.from_numpy(mask_channel_indices)
                .to(x.device)
                .unsqueeze(1)
                .expand(-1, T, -1)
            )
            x[mask_channel_indices] = 0

        return x, mask_indices

    def forward_padding_mask(
            self, features: torch.Tensor, padding_mask: torch.Tensor,
    ) -> torch.Tensor:
        extra = padding_mask.size(1) % features.size(1)
        if extra > 0:
            padding_mask = padding_mask[:, :-extra]
        padding_mask = padding_mask.view(
            padding_mask.size(0), features.size(1), -1
        )
        padding_mask = padding_mask.all(-1)
        return padding_mask

    def extract_features(
        self,
        source: torch.Tensor,
        padding_mask: Optional[torch.Tensor] = None,
        mask: bool = False,
        ret_conv: bool = False,
        output_layer: Optional[int] = None,
        ret_layer_results: bool = False,
    ):

        if self.feature_grad_mult > 0:
            features = self.feature_extractor(source)
            if self.feature_grad_mult != 1.0:
                features = GradMultiply.apply(features, self.feature_grad_mult)
        else:
            with torch.no_grad():
                features = self.feature_extractor(source)

        features = features.transpose(1, 2)
        features = self.layer_norm(features)

        if padding_mask is not None:
            padding_mask = self.forward_padding_mask(features, padding_mask)

        if self.post_extract_proj is not None:
            features = self.post_extract_proj(features)

        features = self.dropout_input(features)

        if mask:
            x, mask_indices = self.apply_mask(
                features, padding_mask
            )
        else:
            x = features

        # feature: (B, T, D), float
        # target: (B, T), long
        # x: (B, T, D), float
        # padding_mask: (B, T), bool
        # mask_indices: (B, T), bool
        x, layer_results = self.encoder(
            x,
            padding_mask=padding_mask,
            layer=None if output_layer is None else output_layer - 1
        )

        res = {"x": x, "padding_mask": padding_mask, "features": features, "layer_results": layer_results}

        feature = res["features"] if ret_conv else res["x"]
        if ret_layer_results:
            feature = (feature, res["layer_results"])
        return feature, res["padding_mask"]


class ConvFeatureExtractionModel(nn.Module):
    def __init__(
            self,
            conv_layers: List[Tuple[int, int, int]],
            dropout: float = 0.0,
            mode: str = "default",
            conv_bias: bool = False,
            conv_type: str = "default"
    ):
        super().__init__()

        assert mode in {"default", "layer_norm"}

        def block(
                n_in,
                n_out,
                k,
                stride,
                is_layer_norm=False,
                is_group_norm=False,
                conv_bias=False,
        ):
            def make_conv():
                conv = nn.Conv1d(n_in, n_out, k, stride=stride, bias=conv_bias)
                nn.init.kaiming_normal_(conv.weight)
                return conv

            assert (
                           is_layer_norm and is_group_norm
                   ) == False, "layer norm and group norm are exclusive"

            if is_layer_norm:
                return nn.Sequential(
                    make_conv(),
                    nn.Dropout(p=dropout),
                    nn.Sequential(
                        TransposeLast(),
                        Fp32LayerNorm(dim, elementwise_affine=True),
                        TransposeLast(),
                    ),
                    nn.GELU(),
                )
            elif is_group_norm:
                return nn.Sequential(
                    make_conv(),
                    nn.Dropout(p=dropout),
                    Fp32GroupNorm(dim, dim, affine=True),
                    nn.GELU(),
                )
            else:
                return nn.Sequential(make_conv(), nn.Dropout(p=dropout), nn.GELU())

        self.conv_type = conv_type
        if self.conv_type == "default":
            in_d = 1
            self.conv_layers = nn.ModuleList()
            for i, cl in enumerate(conv_layers):
                assert len(cl) == 3, "invalid conv definition: " + str(cl)
                (dim, k, stride) = cl

                self.conv_layers.append(
                    block(
                        in_d,
                        dim,
                        k,
                        stride,
                        is_layer_norm=mode == "layer_norm",
                        is_group_norm=mode == "default" and i == 0,
                        conv_bias=conv_bias,
                    )
                )
                in_d = dim
        elif self.conv_type == "conv2d":
            in_d = 1
            self.conv_layers = nn.ModuleList()
            for i, cl in enumerate(conv_layers):
                assert len(cl) == 3
                (dim, k, stride) = cl

                self.conv_layers.append(
                    torch.nn.Conv2d(in_d, dim, k, stride)
                )
                self.conv_layers.append(torch.nn.ReLU())
                in_d = dim
        elif self.conv_type == "custom":
            in_d = 1
            idim = 80
            self.conv_layers = nn.ModuleList()
            for i, cl in enumerate(conv_layers):
                assert len(cl) == 3
                (dim, k, stride) = cl
                self.conv_layers.append(
                    torch.nn.Conv2d(in_d, dim, k, stride, padding=1)
                )
                self.conv_layers.append(
                    torch.nn.LayerNorm([dim, idim])
                )
                self.conv_layers.append(torch.nn.ReLU())
                in_d = dim
                if (i + 1) % 2 == 0:
                    self.conv_layers.append(
                        torch.nn.MaxPool2d(2, stride=2, ceil_mode=True)
                    )
                    idim = int(math.ceil(idim / 2))
        else:
            pass

    def forward(self, x, mask=None):

        # BxT -> BxCxT
        x = x.unsqueeze(1)
        if self.conv_type == "custom":
            for conv in self.conv_layers:
                if isinstance(conv, nn.LayerNorm):
                    x = x.transpose(1, 2)
                    x = conv(x).transpose(1, 2)
                else:
                    x = conv(x)
            x = x.transpose(2, 3).contiguous()
            x = x.view(x.size(0), -1, x.size(-1))
        else:
            for conv in self.conv_layers:
                x = conv(x)
            if self.conv_type == "conv2d":
                b, c, t, f = x.size()
                x = x.transpose(2, 3).contiguous().view(b, c * f, t)
        return x


class TransformerEncoder(nn.Module):
    def __init__(self, args):
        super().__init__()

        self.dropout = args.dropout
        self.embedding_dim = args.encoder_embed_dim

        self.pos_conv = nn.Conv1d(
            self.embedding_dim,
            self.embedding_dim,
            kernel_size=args.conv_pos,
            padding=args.conv_pos // 2,
            groups=args.conv_pos_groups,
        )
        dropout = 0
        std = math.sqrt((4 * (1.0 - dropout)) / (args.conv_pos * self.embedding_dim))
        nn.init.normal_(self.pos_conv.weight, mean=0, std=std)
        nn.init.constant_(self.pos_conv.bias, 0)

        self.pos_conv = nn.utils.weight_norm(self.pos_conv, name="weight", dim=2)
        self.pos_conv = nn.Sequential(self.pos_conv, SamePad(args.conv_pos), nn.GELU())

        if hasattr(args, "relative_position_embedding"):
            self.relative_position_embedding = args.relative_position_embedding
            self.num_buckets = args.num_buckets
            self.max_distance = args.max_distance
        else:
            self.relative_position_embedding = False
            self.num_buckets = 0
            self.max_distance = 0

        self.layers = nn.ModuleList(
            [
                TransformerSentenceEncoderLayer(
                    embedding_dim=self.embedding_dim,
                    ffn_embedding_dim=args.encoder_ffn_embed_dim,
                    num_attention_heads=args.encoder_attention_heads,
                    dropout=self.dropout,
                    attention_dropout=args.attention_dropout,
                    activation_dropout=args.activation_dropout,
                    activation_fn=args.activation_fn,
                    layer_norm_first=args.layer_norm_first,
                    has_relative_attention_bias=(self.relative_position_embedding and i == 0),
                    num_buckets=self.num_buckets,
                    max_distance=self.max_distance,
                    gru_rel_pos=args.gru_rel_pos,
                )
                for i in range(args.encoder_layers)
            ]
        )

        self.layer_norm_first = args.layer_norm_first
        self.layer_norm = LayerNorm(self.embedding_dim)
        self.layerdrop = args.encoder_layerdrop

        self.apply(init_bert_params)

    def forward(self, x, padding_mask=None, streaming_mask=None, layer=None):
        x, layer_results = self.extract_features(x, padding_mask, streaming_mask, layer)

        if self.layer_norm_first and layer is None:
            x = self.layer_norm(x)

        return x, layer_results

    def extract_features(self, x, padding_mask=None, streaming_mask=None, tgt_layer=None):

        if padding_mask is not None:
            x[padding_mask] = 0

        x_conv = self.pos_conv(x.transpose(1, 2))
        x_conv = x_conv.transpose(1, 2)
        x = x + x_conv

        if not self.layer_norm_first:
            x = self.layer_norm(x)

        x = F.dropout(x, p=self.dropout, training=self.training)

        # B x T x C -> T x B x C
        x = x.transpose(0, 1)

        layer_results = []
        z = None
        if tgt_layer is not None:
            layer_results.append((x, z))
        r = None
        pos_bias = None
        for i, layer in enumerate(self.layers):
            dropout_probability = np.random.random()
            if not self.training or (dropout_probability > self.layerdrop):
                x, z, pos_bias = layer(x, self_attn_padding_mask=padding_mask, need_weights=False,
                                       self_attn_mask=streaming_mask, pos_bias=pos_bias)
            if tgt_layer is not None:
                layer_results.append((x, z))
            if i == tgt_layer:
                r = x
                break

        if r is not None:
            x = r

        # T x B x C -> B x T x C
        x = x.transpose(0, 1)

        return x, layer_results


class TransformerSentenceEncoderLayer(nn.Module):
    """
    Implements a Transformer Encoder Layer used in BERT/XLM style pre-trained
    models.
    """

    def __init__(
            self,
            embedding_dim: float = 768,
            ffn_embedding_dim: float = 3072,
            num_attention_heads: float = 8,
            dropout: float = 0.1,
            attention_dropout: float = 0.1,
            activation_dropout: float = 0.1,
            activation_fn: str = "relu",
            layer_norm_first: bool = False,
            has_relative_attention_bias: bool = False,
            num_buckets: int = 0,
            max_distance: int = 0,
            rescale_init: bool = False,
            gru_rel_pos: bool = False,
    ) -> None:

        super().__init__()
        # Initialize parameters
        self.embedding_dim = embedding_dim
        self.dropout = dropout
        self.activation_dropout = activation_dropout

        # Initialize blocks
        self.activation_name = activation_fn
        self.activation_fn = get_activation_fn(activation_fn)
        self.self_attn = MultiheadAttention(
            self.embedding_dim,
            num_attention_heads,
            dropout=attention_dropout,
            self_attention=True,
            has_relative_attention_bias=has_relative_attention_bias,
            num_buckets=num_buckets,
            max_distance=max_distance,
            rescale_init=rescale_init,
            gru_rel_pos=gru_rel_pos,
        )

        self.dropout1 = nn.Dropout(dropout)
        self.dropout2 = nn.Dropout(self.activation_dropout)
        self.dropout3 = nn.Dropout(dropout)

        self.layer_norm_first = layer_norm_first

        # layer norm associated with the self attention layer
        self.self_attn_layer_norm = LayerNorm(self.embedding_dim)

        if self.activation_name == "glu":
            self.fc1 = GLU_Linear(self.embedding_dim, ffn_embedding_dim, "swish")
        else:
            self.fc1 = nn.Linear(self.embedding_dim, ffn_embedding_dim)
        self.fc2 = nn.Linear(ffn_embedding_dim, self.embedding_dim)

        # layer norm associated with the position wise feed-forward NN
        self.final_layer_norm = LayerNorm(self.embedding_dim)

    def forward(
            self,
            x: torch.Tensor,
            self_attn_mask: torch.Tensor = None,
            self_attn_padding_mask: torch.Tensor = None,
            need_weights: bool = False,
            pos_bias=None
    ):
        """
        LayerNorm is applied either before or after the self-attention/ffn
        modules similar to the original Transformer imlementation.
        """
        residual = x

        if self.layer_norm_first:
            x = self.self_attn_layer_norm(x)
            x, attn, pos_bias = self.self_attn(
                query=x,
                key=x,
                value=x,
                key_padding_mask=self_attn_padding_mask,
                need_weights=False,
                attn_mask=self_attn_mask,
                position_bias=pos_bias
            )
            x = self.dropout1(x)
            x = residual + x

            residual = x
            x = self.final_layer_norm(x)
            if self.activation_name == "glu":
                x = self.fc1(x)
            else:
                x = self.activation_fn(self.fc1(x))
            x = self.dropout2(x)
            x = self.fc2(x)
            x = self.dropout3(x)
            x = residual + x
        else:
            x, attn, pos_bias = self.self_attn(
                query=x,
                key=x,
                value=x,
                key_padding_mask=self_attn_padding_mask,
                need_weights=need_weights,
                attn_mask=self_attn_mask,
                position_bias=pos_bias
            )

            x = self.dropout1(x)
            x = residual + x

            x = self.self_attn_layer_norm(x)

            residual = x
            if self.activation_name == "glu":
                x = self.fc1(x)
            else:
                x = self.activation_fn(self.fc1(x))
            x = self.dropout2(x)
            x = self.fc2(x)
            x = self.dropout3(x)
            x = residual + x
            x = self.final_layer_norm(x)

        return x, attn, pos_bias