File size: 12,866 Bytes
b78ae92
5a958b4
 
 
 
 
 
05e06de
5a958b4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9043dde
5a958b4
 
 
 
 
 
 
 
 
 
 
 
 
 
9043dde
 
5a958b4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9043dde
 
5a958b4
 
 
9043dde
5a958b4
 
 
 
 
 
 
 
 
9043dde
5a958b4
 
 
9043dde
 
5a958b4
9043dde
5a958b4
9043dde
5a958b4
 
 
9043dde
5a958b4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
b78ae92
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
import functools
import math

import torch
import torch.nn as nn
import torch.nn.functional as F
import torchaudio
from models.xtransformers import ContinuousTransformerWrapper, RelativePositionBias


def zero_module(module):
    """
    Zero out the parameters of a module and return it.
    """
    for p in module.parameters():
        p.detach().zero_()
    return module


class GroupNorm32(nn.GroupNorm):
    def forward(self, x):
        return super().forward(x.float()).type(x.dtype)


def normalization(channels):
    """
    Make a standard normalization layer.

    :param channels: number of input channels.
    :return: an nn.Module for normalization.
    """
    groups = 32
    if channels <= 16:
        groups = 8
    elif channels <= 64:
        groups = 16
    while channels % groups != 0:
        groups = int(groups / 2)
    assert groups > 2
    return GroupNorm32(groups, channels)


class QKVAttentionLegacy(nn.Module):
    """
    A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
    """

    def __init__(self, n_heads):
        super().__init__()
        self.n_heads = n_heads

    def forward(self, qkv, mask=None, rel_pos=None):
        """
        Apply QKV attention.

        :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
        :return: an [N x (H * C) x T] tensor after attention.
        """
        bs, width, length = qkv.shape
        assert width % (3 * self.n_heads) == 0
        ch = width // (3 * self.n_heads)
        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
        scale = 1 / math.sqrt(math.sqrt(ch))
        weight = torch.einsum(
            "bct,bcs->bts", q * scale, k * scale
        )  # More stable with f16 than dividing afterwards
        if rel_pos is not None:
            weight = rel_pos(weight.reshape(bs, self.n_heads, weight.shape[-2], weight.shape[-1])).reshape(bs * self.n_heads, weight.shape[-2], weight.shape[-1])
        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
        if mask is not None:
            # The proper way to do this is to mask before the softmax using -inf, but that doesn't work properly on CPUs.
            mask = mask.repeat(self.n_heads, 1).unsqueeze(1)
            weight = weight * mask
        a = torch.einsum("bts,bcs->bct", weight, v)

        return a.reshape(bs, -1, length)


class AttentionBlock(nn.Module):
    """
    An attention block that allows spatial positions to attend to each other.

    Originally ported from here, but adapted to the N-d case.
    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
    """

    def __init__(
        self,
        channels,
        num_heads=1,
        num_head_channels=-1,
        do_checkpoint=True,
        relative_pos_embeddings=False,
    ):
        super().__init__()
        self.channels = channels
        self.do_checkpoint = do_checkpoint
        if num_head_channels == -1:
            self.num_heads = num_heads
        else:
            assert (
                channels % num_head_channels == 0
            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
            self.num_heads = channels // num_head_channels
        self.norm = normalization(channels)
        self.qkv = nn.Conv1d(channels, channels * 3, 1)
        # split heads before split qkv
        self.attention = QKVAttentionLegacy(self.num_heads)

        self.proj_out = zero_module(nn.Conv1d(channels, channels, 1))
        if relative_pos_embeddings:
            self.relative_pos_embeddings = RelativePositionBias(scale=(channels // self.num_heads) ** .5, causal=False, heads=num_heads, num_buckets=32, max_distance=64)
        else:
            self.relative_pos_embeddings = None

    def forward(self, x, mask=None):
        b, c, *spatial = x.shape
        x = x.reshape(b, c, -1)
        qkv = self.qkv(self.norm(x))
        h = self.attention(qkv, mask, self.relative_pos_embeddings)
        h = self.proj_out(h)
        return (x + h).reshape(b, c, *spatial)


class Upsample(nn.Module):
    """
    An upsampling layer with an optional convolution.

    :param channels: channels in the inputs and outputs.
    :param use_conv: a bool determining if a convolution is applied.
    """

    def __init__(self, channels, use_conv, out_channels=None, factor=4):
        super().__init__()
        self.channels = channels
        self.out_channels = out_channels or channels
        self.use_conv = use_conv
        self.factor = factor
        if use_conv:
            ksize = 5
            pad = 2
            self.conv = nn.Conv1d(self.channels, self.out_channels, ksize, padding=pad)

    def forward(self, x):
        assert x.shape[1] == self.channels
        x = F.interpolate(x, scale_factor=self.factor, mode="nearest")
        if self.use_conv:
            x = self.conv(x)
        return x


class Downsample(nn.Module):
    """
    A downsampling layer with an optional convolution.

    :param channels: channels in the inputs and outputs.
    :param use_conv: a bool determining if a convolution is applied.
    """

    def __init__(self, channels, use_conv, out_channels=None, factor=4, ksize=5, pad=2):
        super().__init__()
        self.channels = channels
        self.out_channels = out_channels or channels
        self.use_conv = use_conv

        stride = factor
        if use_conv:
            self.op = nn.Conv1d(
                self.channels, self.out_channels, ksize, stride=stride, padding=pad
            )
        else:
            assert self.channels == self.out_channels
            self.op = nn.AvgPool1d(kernel_size=stride, stride=stride)

    def forward(self, x):
        assert x.shape[1] == self.channels
        return self.op(x)


class ResBlock(nn.Module):
    def __init__(
            self,
            channels,
            dropout,
            out_channels=None,
            use_conv=False,
            use_scale_shift_norm=False,
            up=False,
            down=False,
            kernel_size=3,
    ):
        super().__init__()
        self.channels = channels
        self.dropout = dropout
        self.out_channels = out_channels or channels
        self.use_conv = use_conv
        self.use_scale_shift_norm = use_scale_shift_norm
        padding = 1 if kernel_size == 3 else 2

        self.in_layers = nn.Sequential(
            normalization(channels),
            nn.SiLU(),
            nn.Conv1d(channels, self.out_channels, kernel_size, padding=padding),
        )

        self.updown = up or down

        if up:
            self.h_upd = Upsample(channels, False)
            self.x_upd = Upsample(channels, False)
        elif down:
            self.h_upd = Downsample(channels, False)
            self.x_upd = Downsample(channels, False)
        else:
            self.h_upd = self.x_upd = nn.Identity()

        self.out_layers = nn.Sequential(
            normalization(self.out_channels),
            nn.SiLU(),
            nn.Dropout(p=dropout),
            zero_module(
                nn.Conv1d(self.out_channels, self.out_channels, kernel_size, padding=padding)
            ),
        )

        if self.out_channels == channels:
            self.skip_connection = nn.Identity()
        elif use_conv:
            self.skip_connection = nn.Conv1d(
                channels, self.out_channels, kernel_size, padding=padding
            )
        else:
            self.skip_connection = nn.Conv1d(channels, self.out_channels, 1)

    def forward(self, x):
        if self.updown:
            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
            h = in_rest(x)
            h = self.h_upd(h)
            x = self.x_upd(x)
            h = in_conv(h)
        else:
            h = self.in_layers(x)
        h = self.out_layers(h)
        return self.skip_connection(x) + h


class AudioMiniEncoder(nn.Module):
    def __init__(self,
                 spec_dim,
                 embedding_dim,
                 base_channels=128,
                 depth=2,
                 resnet_blocks=2,
                 attn_blocks=4,
                 num_attn_heads=4,
                 dropout=0,
                 downsample_factor=2,
                 kernel_size=3):
        super().__init__()
        self.init = nn.Sequential(
            nn.Conv1d(spec_dim, base_channels, 3, padding=1)
        )
        ch = base_channels
        res = []
        for l in range(depth):
            for r in range(resnet_blocks):
                res.append(ResBlock(ch, dropout, kernel_size=kernel_size))
            res.append(Downsample(ch, use_conv=True, out_channels=ch*2, factor=downsample_factor))
            ch *= 2
        self.res = nn.Sequential(*res)
        self.final = nn.Sequential(
            normalization(ch),
            nn.SiLU(),
            nn.Conv1d(ch, embedding_dim, 1)
        )
        attn = []
        for a in range(attn_blocks):
            attn.append(AttentionBlock(embedding_dim, num_attn_heads,))
        self.attn = nn.Sequential(*attn)
        self.dim = embedding_dim

    def forward(self, x):
        h = self.init(x)
        h = self.res(h)
        h = self.final(h)
        h = self.attn(h)
        return h[:, :, 0]


class TorchMelSpectrogram(nn.Module):
    def __init__(self, filter_length=1024, hop_length=256, win_length=1024, n_mel_channels=80, mel_fmin=0, mel_fmax=8000,
                 sampling_rate=22050, normalize=False, mel_norm_file='data/mel_norms.pth'):
        super().__init__()
        # These are the default tacotron values for the MEL spectrogram.
        self.filter_length = filter_length
        self.hop_length = hop_length
        self.win_length = win_length
        self.n_mel_channels = n_mel_channels
        self.mel_fmin = mel_fmin
        self.mel_fmax = mel_fmax
        self.sampling_rate = sampling_rate
        self.mel_stft = torchaudio.transforms.MelSpectrogram(n_fft=self.filter_length, hop_length=self.hop_length,
                                                             win_length=self.win_length, power=2, normalized=normalize,
                                                             sample_rate=self.sampling_rate, f_min=self.mel_fmin,
                                                             f_max=self.mel_fmax, n_mels=self.n_mel_channels,
                                                             norm="slaney")
        self.mel_norm_file = mel_norm_file
        if self.mel_norm_file is not None:
            self.mel_norms = torch.load(self.mel_norm_file)
        else:
            self.mel_norms = None

    def forward(self, inp):
        if len(inp.shape) == 3:  # Automatically squeeze out the channels dimension if it is present (assuming mono-audio)
            inp = inp.squeeze(1)
        assert len(inp.shape) == 2
        self.mel_stft = self.mel_stft.to(inp.device)
        mel = self.mel_stft(inp)
        # Perform dynamic range compression
        mel = torch.log(torch.clamp(mel, min=1e-5))
        if self.mel_norms is not None:
            self.mel_norms = self.mel_norms.to(mel.device)
            mel = mel / self.mel_norms.unsqueeze(0).unsqueeze(-1)
        return mel


class CheckpointedLayer(nn.Module):
    """
    Wraps a module. When forward() is called, passes kwargs that require_grad through torch.checkpoint() and bypasses
    checkpoint for all other args.
    """
    def __init__(self, wrap):
        super().__init__()
        self.wrap = wrap

    def forward(self, x, *args, **kwargs):
        for k, v in kwargs.items():
            assert not (isinstance(v, torch.Tensor) and v.requires_grad)  # This would screw up checkpointing.
        partial = functools.partial(self.wrap, **kwargs)
        return torch.utils.checkpoint.checkpoint(partial, x, *args)


class CheckpointedXTransformerEncoder(nn.Module):
    """
    Wraps a ContinuousTransformerWrapper and applies CheckpointedLayer to each layer and permutes from channels-mid
    to channels-last that XTransformer expects.
    """
    def __init__(self, needs_permute=True, exit_permute=True, checkpoint=True, **xtransformer_kwargs):
        super().__init__()
        self.transformer = ContinuousTransformerWrapper(**xtransformer_kwargs)
        self.needs_permute = needs_permute
        self.exit_permute = exit_permute

        if not checkpoint:
            return
        for i in range(len(self.transformer.attn_layers.layers)):
            n, b, r = self.transformer.attn_layers.layers[i]
            self.transformer.attn_layers.layers[i] = nn.ModuleList([n, CheckpointedLayer(b), r])

    def forward(self, x, **kwargs):
        if self.needs_permute:
            x = x.permute(0,2,1)
        h = self.transformer(x, **kwargs)
        if self.exit_permute:
            h = h.permute(0,2,1)
        return h