Spaces:
Running
Running
File size: 7,176 Bytes
c968fc3 |
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 |
# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
import torch.nn as nn
import math
from torch.nn import functional as F
class StyleAdaptiveLayerNorm(nn.Module):
def __init__(self, normalized_shape, eps=1e-5):
super().__init__()
self.in_dim = normalized_shape
self.norm = nn.LayerNorm(self.in_dim, eps=eps, elementwise_affine=False)
self.style = nn.Linear(self.in_dim, self.in_dim * 2)
self.style.bias.data[: self.in_dim] = 1
self.style.bias.data[self.in_dim :] = 0
def forward(self, x, condition):
# x: (B, T, d); condition: (B, T, d)
style = self.style(torch.mean(condition, dim=1, keepdim=True))
gamma, beta = style.chunk(2, -1)
out = self.norm(x)
out = gamma * out + beta
return out
class PositionalEncoding(nn.Module):
def __init__(self, d_model, dropout, max_len=5000):
super().__init__()
self.dropout = dropout
position = torch.arange(max_len).unsqueeze(1)
div_term = torch.exp(
torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model)
)
pe = torch.zeros(max_len, 1, d_model)
pe[:, 0, 0::2] = torch.sin(position * div_term)
pe[:, 0, 1::2] = torch.cos(position * div_term)
self.register_buffer("pe", pe)
def forward(self, x):
x = x + self.pe[: x.size(0)]
return F.dropout(x, self.dropout, training=self.training)
class TransformerFFNLayer(nn.Module):
def __init__(
self, encoder_hidden, conv_filter_size, conv_kernel_size, encoder_dropout
):
super().__init__()
self.encoder_hidden = encoder_hidden
self.conv_filter_size = conv_filter_size
self.conv_kernel_size = conv_kernel_size
self.encoder_dropout = encoder_dropout
self.ffn_1 = nn.Conv1d(
self.encoder_hidden,
self.conv_filter_size,
self.conv_kernel_size,
padding=self.conv_kernel_size // 2,
)
self.ffn_1.weight.data.normal_(0.0, 0.02)
self.ffn_2 = nn.Linear(self.conv_filter_size, self.encoder_hidden)
self.ffn_2.weight.data.normal_(0.0, 0.02)
def forward(self, x):
# x: (B, T, d)
x = self.ffn_1(x.permute(0, 2, 1)).permute(
0, 2, 1
) # (B, T, d) -> (B, d, T) -> (B, T, d)
x = F.relu(x)
x = F.dropout(x, self.encoder_dropout, training=self.training)
x = self.ffn_2(x)
return x
class TransformerEncoderLayer(nn.Module):
def __init__(
self,
encoder_hidden,
encoder_head,
conv_filter_size,
conv_kernel_size,
encoder_dropout,
use_cln,
):
super().__init__()
self.encoder_hidden = encoder_hidden
self.encoder_head = encoder_head
self.conv_filter_size = conv_filter_size
self.conv_kernel_size = conv_kernel_size
self.encoder_dropout = encoder_dropout
self.use_cln = use_cln
if not self.use_cln:
self.ln_1 = nn.LayerNorm(self.encoder_hidden)
self.ln_2 = nn.LayerNorm(self.encoder_hidden)
else:
self.ln_1 = StyleAdaptiveLayerNorm(self.encoder_hidden)
self.ln_2 = StyleAdaptiveLayerNorm(self.encoder_hidden)
self.self_attn = nn.MultiheadAttention(
self.encoder_hidden, self.encoder_head, batch_first=True
)
self.ffn = TransformerFFNLayer(
self.encoder_hidden,
self.conv_filter_size,
self.conv_kernel_size,
self.encoder_dropout,
)
def forward(self, x, key_padding_mask, conditon=None):
# x: (B, T, d); key_padding_mask: (B, T), mask is 0; condition: (B, T, d)
# self attention
residual = x
if self.use_cln:
x = self.ln_1(x, conditon)
else:
x = self.ln_1(x)
if key_padding_mask != None:
key_padding_mask_input = ~(key_padding_mask.bool())
else:
key_padding_mask_input = None
x, _ = self.self_attn(
query=x, key=x, value=x, key_padding_mask=key_padding_mask_input
)
x = F.dropout(x, self.encoder_dropout, training=self.training)
x = residual + x
# ffn
residual = x
if self.use_cln:
x = self.ln_2(x, conditon)
else:
x = self.ln_2(x)
x = self.ffn(x)
x = residual + x
return x
class TransformerEncoder(nn.Module):
def __init__(
self,
enc_emb_tokens=None,
encoder_layer=4,
encoder_hidden=256,
encoder_head=4,
conv_filter_size=1024,
conv_kernel_size=5,
encoder_dropout=0.1,
use_cln=False,
cfg=None,
):
super().__init__()
self.encoder_layer = (
encoder_layer if encoder_layer is not None else cfg.encoder_layer
)
self.encoder_hidden = (
encoder_hidden if encoder_hidden is not None else cfg.encoder_hidden
)
self.encoder_head = (
encoder_head if encoder_head is not None else cfg.encoder_head
)
self.conv_filter_size = (
conv_filter_size if conv_filter_size is not None else cfg.conv_filter_size
)
self.conv_kernel_size = (
conv_kernel_size if conv_kernel_size is not None else cfg.conv_kernel_size
)
self.encoder_dropout = (
encoder_dropout if encoder_dropout is not None else cfg.encoder_dropout
)
self.use_cln = use_cln if use_cln is not None else cfg.use_cln
if enc_emb_tokens != None:
self.use_enc_emb = True
self.enc_emb_tokens = enc_emb_tokens
else:
self.use_enc_emb = False
self.position_emb = PositionalEncoding(
self.encoder_hidden, self.encoder_dropout
)
self.layers = nn.ModuleList([])
self.layers.extend(
[
TransformerEncoderLayer(
self.encoder_hidden,
self.encoder_head,
self.conv_filter_size,
self.conv_kernel_size,
self.encoder_dropout,
self.use_cln,
)
for i in range(self.encoder_layer)
]
)
if self.use_cln:
self.last_ln = StyleAdaptiveLayerNorm(self.encoder_hidden)
else:
self.last_ln = nn.LayerNorm(self.encoder_hidden)
def forward(self, x, key_padding_mask, condition=None):
if len(x.shape) == 2 and self.use_enc_emb:
x = self.enc_emb_tokens(x)
x = self.position_emb(x)
else:
x = self.position_emb(x) # (B, T, d)
for layer in self.layers:
x = layer(x, key_padding_mask, condition)
if self.use_cln:
x = self.last_ln(x, condition)
else:
x = self.last_ln(x)
return x
|