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from typing import Optional
import torch
from torch import nn
from torch.nn.utils import weight_norm
from inspiremusic.wavtokenizer.decoder.modules import ConvNeXtBlock, ResBlock1, AdaLayerNorm
def nonlinearity(x):
# swish
return x * torch.sigmoid(x)
def Normalize(in_channels, num_groups=32):
return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
class ResnetBlock(nn.Module):
def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
dropout, temb_channels=512):
super().__init__()
self.in_channels = in_channels
out_channels = in_channels if out_channels is None else out_channels
self.out_channels = out_channels
self.use_conv_shortcut = conv_shortcut
self.norm1 = Normalize(in_channels)
self.conv1 = torch.nn.Conv1d(in_channels,
out_channels,
kernel_size=3,
stride=1,
padding=1)
if temb_channels > 0:
self.temb_proj = torch.nn.Linear(temb_channels,
out_channels)
self.norm2 = Normalize(out_channels)
self.dropout = torch.nn.Dropout(dropout)
self.conv2 = torch.nn.Conv1d(out_channels,
out_channels,
kernel_size=3,
stride=1,
padding=1)
if self.in_channels != self.out_channels:
if self.use_conv_shortcut:
self.conv_shortcut = torch.nn.Conv1d(in_channels,
out_channels,
kernel_size=3,
stride=1,
padding=1)
else:
self.nin_shortcut = torch.nn.Conv1d(in_channels,
out_channels,
kernel_size=1,
stride=1,
padding=0)
def forward(self, x, temb=None):
h = x
h = self.norm1(h)
h = nonlinearity(h)
h = self.conv1(h)
if temb is not None:
h = h + self.temb_proj(nonlinearity(temb))[:, :, None, None]
h = self.norm2(h)
h = nonlinearity(h)
h = self.dropout(h)
h = self.conv2(h)
if self.in_channels != self.out_channels:
if self.use_conv_shortcut:
x = self.conv_shortcut(x)
else:
x = self.nin_shortcut(x)
return x + h
class AttnBlock(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.in_channels = in_channels
self.norm = Normalize(in_channels)
self.q = torch.nn.Conv1d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.k = torch.nn.Conv1d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.v = torch.nn.Conv1d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.proj_out = torch.nn.Conv1d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
def forward(self, x):
h_ = x
h_ = self.norm(h_)
q = self.q(h_)
k = self.k(h_)
v = self.v(h_)
# compute attention
b, c, h = q.shape
q = q.permute(0, 2, 1) # b,hw,c
w_ = torch.bmm(q, k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
w_ = w_ * (int(c) ** (-0.5))
w_ = torch.nn.functional.softmax(w_, dim=2)
# attend to values
w_ = w_.permute(0, 2, 1) # b,hw,hw (first hw of k, second of q)
h_ = torch.bmm(v, w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
h_ = self.proj_out(h_)
return x + h_
def make_attn(in_channels, attn_type="vanilla"):
assert attn_type in ["vanilla", "linear", "none"], f'attn_type {attn_type} unknown'
print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
if attn_type == "vanilla":
return AttnBlock(in_channels)
class Backbone(nn.Module):
"""Base class for the generator's backbone. It preserves the same temporal resolution across all layers."""
def forward(self, x: torch.Tensor, **kwargs) -> torch.Tensor:
"""
Args:
x (Tensor): Input tensor of shape (B, C, L), where B is the batch size,
C denotes output features, and L is the sequence length.
Returns:
Tensor: Output of shape (B, L, H), where B is the batch size, L is the sequence length,
and H denotes the model dimension.
"""
raise NotImplementedError("Subclasses must implement the forward method.")
class VocosBackbone(Backbone):
"""
Vocos backbone module built with ConvNeXt blocks. Supports additional conditioning with Adaptive Layer Normalization
Args:
input_channels (int): Number of input features channels.
dim (int): Hidden dimension of the model.
intermediate_dim (int): Intermediate dimension used in ConvNeXtBlock.
num_layers (int): Number of ConvNeXtBlock layers.
layer_scale_init_value (float, optional): Initial value for layer scaling. Defaults to `1 / num_layers`.
adanorm_num_embeddings (int, optional): Number of embeddings for AdaLayerNorm.
None means non-conditional model. Defaults to None.
"""
def __init__(
self,
input_channels: int,
dim: int,
intermediate_dim: int,
num_layers: int,
layer_scale_init_value: Optional[float] = None,
adanorm_num_embeddings: Optional[int] = None,
):
super().__init__()
self.input_channels = input_channels
self.embed = nn.Conv1d(input_channels, dim, kernel_size=7, padding=3)
self.adanorm = adanorm_num_embeddings is not None
if adanorm_num_embeddings:
self.norm = AdaLayerNorm(adanorm_num_embeddings, dim, eps=1e-6)
else:
self.norm = nn.LayerNorm(dim, eps=1e-6)
layer_scale_init_value = layer_scale_init_value or 1 / num_layers
self.convnext = nn.ModuleList(
[
ConvNeXtBlock(
dim=dim,
intermediate_dim=intermediate_dim,
layer_scale_init_value=layer_scale_init_value,
adanorm_num_embeddings=adanorm_num_embeddings,
)
for _ in range(num_layers)
]
)
self.final_layer_norm = nn.LayerNorm(dim, eps=1e-6)
self.apply(self._init_weights)
self.temb_ch = 0
block_in = dim
dropout = 0.1
attn_type="vanilla"
pos_net : tp.List[nn.Module] = [
ResnetBlock(in_channels=block_in,out_channels=block_in,
temb_channels=self.temb_ch,dropout=dropout),
ResnetBlock(in_channels=block_in,out_channels=block_in,
temb_channels=self.temb_ch,dropout=dropout),
make_attn(block_in, attn_type=attn_type),
ResnetBlock(in_channels=block_in,out_channels=block_in,
temb_channels=self.temb_ch,dropout=dropout),
ResnetBlock(in_channels=block_in,out_channels=block_in,
temb_channels=self.temb_ch,dropout=dropout),
Normalize(block_in)
]
self.pos_net = nn.Sequential(*pos_net)
def _init_weights(self, m):
if isinstance(m, (nn.Conv1d, nn.Linear)):
nn.init.trunc_normal_(m.weight, std=0.02)
nn.init.constant_(m.bias, 0)
def forward(self, x: torch.Tensor, bandwidth_id: Optional[torch.Tensor] = None) -> torch.Tensor:
x = self.embed(x)
x = self.pos_net(x)
if self.adanorm:
# assert bandwidth_id is not None
if bandwidth_id is None:
bandwidth_id = torch.tensor(0, device='cuda')
x = self.norm(x.transpose(1, 2), cond_embedding_id=bandwidth_id)
else:
x = self.norm(x.transpose(1, 2))
x = x.transpose(1, 2)
for conv_block in self.convnext:
x = conv_block(x, cond_embedding_id=bandwidth_id)
x = self.final_layer_norm(x.transpose(1, 2))
return x
class VocosResNetBackbone(Backbone):
"""
Vocos backbone module built with ResBlocks.
Args:
input_channels (int): Number of input features channels.
dim (int): Hidden dimension of the model.
num_blocks (int): Number of ResBlock1 blocks.
layer_scale_init_value (float, optional): Initial value for layer scaling. Defaults to None.
"""
def __init__(
self, input_channels, dim, num_blocks, layer_scale_init_value=None,
):
super().__init__()
self.input_channels = input_channels
self.embed = weight_norm(nn.Conv1d(input_channels, dim, kernel_size=3, padding=1))
layer_scale_init_value = layer_scale_init_value or 1 / num_blocks / 3
self.resnet = nn.Sequential(
*[ResBlock1(dim=dim, layer_scale_init_value=layer_scale_init_value) for _ in range(num_blocks)]
)
def forward(self, x: torch.Tensor, **kwargs) -> torch.Tensor:
x = self.embed(x)
x = self.resnet(x)
x = x.transpose(1, 2)
return x
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