inspiremusic/wavtokenizer/decoder/models.py

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