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| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from einops import rearrange | |
| from mmaudio.ext.autoencoder.edm2_utils import (MPConv1D, mp_silu, mp_sum, normalize) | |
| def nonlinearity(x): | |
| # swish | |
| return mp_silu(x) | |
| class ResnetBlock1D(nn.Module): | |
| def __init__(self, *, in_dim, out_dim=None, conv_shortcut=False, kernel_size=3, use_norm=True): | |
| super().__init__() | |
| self.in_dim = in_dim | |
| out_dim = in_dim if out_dim is None else out_dim | |
| self.out_dim = out_dim | |
| self.use_conv_shortcut = conv_shortcut | |
| self.use_norm = use_norm | |
| self.conv1 = MPConv1D(in_dim, out_dim, kernel_size=kernel_size) | |
| self.conv2 = MPConv1D(out_dim, out_dim, kernel_size=kernel_size) | |
| if self.in_dim != self.out_dim: | |
| if self.use_conv_shortcut: | |
| self.conv_shortcut = MPConv1D(in_dim, out_dim, kernel_size=kernel_size) | |
| else: | |
| self.nin_shortcut = MPConv1D(in_dim, out_dim, kernel_size=1) | |
| def forward(self, x: torch.Tensor) -> torch.Tensor: | |
| # pixel norm | |
| if self.use_norm: | |
| x = normalize(x, dim=1) | |
| h = x | |
| h = nonlinearity(h) | |
| h = self.conv1(h) | |
| h = nonlinearity(h) | |
| h = self.conv2(h) | |
| if self.in_dim != self.out_dim: | |
| if self.use_conv_shortcut: | |
| x = self.conv_shortcut(x) | |
| else: | |
| x = self.nin_shortcut(x) | |
| return mp_sum(x, h, t=0.3) | |
| class AttnBlock1D(nn.Module): | |
| def __init__(self, in_channels, num_heads=1): | |
| super().__init__() | |
| self.in_channels = in_channels | |
| self.num_heads = num_heads | |
| self.qkv = MPConv1D(in_channels, in_channels * 3, kernel_size=1) | |
| self.proj_out = MPConv1D(in_channels, in_channels, kernel_size=1) | |
| def forward(self, x): | |
| h = x | |
| y = self.qkv(h) | |
| y = y.reshape(y.shape[0], self.num_heads, -1, 3, y.shape[-1]) | |
| q, k, v = normalize(y, dim=2).unbind(3) | |
| q = rearrange(q, 'b h c l -> b h l c') | |
| k = rearrange(k, 'b h c l -> b h l c') | |
| v = rearrange(v, 'b h c l -> b h l c') | |
| h = F.scaled_dot_product_attention(q, k, v) | |
| h = rearrange(h, 'b h l c -> b (h c) l') | |
| h = self.proj_out(h) | |
| return mp_sum(x, h, t=0.3) | |
| class Upsample1D(nn.Module): | |
| def __init__(self, in_channels, with_conv): | |
| super().__init__() | |
| self.with_conv = with_conv | |
| if self.with_conv: | |
| self.conv = MPConv1D(in_channels, in_channels, kernel_size=3) | |
| def forward(self, x): | |
| x = F.interpolate(x, scale_factor=2.0, mode='nearest-exact') # support 3D tensor(B,C,T) | |
| if self.with_conv: | |
| x = self.conv(x) | |
| return x | |
| class Downsample1D(nn.Module): | |
| def __init__(self, in_channels, with_conv): | |
| super().__init__() | |
| self.with_conv = with_conv | |
| if self.with_conv: | |
| # no asymmetric padding in torch conv, must do it ourselves | |
| self.conv1 = MPConv1D(in_channels, in_channels, kernel_size=1) | |
| self.conv2 = MPConv1D(in_channels, in_channels, kernel_size=1) | |
| def forward(self, x): | |
| if self.with_conv: | |
| x = self.conv1(x) | |
| x = F.avg_pool1d(x, kernel_size=2, stride=2) | |
| if self.with_conv: | |
| x = self.conv2(x) | |
| return x | |