| 103 | |
| 104 | # A causal variant of ResnetBlock |
| 105 | class CausalResnetBlock(nn.Module): |
| 106 | def __init__( |
| 107 | self, |
| 108 | in_channels: int, |
| 109 | out_channels: int = None, |
| 110 | dropout: float = 0.0, |
| 111 | ): |
| 112 | super().__init__() |
| 113 | self.in_channels = in_channels |
| 114 | out_channels = in_channels if out_channels is None else out_channels |
| 115 | self.out_channels = out_channels |
| 116 | |
| 117 | self.block1 = nn.Sequential( |
| 118 | Transpose(1, 2), |
| 119 | nn.LayerNorm(in_channels), |
| 120 | Transpose(1, 2), |
| 121 | nn.SiLU(), |
| 122 | CausalConv1d(in_channels, out_channels, kernel_size=3), |
| 123 | ) |
| 124 | |
| 125 | self.block2 = nn.Sequential( |
| 126 | Transpose(1, 2), |
| 127 | nn.LayerNorm(out_channels), |
| 128 | Transpose(1, 2), |
| 129 | nn.SiLU(), |
| 130 | nn.Dropout(dropout), |
| 131 | CausalConv1d(out_channels, out_channels, kernel_size=3), |
| 132 | ) |
| 133 | if self.in_channels != self.out_channels: |
| 134 | self.nin_shortcut = torch.nn.Conv1d( |
| 135 | in_channels, out_channels, kernel_size=1, stride=1, padding=0 |
| 136 | ) |
| 137 | |
| 138 | def forward(self, x: torch.Tensor): |
| 139 | """ |
| 140 | Args: |
| 141 | x: shape (b, c, t) |
| 142 | """ |
| 143 | h = x |
| 144 | h = self.block1(h) |
| 145 | h = self.block2(h) |
| 146 | if self.in_channels != self.out_channels: |
| 147 | x = self.nin_shortcut(x) |
| 148 | return x + h |
| 149 | |
| 150 | def forward_chunk(self, x: torch.Tensor, cache: torch.Tensor = None): |
| 151 | """ |
| 152 | Args: |
| 153 | x: shape (b, c, t) |
| 154 | cache: shape (b, c_in+c_out, t=2) |
| 155 | """ |
| 156 | cache1, cache2 = ( |
| 157 | (None, None) |
| 158 | if cache is None |
| 159 | else cache.split((self.in_channels, self.out_channels), dim=1) |
| 160 | ) |
| 161 | h = x |
| 162 | # block1 |