Spaces:
Paused
Paused
| import math | |
| import torch | |
| from rvc.lib.algorithm.commons import convert_pad_shape | |
| class MultiHeadAttention(torch.nn.Module): | |
| """ | |
| Multi-head attention module with optional relative positional encoding and proximal bias. | |
| Args: | |
| channels (int): Number of input channels. | |
| out_channels (int): Number of output channels. | |
| n_heads (int): Number of attention heads. | |
| p_dropout (float, optional): Dropout probability. Defaults to 0.0. | |
| window_size (int, optional): Window size for relative positional encoding. Defaults to None. | |
| heads_share (bool, optional): Whether to share relative positional embeddings across heads. Defaults to True. | |
| block_length (int, optional): Block length for local attention. Defaults to None. | |
| proximal_bias (bool, optional): Whether to use proximal bias in self-attention. Defaults to False. | |
| proximal_init (bool, optional): Whether to initialize the key projection weights the same as query projection weights. Defaults to False. | |
| """ | |
| def __init__( | |
| self, | |
| channels: int, | |
| out_channels: int, | |
| n_heads: int, | |
| p_dropout: float = 0.0, | |
| window_size: int = None, | |
| heads_share: bool = True, | |
| block_length: int = None, | |
| proximal_bias: bool = False, | |
| proximal_init: bool = False, | |
| ): | |
| super().__init__() | |
| assert ( | |
| channels % n_heads == 0 | |
| ), "Channels must be divisible by the number of heads." | |
| self.channels = channels | |
| self.out_channels = out_channels | |
| self.n_heads = n_heads | |
| self.k_channels = channels // n_heads | |
| self.window_size = window_size | |
| self.block_length = block_length | |
| self.proximal_bias = proximal_bias | |
| # Define projections | |
| self.conv_q = torch.nn.Conv1d(channels, channels, 1) | |
| self.conv_k = torch.nn.Conv1d(channels, channels, 1) | |
| self.conv_v = torch.nn.Conv1d(channels, channels, 1) | |
| self.conv_o = torch.nn.Conv1d(channels, out_channels, 1) | |
| self.drop = torch.nn.Dropout(p_dropout) | |
| # Relative positional encodings | |
| if window_size: | |
| n_heads_rel = 1 if heads_share else n_heads | |
| rel_stddev = self.k_channels**-0.5 | |
| self.emb_rel_k = torch.nn.Parameter( | |
| torch.randn(n_heads_rel, 2 * window_size + 1, self.k_channels) | |
| * rel_stddev | |
| ) | |
| self.emb_rel_v = torch.nn.Parameter( | |
| torch.randn(n_heads_rel, 2 * window_size + 1, self.k_channels) | |
| * rel_stddev | |
| ) | |
| # Initialize weights | |
| torch.nn.init.xavier_uniform_(self.conv_q.weight) | |
| torch.nn.init.xavier_uniform_(self.conv_k.weight) | |
| torch.nn.init.xavier_uniform_(self.conv_v.weight) | |
| torch.nn.init.xavier_uniform_(self.conv_o.weight) | |
| if proximal_init: | |
| with torch.no_grad(): | |
| self.conv_k.weight.copy_(self.conv_q.weight) | |
| self.conv_k.bias.copy_(self.conv_q.bias) | |
| def forward(self, x, c, attn_mask=None): | |
| # Compute query, key, value projections | |
| q, k, v = self.conv_q(x), self.conv_k(c), self.conv_v(c) | |
| # Compute attention | |
| x, self.attn = self.attention(q, k, v, mask=attn_mask) | |
| # Final output projection | |
| return self.conv_o(x) | |
| def attention(self, query, key, value, mask=None): | |
| # Reshape and compute scaled dot-product attention | |
| b, d, t_s, t_t = (*key.size(), query.size(2)) | |
| query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3) | |
| key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) | |
| value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) | |
| scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1)) | |
| if self.window_size: | |
| assert t_s == t_t, "Relative attention only supports self-attention." | |
| scores += self._compute_relative_scores(query, t_s) | |
| if self.proximal_bias: | |
| assert t_s == t_t, "Proximal bias only supports self-attention." | |
| scores += self._attention_bias_proximal(t_s).to(scores.device, scores.dtype) | |
| if mask is not None: | |
| scores = scores.masked_fill(mask == 0, -1e4) | |
| if self.block_length: | |
| block_mask = ( | |
| torch.ones_like(scores) | |
| .triu(-self.block_length) | |
| .tril(self.block_length) | |
| ) | |
| scores = scores.masked_fill(block_mask == 0, -1e4) | |
| # Apply softmax and dropout | |
| p_attn = self.drop(torch.nn.functional.softmax(scores, dim=-1)) | |
| # Compute attention output | |
| output = torch.matmul(p_attn, value) | |
| if self.window_size: | |
| output += self._apply_relative_values(p_attn, t_s) | |
| return output.transpose(2, 3).contiguous().view(b, d, t_t), p_attn | |
| def _compute_relative_scores(self, query, length): | |
| rel_emb = self._get_relative_embeddings(self.emb_rel_k, length) | |
| rel_logits = self._matmul_with_relative_keys( | |
| query / math.sqrt(self.k_channels), rel_emb | |
| ) | |
| return self._relative_position_to_absolute_position(rel_logits) | |
| def _apply_relative_values(self, p_attn, length): | |
| rel_weights = self._absolute_position_to_relative_position(p_attn) | |
| rel_emb = self._get_relative_embeddings(self.emb_rel_v, length) | |
| return self._matmul_with_relative_values(rel_weights, rel_emb) | |
| # Helper methods | |
| def _matmul_with_relative_values(self, x, y): | |
| return torch.matmul(x, y.unsqueeze(0)) | |
| def _matmul_with_relative_keys(self, x, y): | |
| return torch.matmul(x, y.unsqueeze(0).transpose(-2, -1)) | |
| def _get_relative_embeddings(self, embeddings, length): | |
| pad_length = max(length - (self.window_size + 1), 0) | |
| start = max((self.window_size + 1) - length, 0) | |
| end = start + 2 * length - 1 | |
| if pad_length > 0: | |
| embeddings = torch.nn.functional.pad( | |
| embeddings, | |
| convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]), | |
| ) | |
| return embeddings[:, start:end] | |
| def _relative_position_to_absolute_position(self, x): | |
| batch, heads, length, _ = x.size() | |
| x = torch.nn.functional.pad( | |
| x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]) | |
| ) | |
| x_flat = x.view(batch, heads, length * 2 * length) | |
| x_flat = torch.nn.functional.pad( | |
| x_flat, convert_pad_shape([[0, 0], [0, 0], [0, length - 1]]) | |
| ) | |
| return x_flat.view(batch, heads, length + 1, 2 * length - 1)[ | |
| :, :, :length, length - 1 : | |
| ] | |
| def _absolute_position_to_relative_position(self, x): | |
| batch, heads, length, _ = x.size() | |
| x = torch.nn.functional.pad( | |
| x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]]) | |
| ) | |
| x_flat = x.view(batch, heads, length**2 + length * (length - 1)) | |
| x_flat = torch.nn.functional.pad( | |
| x_flat, convert_pad_shape([[0, 0], [0, 0], [length, 0]]) | |
| ) | |
| return x_flat.view(batch, heads, length, 2 * length)[:, :, :, 1:] | |
| def _attention_bias_proximal(self, length): | |
| r = torch.arange(length, dtype=torch.float32) | |
| diff = r.unsqueeze(0) - r.unsqueeze(1) | |
| return -torch.log1p(torch.abs(diff)).unsqueeze(0).unsqueeze(0) | |
| class FFN(torch.nn.Module): | |
| """ | |
| Feed-forward network module. | |
| Args: | |
| in_channels (int): Number of input channels. | |
| out_channels (int): Number of output channels. | |
| filter_channels (int): Number of filter channels in the convolution layers. | |
| kernel_size (int): Kernel size of the convolution layers. | |
| p_dropout (float, optional): Dropout probability. Defaults to 0.0. | |
| activation (str, optional): Activation function to use. Defaults to None. | |
| causal (bool, optional): Whether to use causal padding in the convolution layers. Defaults to False. | |
| """ | |
| def __init__( | |
| self, | |
| in_channels: int, | |
| out_channels: int, | |
| filter_channels: int, | |
| kernel_size: int, | |
| p_dropout: float = 0.0, | |
| activation: str = None, | |
| causal: bool = False, | |
| ): | |
| super().__init__() | |
| self.padding_fn = self._causal_padding if causal else self._same_padding | |
| self.conv_1 = torch.nn.Conv1d(in_channels, filter_channels, kernel_size) | |
| self.conv_2 = torch.nn.Conv1d(filter_channels, out_channels, kernel_size) | |
| self.drop = torch.nn.Dropout(p_dropout) | |
| self.activation = activation | |
| def forward(self, x, x_mask): | |
| x = self.conv_1(self.padding_fn(x * x_mask)) | |
| x = self._apply_activation(x) | |
| x = self.drop(x) | |
| x = self.conv_2(self.padding_fn(x * x_mask)) | |
| return x * x_mask | |
| def _apply_activation(self, x): | |
| if self.activation == "gelu": | |
| return x * torch.sigmoid(1.702 * x) | |
| return torch.relu(x) | |
| def _causal_padding(self, x): | |
| pad_l, pad_r = self.conv_1.kernel_size[0] - 1, 0 | |
| return torch.nn.functional.pad( | |
| x, convert_pad_shape([[0, 0], [0, 0], [pad_l, pad_r]]) | |
| ) | |
| def _same_padding(self, x): | |
| pad = (self.conv_1.kernel_size[0] - 1) // 2 | |
| return torch.nn.functional.pad( | |
| x, convert_pad_shape([[0, 0], [0, 0], [pad, pad]]) | |
| ) | |