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]]) )