| import math |
| import torch |
| from .commons import convert_pad_shape |
|
|
|
|
| class MultiHeadAttention(torch.nn.Module): |
|
|
| 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 |
|
|
| 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) |
|
|
| 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 |
| ) |
|
|
| 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): |
| q, k, v = self.conv_q(x), self.conv_k(c), self.conv_v(c) |
|
|
| x, self.attn = self.attention(q, k, v, mask=attn_mask) |
|
|
| return self.conv_o(x) |
|
|
| def attention(self, query, key, value, mask=None): |
| 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) |
|
|
| p_attn = self.drop(torch.nn.functional.softmax(scores, dim=-1)) |
|
|
| 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) |
|
|
| 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): |
|
|
| 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]]) |
| ) |
|
|
