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| import copy | |
| import math | |
| import numpy as np | |
| import torch | |
| from torch import nn | |
| from torch.nn import functional as F | |
| import commons | |
| import modules | |
| from modules import LayerNorm | |
| class Encoder(nn.Module): | |
| def __init__( | |
| self, | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers, | |
| kernel_size=1, | |
| p_dropout=0.0, | |
| window_size=None, | |
| block_length=None, | |
| **kwargs | |
| ): | |
| super().__init__() | |
| self.hidden_channels = hidden_channels | |
| self.filter_channels = filter_channels | |
| self.n_heads = n_heads | |
| self.n_layers = n_layers | |
| self.kernel_size = kernel_size | |
| self.p_dropout = p_dropout | |
| self.window_size = window_size | |
| self.block_length = block_length | |
| self.drop = nn.Dropout(p_dropout) | |
| self.attn_layers = nn.ModuleList() | |
| self.norm_layers_1 = nn.ModuleList() | |
| self.ffn_layers = nn.ModuleList() | |
| self.norm_layers_2 = nn.ModuleList() | |
| for i in range(self.n_layers): | |
| self.attn_layers.append( | |
| MultiHeadAttention( | |
| hidden_channels, | |
| hidden_channels, | |
| n_heads, | |
| window_size=window_size, | |
| p_dropout=p_dropout, | |
| block_length=block_length, | |
| ) | |
| ) | |
| self.norm_layers_1.append(LayerNorm(hidden_channels)) | |
| self.ffn_layers.append( | |
| FFN( | |
| hidden_channels, | |
| hidden_channels, | |
| filter_channels, | |
| kernel_size, | |
| p_dropout=p_dropout, | |
| ) | |
| ) | |
| self.norm_layers_2.append(LayerNorm(hidden_channels)) | |
| def forward(self, x, x_mask): | |
| attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1) | |
| for i in range(self.n_layers): | |
| x = x * x_mask | |
| y = self.attn_layers[i](x, x, attn_mask) | |
| y = self.drop(y) | |
| x = self.norm_layers_1[i](x + y) | |
| y = self.ffn_layers[i](x, x_mask) | |
| y = self.drop(y) | |
| x = self.norm_layers_2[i](x + y) | |
| x = x * x_mask | |
| return x | |
| class CouplingBlock(nn.Module): | |
| def __init__( | |
| self, | |
| in_channels, | |
| hidden_channels, | |
| kernel_size, | |
| dilation_rate, | |
| n_layers, | |
| gin_channels=0, | |
| p_dropout=0, | |
| sigmoid_scale=False, | |
| ): | |
| super().__init__() | |
| self.in_channels = in_channels | |
| self.hidden_channels = hidden_channels | |
| self.kernel_size = kernel_size | |
| self.dilation_rate = dilation_rate | |
| self.n_layers = n_layers | |
| self.gin_channels = gin_channels | |
| self.p_dropout = p_dropout | |
| self.sigmoid_scale = sigmoid_scale | |
| start = torch.nn.Conv1d(in_channels // 2, hidden_channels, 1) | |
| start = torch.nn.utils.weight_norm(start) | |
| self.start = start | |
| # Initializing last layer to 0 makes the affine coupling layers | |
| # do nothing at first. It helps to stabilze training. | |
| end = torch.nn.Conv1d(hidden_channels, in_channels, 1) | |
| end.weight.data.zero_() | |
| end.bias.data.zero_() | |
| self.end = end | |
| self.wn = modules.WN( | |
| in_channels, | |
| hidden_channels, | |
| kernel_size, | |
| dilation_rate, | |
| n_layers, | |
| gin_channels, | |
| p_dropout, | |
| ) | |
| def forward(self, x, x_mask=None, reverse=False, g=None, **kwargs): | |
| b, c, t = x.size() | |
| if x_mask is None: | |
| x_mask = 1 | |
| x_0, x_1 = x[:, : self.in_channels // 2], x[:, self.in_channels // 2 :] | |
| x = self.start(x_0) * x_mask | |
| x = self.wn(x, x_mask, g) | |
| out = self.end(x) | |
| z_0 = x_0 | |
| m = out[:, : self.in_channels // 2, :] | |
| logs = out[:, self.in_channels // 2 :, :] | |
| if self.sigmoid_scale: | |
| logs = torch.log(1e-6 + torch.sigmoid(logs + 2)) | |
| if reverse: | |
| z_1 = (x_1 - m) * torch.exp(-logs) * x_mask | |
| logdet = None | |
| else: | |
| z_1 = (m + torch.exp(logs) * x_1) * x_mask | |
| logdet = torch.sum(logs * x_mask, [1, 2]) | |
| z = torch.cat([z_0, z_1], 1) | |
| return z, logdet | |
| def store_inverse(self): | |
| self.wn.remove_weight_norm() | |
| class MultiHeadAttention(nn.Module): | |
| def __init__( | |
| self, | |
| channels, | |
| out_channels, | |
| n_heads, | |
| window_size=None, | |
| heads_share=True, | |
| p_dropout=0.0, | |
| block_length=None, | |
| proximal_bias=False, | |
| proximal_init=False, | |
| ): | |
| super().__init__() | |
| assert channels % n_heads == 0 | |
| self.channels = channels | |
| self.out_channels = out_channels | |
| self.n_heads = n_heads | |
| self.window_size = window_size | |
| self.heads_share = heads_share | |
| self.block_length = block_length | |
| self.proximal_bias = proximal_bias | |
| self.p_dropout = p_dropout | |
| self.attn = None | |
| self.k_channels = channels // n_heads | |
| self.conv_q = nn.Conv1d(channels, channels, 1) | |
| self.conv_k = nn.Conv1d(channels, channels, 1) | |
| self.conv_v = nn.Conv1d(channels, channels, 1) | |
| if window_size is not None: | |
| n_heads_rel = 1 if heads_share else n_heads | |
| rel_stddev = self.k_channels ** -0.5 | |
| self.emb_rel_k = nn.Parameter( | |
| torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) | |
| * rel_stddev | |
| ) | |
| self.emb_rel_v = nn.Parameter( | |
| torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) | |
| * rel_stddev | |
| ) | |
| self.conv_o = nn.Conv1d(channels, out_channels, 1) | |
| self.drop = nn.Dropout(p_dropout) | |
| nn.init.xavier_uniform_(self.conv_q.weight) | |
| nn.init.xavier_uniform_(self.conv_k.weight) | |
| if proximal_init: | |
| self.conv_k.weight.data.copy_(self.conv_q.weight.data) | |
| self.conv_k.bias.data.copy_(self.conv_q.bias.data) | |
| nn.init.xavier_uniform_(self.conv_v.weight) | |
| def forward(self, x, c, attn_mask=None): | |
| q = self.conv_q(x) | |
| k = self.conv_k(c) | |
| v = self.conv_v(c) | |
| x, self.attn = self.attention(q, k, v, mask=attn_mask) | |
| x = self.conv_o(x) | |
| return x | |
| def attention(self, query, key, value, mask=None): | |
| # reshape [b, d, t] -> [b, n_h, t, d_k] | |
| 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, key.transpose(-2, -1)) / math.sqrt(self.k_channels) | |
| if self.window_size is not None: | |
| assert ( | |
| t_s == t_t | |
| ), "Relative attention is only available for self-attention." | |
| key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s) | |
| rel_logits = self._matmul_with_relative_keys(query, key_relative_embeddings) | |
| rel_logits = self._relative_position_to_absolute_position(rel_logits) | |
| scores_local = rel_logits / math.sqrt(self.k_channels) | |
| scores = scores + scores_local | |
| if self.proximal_bias: | |
| assert t_s == t_t, "Proximal bias is only available for self-attention." | |
| scores = scores + self._attention_bias_proximal(t_s).to( | |
| device=scores.device, dtype=scores.dtype | |
| ) | |
| if mask is not None: | |
| scores = scores.masked_fill(mask == 0, -1e4) | |
| if self.block_length is not None: | |
| block_mask = ( | |
| torch.ones_like(scores) | |
| .triu(-self.block_length) | |
| .tril(self.block_length) | |
| ) | |
| scores = scores * block_mask + -1e4 * (1 - block_mask) | |
| p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s] | |
| p_attn = self.drop(p_attn) | |
| output = torch.matmul(p_attn, value) | |
| if self.window_size is not None: | |
| relative_weights = self._absolute_position_to_relative_position(p_attn) | |
| value_relative_embeddings = self._get_relative_embeddings( | |
| self.emb_rel_v, t_s | |
| ) | |
| output = output + self._matmul_with_relative_values( | |
| relative_weights, value_relative_embeddings | |
| ) | |
| output = ( | |
| output.transpose(2, 3).contiguous().view(b, d, t_t) | |
| ) # [b, n_h, t_t, d_k] -> [b, d, t_t] | |
| return output, p_attn | |
| def _matmul_with_relative_values(self, x, y): | |
| """ | |
| x: [b, h, l, m] | |
| y: [h or 1, m, d] | |
| ret: [b, h, l, d] | |
| """ | |
| ret = torch.matmul(x, y.unsqueeze(0)) | |
| return ret | |
| def _matmul_with_relative_keys(self, x, y): | |
| """ | |
| x: [b, h, l, d] | |
| y: [h or 1, m, d] | |
| ret: [b, h, l, m] | |
| """ | |
| ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1)) | |
| return ret | |
| def _get_relative_embeddings(self, relative_embeddings, length): | |
| max_relative_position = 2 * self.window_size + 1 | |
| # Pad first before slice to avoid using cond ops. | |
| pad_length = max(length - (self.window_size + 1), 0) | |
| slice_start_position = max((self.window_size + 1) - length, 0) | |
| slice_end_position = slice_start_position + 2 * length - 1 | |
| if pad_length > 0: | |
| padded_relative_embeddings = F.pad( | |
| relative_embeddings, | |
| commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]), | |
| ) | |
| else: | |
| padded_relative_embeddings = relative_embeddings | |
| used_relative_embeddings = padded_relative_embeddings[ | |
| :, slice_start_position:slice_end_position | |
| ] | |
| return used_relative_embeddings | |
| def _relative_position_to_absolute_position(self, x): | |
| """ | |
| x: [b, h, l, 2*l-1] | |
| ret: [b, h, l, l] | |
| """ | |
| batch, heads, length, _ = x.size() | |
| # Concat columns of pad to shift from relative to absolute indexing. | |
| x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]])) | |
| # Concat extra elements so to add up to shape (len+1, 2*len-1). | |
| x_flat = x.view([batch, heads, length * 2 * length]) | |
| x_flat = F.pad( | |
| x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]]) | |
| ) | |
| # Reshape and slice out the padded elements. | |
| x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[ | |
| :, :, :length, length - 1 : | |
| ] | |
| return x_final | |
| def _absolute_position_to_relative_position(self, x): | |
| """ | |
| x: [b, h, l, l] | |
| ret: [b, h, l, 2*l-1] | |
| """ | |
| batch, heads, length, _ = x.size() | |
| # padd along column | |
| x = F.pad( | |
| x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]]) | |
| ) | |
| x_flat = x.view([batch, heads, length ** 2 + length * (length - 1)]) | |
| # add 0's in the beginning that will skew the elements after reshape | |
| x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]])) | |
| x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:] | |
| return x_final | |
| def _attention_bias_proximal(self, length): | |
| """Bias for self-attention to encourage attention to close positions. | |
| Args: | |
| length: an integer scalar. | |
| Returns: | |
| a Tensor with shape [1, 1, length, length] | |
| """ | |
| r = torch.arange(length, dtype=torch.float32) | |
| diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1) | |
| return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0) | |
| class FFN(nn.Module): | |
| def __init__( | |
| self, | |
| in_channels, | |
| out_channels, | |
| filter_channels, | |
| kernel_size, | |
| p_dropout=0.0, | |
| activation=None, | |
| ): | |
| super().__init__() | |
| self.in_channels = in_channels | |
| self.out_channels = out_channels | |
| self.filter_channels = filter_channels | |
| self.kernel_size = kernel_size | |
| self.p_dropout = p_dropout | |
| self.activation = activation | |
| self.conv_1 = nn.Conv1d( | |
| in_channels, filter_channels, kernel_size, padding=kernel_size // 2 | |
| ) | |
| self.conv_2 = nn.Conv1d( | |
| filter_channels, out_channels, kernel_size, padding=kernel_size // 2 | |
| ) | |
| self.drop = nn.Dropout(p_dropout) | |
| def forward(self, x, x_mask): | |
| x = self.conv_1(x * x_mask) | |
| if self.activation == "gelu": | |
| x = x * torch.sigmoid(1.702 * x) | |
| else: | |
| x = torch.relu(x) | |
| x = self.drop(x) | |
| x = self.conv_2(x * x_mask) | |
| return x * x_mask | |