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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., window_size=4, **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.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, p_dropout=p_dropout, window_size=window_size)) | |
| 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) | |
| x = x * x_mask | |
| for i in range(self.n_layers): | |
| 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 Decoder(nn.Module): | |
| def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **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.proximal_bias = proximal_bias | |
| self.proximal_init = proximal_init | |
| self.drop = nn.Dropout(p_dropout) | |
| self.self_attn_layers = nn.ModuleList() | |
| self.norm_layers_0 = nn.ModuleList() | |
| self.encdec_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.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init)) | |
| self.norm_layers_0.append(LayerNorm(hidden_channels)) | |
| self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout)) | |
| 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, causal=True)) | |
| self.norm_layers_2.append(LayerNorm(hidden_channels)) | |
| def forward(self, x, x_mask, h, h_mask): | |
| """ | |
| x: decoder input | |
| h: encoder output | |
| """ | |
| self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype) | |
| encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1) | |
| x = x * x_mask | |
| for i in range(self.n_layers): | |
| y = self.self_attn_layers[i](x, x, self_attn_mask) | |
| y = self.drop(y) | |
| x = self.norm_layers_0[i](x + y) | |
| y = self.encdec_attn_layers[i](x, h, encdec_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 MultiHeadAttention(nn.Module): | |
| def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, 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.p_dropout = p_dropout | |
| self.window_size = window_size | |
| self.heads_share = heads_share | |
| self.block_length = block_length | |
| self.proximal_bias = proximal_bias | |
| self.proximal_init = proximal_init | |
| 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) | |
| self.conv_o = nn.Conv1d(channels, out_channels, 1) | |
| self.drop = nn.Dropout(p_dropout) | |
| 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) | |
| nn.init.xavier_uniform_(self.conv_q.weight) | |
| nn.init.xavier_uniform_(self.conv_k.weight) | |
| nn.init.xavier_uniform_(self.conv_v.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 = 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 / math.sqrt(self.k_channels), key.transpose(-2, -1)) | |
| 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 /math.sqrt(self.k_channels), key_relative_embeddings) | |
| scores_local = self._relative_position_to_absolute_position(rel_logits) | |
| 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: | |
| assert t_s == t_t, "Local attention is only available for self-attention." | |
| block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length) | |
| scores = scores.masked_fill(block_mask == 0, -1e4) | |
| 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., activation=None, causal=False): | |
| 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.causal = causal | |
| if causal: | |
| self.padding = self._causal_padding | |
| else: | |
| self.padding = self._same_padding | |
| self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size) | |
| self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size) | |
| self.drop = nn.Dropout(p_dropout) | |
| def forward(self, x, x_mask): | |
| x = self.conv_1(self.padding(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(self.padding(x * x_mask)) | |
| return x * x_mask | |
| def _causal_padding(self, x): | |
| if self.kernel_size == 1: | |
| return x | |
| pad_l = self.kernel_size - 1 | |
| pad_r = 0 | |
| padding = [[0, 0], [0, 0], [pad_l, pad_r]] | |
| x = F.pad(x, commons.convert_pad_shape(padding)) | |
| return x | |
| def _same_padding(self, x): | |
| if self.kernel_size == 1: | |
| return x | |
| pad_l = (self.kernel_size - 1) // 2 | |
| pad_r = self.kernel_size // 2 | |
| padding = [[0, 0], [0, 0], [pad_l, pad_r]] | |
| x = F.pad(x, commons.convert_pad_shape(padding)) | |
| return x | |