import math import torch from torch import nn from torch.nn import functional as F from module import commons from module.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=4, isflow=False, **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)) if isflow: cond_layer = torch.nn.Conv1d( kwargs["gin_channels"], 2 * hidden_channels * n_layers, 1 ) self.cond_pre = torch.nn.Conv1d(hidden_channels, 2 * hidden_channels, 1) self.cond_layer = weight_norm_modules(cond_layer, name="weight") self.gin_channels = kwargs["gin_channels"] def forward(self, x, x_mask, g=None): attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1) x = x * x_mask if g is not None: g = self.cond_layer(g) for i in range(self.n_layers): if g is not None: x = self.cond_pre(x) cond_offset = i * 2 * self.hidden_channels g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :] x = commons.fused_add_tanh_sigmoid_multiply( x, g_l, torch.IntTensor([self.hidden_channels]) ) 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=0.0, 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=0.0, 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=0.0, 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 import torch.nn as nn from torch.nn.utils import remove_weight_norm, weight_norm class Depthwise_Separable_Conv1D(nn.Module): def __init__( self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, bias=True, padding_mode="zeros", # TODO: refine this type device=None, dtype=None, ): super().__init__() self.depth_conv = nn.Conv1d( in_channels=in_channels, out_channels=in_channels, kernel_size=kernel_size, groups=in_channels, stride=stride, padding=padding, dilation=dilation, bias=bias, padding_mode=padding_mode, device=device, dtype=dtype, ) self.point_conv = nn.Conv1d( in_channels=in_channels, out_channels=out_channels, kernel_size=1, bias=bias, device=device, dtype=dtype, ) def forward(self, input): return self.point_conv(self.depth_conv(input)) def weight_norm(self): self.depth_conv = weight_norm(self.depth_conv, name="weight") self.point_conv = weight_norm(self.point_conv, name="weight") def remove_weight_norm(self): self.depth_conv = remove_weight_norm(self.depth_conv, name="weight") self.point_conv = remove_weight_norm(self.point_conv, name="weight") class Depthwise_Separable_TransposeConv1D(nn.Module): def __init__( self, in_channels, out_channels, kernel_size, stride=1, padding=0, output_padding=0, bias=True, dilation=1, padding_mode="zeros", # TODO: refine this type device=None, dtype=None, ): super().__init__() self.depth_conv = nn.ConvTranspose1d( in_channels=in_channels, out_channels=in_channels, kernel_size=kernel_size, groups=in_channels, stride=stride, output_padding=output_padding, padding=padding, dilation=dilation, bias=bias, padding_mode=padding_mode, device=device, dtype=dtype, ) self.point_conv = nn.Conv1d( in_channels=in_channels, out_channels=out_channels, kernel_size=1, bias=bias, device=device, dtype=dtype, ) def forward(self, input): return self.point_conv(self.depth_conv(input)) def weight_norm(self): self.depth_conv = weight_norm(self.depth_conv, name="weight") self.point_conv = weight_norm(self.point_conv, name="weight") def remove_weight_norm(self): remove_weight_norm(self.depth_conv, name="weight") remove_weight_norm(self.point_conv, name="weight") def weight_norm_modules(module, name="weight", dim=0): if isinstance(module, Depthwise_Separable_Conv1D) or isinstance( module, Depthwise_Separable_TransposeConv1D ): module.weight_norm() return module else: return weight_norm(module, name, dim) def remove_weight_norm_modules(module, name="weight"): if isinstance(module, Depthwise_Separable_Conv1D) or isinstance( module, Depthwise_Separable_TransposeConv1D ): module.remove_weight_norm() else: remove_weight_norm(module, name) class FFT(nn.Module): def __init__( self, hidden_channels, filter_channels, n_heads, n_layers=1, kernel_size=1, p_dropout=0.0, proximal_bias=False, proximal_init=True, isflow=False, **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 if isflow: cond_layer = torch.nn.Conv1d( kwargs["gin_channels"], 2 * hidden_channels * n_layers, 1 ) self.cond_pre = torch.nn.Conv1d(hidden_channels, 2 * hidden_channels, 1) self.cond_layer = weight_norm_modules(cond_layer, name="weight") self.gin_channels = kwargs["gin_channels"] self.drop = nn.Dropout(p_dropout) self.self_attn_layers = nn.ModuleList() self.norm_layers_0 = nn.ModuleList() self.ffn_layers = nn.ModuleList() self.norm_layers_1 = 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.ffn_layers.append( FFN( hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True, ) ) self.norm_layers_1.append(LayerNorm(hidden_channels)) def forward(self, x, x_mask, g=None): """ x: decoder input h: encoder output """ if g is not None: g = self.cond_layer(g) self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to( device=x.device, dtype=x.dtype ) x = x * x_mask for i in range(self.n_layers): if g is not None: x = self.cond_pre(x) cond_offset = i * 2 * self.hidden_channels g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :] x = commons.fused_add_tanh_sigmoid_multiply( x, g_l, torch.IntTensor([self.hidden_channels]) ) 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.ffn_layers[i](x, x_mask) y = self.drop(y) x = self.norm_layers_1[i](x + y) x = x * x_mask return x class TransformerCouplingLayer(nn.Module): def __init__( self, channels, hidden_channels, kernel_size, n_layers, n_heads, p_dropout=0, filter_channels=0, mean_only=False, wn_sharing_parameter=None, gin_channels=0, ): assert channels % 2 == 0, "channels should be divisible by 2" super().__init__() self.channels = channels self.hidden_channels = hidden_channels self.kernel_size = kernel_size self.n_layers = n_layers self.half_channels = channels // 2 self.mean_only = mean_only self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1) self.enc = ( Encoder( hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, isflow=True, gin_channels=gin_channels, ) if wn_sharing_parameter is None else wn_sharing_parameter ) self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1) self.post.weight.data.zero_() self.post.bias.data.zero_() def forward(self, x, x_mask, g=None, reverse=False): x0, x1 = torch.split(x, [self.half_channels] * 2, 1) h = self.pre(x0) * x_mask h = self.enc(h, x_mask, g=g) stats = self.post(h) * x_mask if not self.mean_only: m, logs = torch.split(stats, [self.half_channels] * 2, 1) else: m = stats logs = torch.zeros_like(m) if not reverse: x1 = m + x1 * torch.exp(logs) * x_mask x = torch.cat([x0, x1], 1) logdet = torch.sum(logs, [1, 2]) return x, logdet else: x1 = (x1 - m) * torch.exp(-logs) * x_mask x = torch.cat([x0, x1], 1) return x