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import copy |
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import math |
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import numpy as np |
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import torch |
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from torch import nn |
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from torch.nn import functional as F |
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from infer_pack import commons |
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from infer_pack import modules |
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from infer_pack.modules import LayerNorm |
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class Encoder(nn.Module): |
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def __init__( |
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self, |
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hidden_channels, |
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filter_channels, |
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n_heads, |
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n_layers, |
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kernel_size=1, |
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p_dropout=0.0, |
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window_size=10, |
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**kwargs |
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): |
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super().__init__() |
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self.hidden_channels = hidden_channels |
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self.filter_channels = filter_channels |
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self.n_heads = n_heads |
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self.n_layers = n_layers |
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self.kernel_size = kernel_size |
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self.p_dropout = p_dropout |
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self.window_size = window_size |
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self.drop = nn.Dropout(p_dropout) |
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self.attn_layers = nn.ModuleList() |
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self.norm_layers_1 = nn.ModuleList() |
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self.ffn_layers = nn.ModuleList() |
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self.norm_layers_2 = nn.ModuleList() |
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for i in range(self.n_layers): |
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self.attn_layers.append( |
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MultiHeadAttention( |
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hidden_channels, |
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hidden_channels, |
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n_heads, |
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p_dropout=p_dropout, |
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window_size=window_size, |
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) |
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) |
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self.norm_layers_1.append(LayerNorm(hidden_channels)) |
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self.ffn_layers.append( |
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FFN( |
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hidden_channels, |
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hidden_channels, |
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filter_channels, |
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kernel_size, |
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p_dropout=p_dropout, |
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) |
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) |
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self.norm_layers_2.append(LayerNorm(hidden_channels)) |
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def forward(self, x, x_mask): |
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attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1) |
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x = x * x_mask |
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for i in range(self.n_layers): |
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y = self.attn_layers[i](x, x, attn_mask) |
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y = self.drop(y) |
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x = self.norm_layers_1[i](x + y) |
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y = self.ffn_layers[i](x, x_mask) |
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y = self.drop(y) |
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x = self.norm_layers_2[i](x + y) |
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x = x * x_mask |
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return x |
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class Decoder(nn.Module): |
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def __init__( |
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self, |
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hidden_channels, |
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filter_channels, |
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n_heads, |
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n_layers, |
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kernel_size=1, |
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p_dropout=0.0, |
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proximal_bias=False, |
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proximal_init=True, |
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**kwargs |
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): |
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super().__init__() |
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self.hidden_channels = hidden_channels |
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self.filter_channels = filter_channels |
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self.n_heads = n_heads |
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self.n_layers = n_layers |
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self.kernel_size = kernel_size |
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self.p_dropout = p_dropout |
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self.proximal_bias = proximal_bias |
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self.proximal_init = proximal_init |
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self.drop = nn.Dropout(p_dropout) |
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self.self_attn_layers = nn.ModuleList() |
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self.norm_layers_0 = nn.ModuleList() |
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self.encdec_attn_layers = nn.ModuleList() |
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self.norm_layers_1 = nn.ModuleList() |
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self.ffn_layers = nn.ModuleList() |
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self.norm_layers_2 = nn.ModuleList() |
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for i in range(self.n_layers): |
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self.self_attn_layers.append( |
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MultiHeadAttention( |
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hidden_channels, |
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hidden_channels, |
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n_heads, |
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p_dropout=p_dropout, |
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proximal_bias=proximal_bias, |
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proximal_init=proximal_init, |
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) |
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) |
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self.norm_layers_0.append(LayerNorm(hidden_channels)) |
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self.encdec_attn_layers.append( |
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MultiHeadAttention( |
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hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout |
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) |
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) |
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self.norm_layers_1.append(LayerNorm(hidden_channels)) |
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self.ffn_layers.append( |
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FFN( |
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hidden_channels, |
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hidden_channels, |
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filter_channels, |
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kernel_size, |
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p_dropout=p_dropout, |
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causal=True, |
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) |
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) |
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self.norm_layers_2.append(LayerNorm(hidden_channels)) |
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def forward(self, x, x_mask, h, h_mask): |
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""" |
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x: decoder input |
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h: encoder output |
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""" |
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self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to( |
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device=x.device, dtype=x.dtype |
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) |
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encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1) |
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x = x * x_mask |
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for i in range(self.n_layers): |
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y = self.self_attn_layers[i](x, x, self_attn_mask) |
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y = self.drop(y) |
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x = self.norm_layers_0[i](x + y) |
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y = self.encdec_attn_layers[i](x, h, encdec_attn_mask) |
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y = self.drop(y) |
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x = self.norm_layers_1[i](x + y) |
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y = self.ffn_layers[i](x, x_mask) |
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y = self.drop(y) |
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x = self.norm_layers_2[i](x + y) |
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x = x * x_mask |
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return x |
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class MultiHeadAttention(nn.Module): |
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def __init__( |
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self, |
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channels, |
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out_channels, |
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n_heads, |
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p_dropout=0.0, |
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window_size=None, |
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heads_share=True, |
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block_length=None, |
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proximal_bias=False, |
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proximal_init=False, |
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): |
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super().__init__() |
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assert channels % n_heads == 0 |
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self.channels = channels |
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self.out_channels = out_channels |
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self.n_heads = n_heads |
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self.p_dropout = p_dropout |
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self.window_size = window_size |
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self.heads_share = heads_share |
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self.block_length = block_length |
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self.proximal_bias = proximal_bias |
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self.proximal_init = proximal_init |
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self.attn = None |
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self.k_channels = channels // n_heads |
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self.conv_q = nn.Conv1d(channels, channels, 1) |
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self.conv_k = nn.Conv1d(channels, channels, 1) |
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self.conv_v = nn.Conv1d(channels, channels, 1) |
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self.conv_o = nn.Conv1d(channels, out_channels, 1) |
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self.drop = nn.Dropout(p_dropout) |
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if window_size is not None: |
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n_heads_rel = 1 if heads_share else n_heads |
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rel_stddev = self.k_channels**-0.5 |
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self.emb_rel_k = nn.Parameter( |
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torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) |
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* rel_stddev |
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) |
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self.emb_rel_v = nn.Parameter( |
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torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) |
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* rel_stddev |
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) |
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nn.init.xavier_uniform_(self.conv_q.weight) |
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nn.init.xavier_uniform_(self.conv_k.weight) |
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nn.init.xavier_uniform_(self.conv_v.weight) |
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if proximal_init: |
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with torch.no_grad(): |
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self.conv_k.weight.copy_(self.conv_q.weight) |
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self.conv_k.bias.copy_(self.conv_q.bias) |
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def forward(self, x, c, attn_mask=None): |
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q = self.conv_q(x) |
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k = self.conv_k(c) |
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v = self.conv_v(c) |
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x, self.attn = self.attention(q, k, v, mask=attn_mask) |
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x = self.conv_o(x) |
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return x |
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def attention(self, query, key, value, mask=None): |
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b, d, t_s, t_t = (*key.size(), query.size(2)) |
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query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3) |
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key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) |
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value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) |
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scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1)) |
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if self.window_size is not None: |
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assert ( |
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t_s == t_t |
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), "Relative attention is only available for self-attention." |
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key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s) |
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rel_logits = self._matmul_with_relative_keys( |
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query / math.sqrt(self.k_channels), key_relative_embeddings |
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) |
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scores_local = self._relative_position_to_absolute_position(rel_logits) |
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scores = scores + scores_local |
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if self.proximal_bias: |
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assert t_s == t_t, "Proximal bias is only available for self-attention." |
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scores = scores + self._attention_bias_proximal(t_s).to( |
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device=scores.device, dtype=scores.dtype |
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) |
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if mask is not None: |
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scores = scores.masked_fill(mask == 0, -1e4) |
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if self.block_length is not None: |
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assert ( |
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t_s == t_t |
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), "Local attention is only available for self-attention." |
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block_mask = ( |
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torch.ones_like(scores) |
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.triu(-self.block_length) |
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.tril(self.block_length) |
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) |
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scores = scores.masked_fill(block_mask == 0, -1e4) |
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p_attn = F.softmax(scores, dim=-1) |
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p_attn = self.drop(p_attn) |
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output = torch.matmul(p_attn, value) |
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if self.window_size is not None: |
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relative_weights = self._absolute_position_to_relative_position(p_attn) |
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value_relative_embeddings = self._get_relative_embeddings( |
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self.emb_rel_v, t_s |
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) |
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output = output + self._matmul_with_relative_values( |
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relative_weights, value_relative_embeddings |
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) |
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output = ( |
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output.transpose(2, 3).contiguous().view(b, d, t_t) |
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) |
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return output, p_attn |
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def _matmul_with_relative_values(self, x, y): |
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""" |
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x: [b, h, l, m] |
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y: [h or 1, m, d] |
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ret: [b, h, l, d] |
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""" |
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ret = torch.matmul(x, y.unsqueeze(0)) |
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return ret |
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def _matmul_with_relative_keys(self, x, y): |
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""" |
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x: [b, h, l, d] |
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y: [h or 1, m, d] |
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ret: [b, h, l, m] |
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""" |
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ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1)) |
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return ret |
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def _get_relative_embeddings(self, relative_embeddings, length): |
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max_relative_position = 2 * self.window_size + 1 |
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pad_length = max(length - (self.window_size + 1), 0) |
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slice_start_position = max((self.window_size + 1) - length, 0) |
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slice_end_position = slice_start_position + 2 * length - 1 |
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if pad_length > 0: |
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padded_relative_embeddings = F.pad( |
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relative_embeddings, |
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commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]), |
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) |
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else: |
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padded_relative_embeddings = relative_embeddings |
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used_relative_embeddings = padded_relative_embeddings[ |
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:, slice_start_position:slice_end_position |
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] |
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return used_relative_embeddings |
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def _relative_position_to_absolute_position(self, x): |
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""" |
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x: [b, h, l, 2*l-1] |
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ret: [b, h, l, l] |
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""" |
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batch, heads, length, _ = x.size() |
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x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]])) |
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x_flat = x.view([batch, heads, length * 2 * length]) |
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x_flat = F.pad( |
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x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]]) |
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) |
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x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[ |
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:, :, :length, length - 1 : |
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] |
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return x_final |
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def _absolute_position_to_relative_position(self, x): |
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""" |
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x: [b, h, l, l] |
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ret: [b, h, l, 2*l-1] |
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""" |
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batch, heads, length, _ = x.size() |
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x = F.pad( |
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x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]]) |
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) |
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x_flat = x.view([batch, heads, length**2 + length * (length - 1)]) |
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x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]])) |
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x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:] |
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return x_final |
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def _attention_bias_proximal(self, length): |
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"""Bias for self-attention to encourage attention to close positions. |
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Args: |
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length: an integer scalar. |
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Returns: |
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a Tensor with shape [1, 1, length, length] |
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""" |
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r = torch.arange(length, dtype=torch.float32) |
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diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1) |
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return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0) |
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class FFN(nn.Module): |
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def __init__( |
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self, |
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in_channels, |
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out_channels, |
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filter_channels, |
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kernel_size, |
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p_dropout=0.0, |
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activation=None, |
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causal=False, |
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): |
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super().__init__() |
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self.in_channels = in_channels |
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self.out_channels = out_channels |
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self.filter_channels = filter_channels |
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self.kernel_size = kernel_size |
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self.p_dropout = p_dropout |
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self.activation = activation |
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self.causal = causal |
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if causal: |
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self.padding = self._causal_padding |
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else: |
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self.padding = self._same_padding |
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self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size) |
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self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size) |
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self.drop = nn.Dropout(p_dropout) |
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def forward(self, x, x_mask): |
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x = self.conv_1(self.padding(x * x_mask)) |
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if self.activation == "gelu": |
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x = x * torch.sigmoid(1.702 * x) |
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else: |
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x = torch.relu(x) |
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x = self.drop(x) |
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x = self.conv_2(self.padding(x * x_mask)) |
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return x * x_mask |
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def _causal_padding(self, x): |
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if self.kernel_size == 1: |
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return x |
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pad_l = self.kernel_size - 1 |
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pad_r = 0 |
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padding = [[0, 0], [0, 0], [pad_l, pad_r]] |
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x = F.pad(x, commons.convert_pad_shape(padding)) |
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return x |
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def _same_padding(self, x): |
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if self.kernel_size == 1: |
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return x |
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pad_l = (self.kernel_size - 1) // 2 |
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pad_r = self.kernel_size // 2 |
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padding = [[0, 0], [0, 0], [pad_l, pad_r]] |
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x = F.pad(x, commons.convert_pad_shape(padding)) |
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return x |
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