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GPT_SoVITS/module/attentions_onnx.py
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354
GPT_SoVITS/module/attentions_onnx.py
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import math
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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 module import commons
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from module.modules import LayerNorm
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class LayerNorm(nn.Module):
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def __init__(self, channels, eps=1e-5):
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super().__init__()
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self.channels = channels
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self.eps = eps
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self.gamma = nn.Parameter(torch.ones(channels))
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self.beta = nn.Parameter(torch.zeros(channels))
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def forward(self, x):
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x = x.transpose(1, -1)
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x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
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return x.transpose(1, -1)
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@torch.jit.script
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def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
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n_channels_int = n_channels[0]
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in_act = input_a + input_b
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t_act = torch.tanh(in_act[:, :n_channels_int, :])
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s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
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acts = t_act * s_act
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return acts
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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=4,
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isflow=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.window_size = window_size
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# if isflow:
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# cond_layer = torch.nn.Conv1d(256, 2*hidden_channels*n_layers, 1)
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# self.cond_pre = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, 1)
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# self.cond_layer = weight_norm(cond_layer, name='weight')
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# self.gin_channels = 256
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self.cond_layer_idx = self.n_layers
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if "gin_channels" in kwargs:
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self.gin_channels = kwargs["gin_channels"]
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if self.gin_channels != 0:
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self.spk_emb_linear = nn.Linear(self.gin_channels, self.hidden_channels)
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# vits2 says 3rd block, so idx is 2 by default
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self.cond_layer_idx = (
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kwargs["cond_layer_idx"] if "cond_layer_idx" in kwargs else 2
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)
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logging.debug(self.gin_channels, self.cond_layer_idx)
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assert (
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self.cond_layer_idx < self.n_layers
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), "cond_layer_idx should be less than n_layers"
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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, g=None):
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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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if i == self.cond_layer_idx and g is not None:
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g = self.spk_emb_linear(g.transpose(1, 2))
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g = g.transpose(1, 2)
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x = x + g
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x = x * x_mask
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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 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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# reshape [b, d, t] -> [b, n_h, t, d_k]
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b, d, t_s, _ = (*key.size(), query.size(2))
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query = query.view(b, self.n_heads, self.k_channels, -1).transpose(2, 3)
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key = key.view(b, self.n_heads, self.k_channels, -1).transpose(2, 3)
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value = value.view(b, self.n_heads, self.k_channels, -1).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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key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
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rel_logits = self._matmul_with_relative_keys(query / math.sqrt(self.k_channels), key_relative_embeddings)
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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 mask is not None:
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scores = scores.masked_fill(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(self.emb_rel_v, t_s)
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output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
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output = (output.transpose(2, 3).contiguous().view(b, d, -1))
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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 first before slice to avoid using cond ops.
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pad_l = torch.zeros((1), dtype = torch.int64) + length - (self.window_size + 1)
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pad_s = torch.zeros((1), dtype = torch.int64) + (self.window_size + 1) - length
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pad_length = torch.max(pad_l, other=torch.zeros((1), dtype = torch.int64))
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slice_start_position = torch.max(pad_s, other=torch.zeros((1), dtype = torch.int64))
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slice_end_position = slice_start_position + 2 * length - 1
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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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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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# Concat columns of pad to shift from relative to absolute indexing.
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x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
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# Concat extra elements so to add up to shape (len+1, 2*len-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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# Reshape and slice out the padded elements.
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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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# padd along column
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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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# add 0's in the beginning that will skew the elements after reshape
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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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