import math
import torch
from torch import nn
from torch.nn import functional as F
from TTS.tts.layers.glow_tts.glow import LayerNorm
class RelativePositionMultiHeadAttention(nn.Module):
"""Implementation of Relative Position Encoding based on
https://arxiv.org/pdf/1809.04281.pdf
"""
def __init__(self,
channels,
out_channels,
num_heads,
rel_attn_window_size=None,
heads_share=True,
dropout_p=0.,
input_length=None,
proximal_bias=False,
proximal_init=False):
super().__init__()
assert channels % num_heads == 0, " [!] channels should be divisible by num_heads."
# class attributes
self.channels = channels
self.out_channels = out_channels
self.num_heads = num_heads
self.rel_attn_window_size = rel_attn_window_size
self.heads_share = heads_share
self.input_length = input_length
self.proximal_bias = proximal_bias
self.dropout_p = dropout_p
self.attn = None
# query, key, value layers
self.k_channels = channels // num_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)
# output layers
self.conv_o = nn.Conv1d(channels, out_channels, 1)
self.dropout = nn.Dropout(dropout_p)
# relative positional encoding layers
if rel_attn_window_size is not None:
n_heads_rel = 1 if heads_share else num_heads
rel_stddev = self.k_channels**-0.5
emb_rel_k = nn.Parameter(
torch.randn(n_heads_rel, rel_attn_window_size * 2 + 1,
self.k_channels) * rel_stddev)
emb_rel_v = nn.Parameter(
torch.randn(n_heads_rel, rel_attn_window_size * 2 + 1,
self.k_channels) * rel_stddev)
self.register_parameter('emb_rel_k', emb_rel_k)
self.register_parameter('emb_rel_v', emb_rel_v)
# init layers
nn.init.xavier_uniform_(self.conv_q.weight)
nn.init.xavier_uniform_(self.conv_k.weight)
# proximal bias
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.num_heads, self.k_channels,
t_t).transpose(2, 3)
key = key.view(b, self.num_heads, self.k_channels, t_s).transpose(2, 3)
value = value.view(b, self.num_heads, self.k_channels,
t_s).transpose(2, 3)
# compute raw attention scores
scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(
self.k_channels)
# relative positional encoding
if self.rel_attn_window_size is not None:
assert t_s == t_t, "Relative attention is only available for self-attention."
# get relative key embeddings
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
# proximan bias
if self.proximal_bias:
assert t_s == t_t, "Proximal bias is only available for self-attention."
scores = scores + self._attn_proximity_bias(t_s).to(
device=scores.device, dtype=scores.dtype)
# attention score masking
if mask is not None:
# add small value to prevent oor error.
scores = scores.masked_fill(mask == 0, -1e4)
if self.input_length is not None:
block_mask = torch.ones_like(scores).triu(
-1 * self.input_length).tril(self.input_length)
scores = scores * block_mask + -1e4 * (1 - block_mask)
# attention score normalization
p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
# apply dropout to attention weights
p_attn = self.dropout(p_attn)
# compute output
output = torch.matmul(p_attn, value)
# relative positional encoding for values
if self.rel_attn_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
@staticmethod
def _matmul_with_relative_values(p_attn, re):
"""
Args:
p_attn (Tensor): attention weights.
re (Tensor): relative value embedding vector. (a_(i,j)^V)
Shapes:
p_attn: [B, H, T, V]
re: [H or 1, V, D]
logits: [B, H, T, D]
"""
logits = torch.matmul(p_attn, re.unsqueeze(0))
return logits
@staticmethod
def _matmul_with_relative_keys(query, re):
"""
Args:
query (Tensor): batch of query vectors. (x*W^Q)
re (Tensor): relative key embedding vector. (a_(i,j)^K)
Shapes:
query: [B, H, T, D]
re: [H or 1, V, D]
logits: [B, H, T, V]
"""
# logits = torch.einsum('bhld, kmd -> bhlm', [query, re.to(query.dtype)])
logits = torch.matmul(query, re.unsqueeze(0).transpose(-2, -1))
return logits
def _get_relative_embeddings(self, relative_embeddings, length):
"""Convert embedding vestors to a tensor of embeddings
"""
# Pad first before slice to avoid using cond ops.
pad_length = max(length - (self.rel_attn_window_size + 1), 0)
slice_start_position = max((self.rel_attn_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, [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
@staticmethod
def _relative_position_to_absolute_position(x):
"""Converts tensor from relative to absolute indexing for local attention.
Args:
x: [B, D, length, 2 * length - 1]
Returns:
A Tensor of shape [B, D, length, length]
"""
batch, heads, length, _ = x.size()
# Pad to shift from relative to absolute indexing.
x = F.pad(x, [0, 1, 0, 0, 0, 0, 0, 0])
# Pad 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, [0, length - 1, 0, 0, 0, 0])
# 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
@staticmethod
def _absolute_position_to_relative_position(x):
"""
x: [B, H, T, T]
ret: [B, H, T, 2*T-1]
"""
batch, heads, length, _ = x.size()
# padd along column
x = F.pad(x, [0, length - 1, 0, 0, 0, 0, 0, 0])
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, [length, 0, 0, 0, 0, 0])
x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
return x_final
@staticmethod
def _attn_proximity_bias(length):
"""Produce an attention mask that discourages distant
attention values.
Args:
length (int): an integer scalar.
Returns:
a Tensor with shape [1, 1, length, length]
"""
# L
r = torch.arange(length, dtype=torch.float32)
# L x L
diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
# scale mask values
diff = -torch.log1p(torch.abs(diff))
# 1 x 1 x L x L
return diff.unsqueeze(0).unsqueeze(0)
class FFN(nn.Module):
def __init__(self,
in_channels,
out_channels,
filter_channels,
kernel_size,
dropout_p=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.dropout_p = dropout_p
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.dropout = nn.Dropout(dropout_p)
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.dropout(x)
x = self.conv_2(x * x_mask)
return x * x_mask
class Transformer(nn.Module):
def __init__(self,
hidden_channels,
filter_channels,
num_heads,
num_layers,
kernel_size=1,
dropout_p=0.,
rel_attn_window_size=None,
input_length=None):
super().__init__()
self.hidden_channels = hidden_channels
self.filter_channels = filter_channels
self.num_heads = num_heads
self.num_layers = num_layers
self.kernel_size = kernel_size
self.dropout_p = dropout_p
self.rel_attn_window_size = rel_attn_window_size
self.dropout = nn.Dropout(dropout_p)
self.attn_layers = nn.ModuleList()
self.norm_layers_1 = nn.ModuleList()
self.ffn_layers = nn.ModuleList()
self.norm_layers_2 = nn.ModuleList()
for _ in range(self.num_layers):
self.attn_layers.append(
RelativePositionMultiHeadAttention(
hidden_channels,
hidden_channels,
num_heads,
rel_attn_window_size=rel_attn_window_size,
dropout_p=dropout_p,
input_length=input_length))
self.norm_layers_1.append(LayerNorm(hidden_channels))
self.ffn_layers.append(
FFN(hidden_channels,
hidden_channels,
filter_channels,
kernel_size,
dropout_p=dropout_p))
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.num_layers):
x = x * x_mask
y = self.attn_layers[i](x, x, attn_mask)
y = self.dropout(y)
x = self.norm_layers_1[i](x + y)
y = self.ffn_layers[i](x, x_mask)
y = self.dropout(y)
x = self.norm_layers_2[i](x + y)
x = x * x_mask
return x