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# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import torch
import numpy as np
import torch.nn as nn
from utils.util import convert_pad_shape
class BaseModule(torch.nn.Module):
def __init__(self):
super(BaseModule, self).__init__()
@property
def nparams(self):
"""
Returns number of trainable parameters of the module.
"""
num_params = 0
for name, param in self.named_parameters():
if param.requires_grad:
num_params += np.prod(param.detach().cpu().numpy().shape)
return num_params
def relocate_input(self, x: list):
"""
Relocates provided tensors to the same device set for the module.
"""
device = next(self.parameters()).device
for i in range(len(x)):
if isinstance(x[i], torch.Tensor) and x[i].device != device:
x[i] = x[i].to(device)
return x
class LayerNorm(BaseModule):
def __init__(self, channels, eps=1e-4):
super(LayerNorm, self).__init__()
self.channels = channels
self.eps = eps
self.gamma = torch.nn.Parameter(torch.ones(channels))
self.beta = torch.nn.Parameter(torch.zeros(channels))
def forward(self, x):
n_dims = len(x.shape)
mean = torch.mean(x, 1, keepdim=True)
variance = torch.mean((x - mean) ** 2, 1, keepdim=True)
x = (x - mean) * torch.rsqrt(variance + self.eps)
shape = [1, -1] + [1] * (n_dims - 2)
x = x * self.gamma.view(*shape) + self.beta.view(*shape)
return x
class ConvReluNorm(BaseModule):
def __init__(
self,
in_channels,
hidden_channels,
out_channels,
kernel_size,
n_layers,
p_dropout,
eps=1e-5,
):
super(ConvReluNorm, self).__init__()
self.in_channels = in_channels
self.hidden_channels = hidden_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.n_layers = n_layers
self.p_dropout = p_dropout
self.eps = eps
self.conv_layers = torch.nn.ModuleList()
self.conv_layers.append(
torch.nn.Conv1d(
in_channels, hidden_channels, kernel_size, padding=kernel_size // 2
)
)
self.relu_drop = torch.nn.Sequential(
torch.nn.ReLU(), torch.nn.Dropout(p_dropout)
)
for _ in range(n_layers - 1):
self.conv_layers.append(
torch.nn.Conv1d(
hidden_channels,
hidden_channels,
kernel_size,
padding=kernel_size // 2,
)
)
self.proj = torch.nn.Conv1d(hidden_channels, out_channels, 1)
self.proj.weight.data.zero_()
self.proj.bias.data.zero_()
def forward(self, x, x_mask):
for i in range(self.n_layers):
x = self.conv_layers[i](x * x_mask)
x = self.instance_norm(x, x_mask)
x = self.relu_drop(x)
x = self.proj(x)
return x * x_mask
def instance_norm(self, x, mask, return_mean_std=False):
mean, std = self.calc_mean_std(x, mask)
x = (x - mean) / std
if return_mean_std:
return x, mean, std
else:
return x
def calc_mean_std(self, x, mask=None):
x = x * mask
B, C = x.shape[:2]
mn = x.view(B, C, -1).mean(-1)
sd = (x.view(B, C, -1).var(-1) + self.eps).sqrt()
mn = mn.view(B, C, *((len(x.shape) - 2) * [1]))
sd = sd.view(B, C, *((len(x.shape) - 2) * [1]))
return mn, sd
class MultiHeadAttention(BaseModule):
def __init__(
self,
channels,
out_channels,
n_heads,
window_size=None,
heads_share=True,
p_dropout=0.0,
proximal_bias=False,
proximal_init=False,
):
super(MultiHeadAttention, self).__init__()
assert channels % n_heads == 0
self.channels = channels
self.out_channels = out_channels
self.n_heads = n_heads
self.window_size = window_size
self.heads_share = heads_share
self.proximal_bias = proximal_bias
self.p_dropout = p_dropout
self.attn = None
self.k_channels = channels // n_heads
self.conv_q = torch.nn.Conv1d(channels, channels, 1)
self.conv_k = torch.nn.Conv1d(channels, channels, 1)
self.conv_v = torch.nn.Conv1d(channels, channels, 1)
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 = torch.nn.Parameter(
torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
* rel_stddev
)
self.emb_rel_v = torch.nn.Parameter(
torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
* rel_stddev
)
self.conv_o = torch.nn.Conv1d(channels, out_channels, 1)
self.drop = torch.nn.Dropout(p_dropout)
torch.nn.init.xavier_uniform_(self.conv_q.weight)
torch.nn.init.xavier_uniform_(self.conv_k.weight)
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)
torch.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):
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, key.transpose(-2, -1)) / math.sqrt(self.k_channels)
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, 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
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)
p_attn = torch.nn.functional.softmax(scores, dim=-1)
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)
return output, p_attn
def _matmul_with_relative_values(self, x, y):
ret = torch.matmul(x, y.unsqueeze(0))
return ret
def _matmul_with_relative_keys(self, x, y):
ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
return ret
def _get_relative_embeddings(self, relative_embeddings, length):
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 = torch.nn.functional.pad(
relative_embeddings,
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):
batch, heads, length, _ = x.size()
x = torch.nn.functional.pad(
x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]])
)
x_flat = x.view([batch, heads, length * 2 * length])
x_flat = torch.nn.functional.pad(
x_flat, convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
)
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):
batch, heads, length, _ = x.size()
x = torch.nn.functional.pad(
x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
)
x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
x_flat = torch.nn.functional.pad(
x_flat, 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):
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(BaseModule):
def __init__(
self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0.0
):
super(FFN, self).__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.conv_1 = torch.nn.Conv1d(
in_channels, filter_channels, kernel_size, padding=kernel_size // 2
)
self.conv_2 = torch.nn.Conv1d(
filter_channels, out_channels, kernel_size, padding=kernel_size // 2
)
self.drop = torch.nn.Dropout(p_dropout)
def forward(self, x, x_mask):
x = self.conv_1(x * x_mask)
x = torch.relu(x)
x = self.drop(x)
x = self.conv_2(x * x_mask)
return x * x_mask
class Encoder(BaseModule):
def __init__(
self,
hidden_channels,
filter_channels,
n_heads=2,
n_layers=6,
kernel_size=3,
p_dropout=0.1,
window_size=4,
**kwargs
):
super(Encoder, self).__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 = torch.nn.Dropout(p_dropout)
self.attn_layers = torch.nn.ModuleList()
self.norm_layers_1 = torch.nn.ModuleList()
self.ffn_layers = torch.nn.ModuleList()
self.norm_layers_2 = torch.nn.ModuleList()
for _ in range(self.n_layers):
self.attn_layers.append(
MultiHeadAttention(
hidden_channels,
hidden_channels,
n_heads,
window_size=window_size,
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,
)
)
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.n_layers):
x = x * x_mask
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 Conformer(BaseModule):
def __init__(self, cfg):
super().__init__()
self.cfg = cfg
self.n_heads = self.cfg.n_heads
self.n_layers = self.cfg.n_layers
self.hidden_channels = self.cfg.input_dim
self.filter_channels = self.cfg.filter_channels
self.output_dim = self.cfg.output_dim
self.dropout = self.cfg.dropout
self.conformer_encoder = Encoder(
self.hidden_channels,
self.filter_channels,
n_heads=self.n_heads,
n_layers=self.n_layers,
kernel_size=3,
p_dropout=self.dropout,
window_size=4,
)
self.projection = nn.Conv1d(self.hidden_channels, self.output_dim, 1)
def forward(self, x, x_mask):
"""
Args:
x: (N, seq_len, input_dim)
Returns:
output: (N, seq_len, output_dim)
"""
# (N, seq_len, d_model)
x = x.transpose(1, 2)
x_mask = x_mask.transpose(1, 2)
output = self.conformer_encoder(x, x_mask)
# (N, seq_len, output_dim)
output = self.projection(output)
output = output.transpose(1, 2)
return output
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