transformer.py
# 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 numpy as np
import torch
import torch.nn as nn
import math
from torch.nn import functional as F
class StyleAdaptiveLayerNorm(nn.Module):
def __init__(self, normalized_shape, eps=1e-5):
super().__init__()
self.in_dim = normalized_shape
self.norm = nn.LayerNorm(self.in_dim, eps=eps, elementwise_affine=False)
self.style = nn.Linear(self.in_dim, self.in_dim * 2)
self.style.bias.data[: self.in_dim] = 1
self.style.bias.data[self.in_dim :] = 0
def forward(self, x, condition):
# x: (B, T, d); condition: (B, T, d)
style = self.style(torch.mean(condition, dim=1, keepdim=True))
gamma, beta = style.chunk(2, -1)
out = self.norm(x)
out = gamma * out + beta
return out
class PositionalEncoding(nn.Module):
def __init__(self, d_model, dropout, max_len=5000):
super().__init__()
self.dropout = dropout
position = torch.arange(max_len).unsqueeze(1)
div_term = torch.exp(
torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model)
)
pe = torch.zeros(max_len, 1, d_model)
pe[:, 0, 0::2] = torch.sin(position * div_term)
pe[:, 0, 1::2] = torch.cos(position * div_term)
self.register_buffer("pe", pe)
def forward(self, x):
x = x + self.pe[: x.size(0)]
return F.dropout(x, self.dropout, training=self.training)
class TransformerFFNLayer(nn.Module):
def __init__(
self, encoder_hidden, conv_filter_size, conv_kernel_size, encoder_dropout
):
super().__init__()
self.encoder_hidden = encoder_hidden
self.conv_filter_size = conv_filter_size
self.conv_kernel_size = conv_kernel_size
self.encoder_dropout = encoder_dropout
self.ffn_1 = nn.Conv1d(
self.encoder_hidden,
self.conv_filter_size,
self.conv_kernel_size,
padding=self.conv_kernel_size // 2,
)
self.ffn_1.weight.data.normal_(0.0, 0.02)
self.ffn_2 = nn.Linear(self.conv_filter_size, self.encoder_hidden)
self.ffn_2.weight.data.normal_(0.0, 0.02)
def forward(self, x):
# x: (B, T, d)
x = self.ffn_1(x.permute(0, 2, 1)).permute(
0, 2, 1
) # (B, T, d) -> (B, d, T) -> (B, T, d)
x = F.relu(x)
x = F.dropout(x, self.encoder_dropout, training=self.training)
x = self.ffn_2(x)
return x
class TransformerEncoderLayer(nn.Module):
def __init__(
self,
encoder_hidden,
encoder_head,
conv_filter_size,
conv_kernel_size,
encoder_dropout,
use_cln,
):
super().__init__()
self.encoder_hidden = encoder_hidden
self.encoder_head = encoder_head
self.conv_filter_size = conv_filter_size
self.conv_kernel_size = conv_kernel_size
self.encoder_dropout = encoder_dropout
self.use_cln = use_cln
if not self.use_cln:
self.ln_1 = nn.LayerNorm(self.encoder_hidden)
self.ln_2 = nn.LayerNorm(self.encoder_hidden)
else:
self.ln_1 = StyleAdaptiveLayerNorm(self.encoder_hidden)
self.ln_2 = StyleAdaptiveLayerNorm(self.encoder_hidden)
self.self_attn = nn.MultiheadAttention(
self.encoder_hidden, self.encoder_head, batch_first=True
)
self.ffn = TransformerFFNLayer(
self.encoder_hidden,
self.conv_filter_size,
self.conv_kernel_size,
self.encoder_dropout,
)
def forward(self, x, key_padding_mask, conditon=None):
# x: (B, T, d); key_padding_mask: (B, T), mask is 0; condition: (B, T, d)
# self attention
residual = x
if self.use_cln:
x = self.ln_1(x, conditon)
else:
x = self.ln_1(x)
if key_padding_mask != None:
key_padding_mask_input = ~(key_padding_mask.bool())
else:
key_padding_mask_input = None
x, _ = self.self_attn(
query=x, key=x, value=x, key_padding_mask=key_padding_mask_input
)
x = F.dropout(x, self.encoder_dropout, training=self.training)
x = residual + x
# ffn
residual = x
if self.use_cln:
x = self.ln_2(x, conditon)
else:
x = self.ln_2(x)
x = self.ffn(x)
x = residual + x
return x
class TransformerEncoder(nn.Module):
def __init__(
self,
enc_emb_tokens=None,
encoder_layer=4,
encoder_hidden=256,
encoder_head=4,
conv_filter_size=1024,
conv_kernel_size=5,
encoder_dropout=0.1,
use_cln=False,
cfg=None,
):
super().__init__()
self.encoder_layer = (
encoder_layer if encoder_layer is not None else cfg.encoder_layer
)
self.encoder_hidden = (
encoder_hidden if encoder_hidden is not None else cfg.encoder_hidden
)
self.encoder_head = (
encoder_head if encoder_head is not None else cfg.encoder_head
)
self.conv_filter_size = (
conv_filter_size if conv_filter_size is not None else cfg.conv_filter_size
)
self.conv_kernel_size = (
conv_kernel_size if conv_kernel_size is not None else cfg.conv_kernel_size
)
self.encoder_dropout = (
encoder_dropout if encoder_dropout is not None else cfg.encoder_dropout
)
self.use_cln = use_cln if use_cln is not None else cfg.use_cln
if enc_emb_tokens != None:
self.use_enc_emb = True
self.enc_emb_tokens = enc_emb_tokens
else:
self.use_enc_emb = False
self.position_emb = PositionalEncoding(
self.encoder_hidden, self.encoder_dropout
)
self.layers = nn.ModuleList([])
self.layers.extend(
[
TransformerEncoderLayer(
self.encoder_hidden,
self.encoder_head,
self.conv_filter_size,
self.conv_kernel_size,
self.encoder_dropout,
self.use_cln,
)
for i in range(self.encoder_layer)
]
)
if self.use_cln:
self.last_ln = StyleAdaptiveLayerNorm(self.encoder_hidden)
else:
self.last_ln = nn.LayerNorm(self.encoder_hidden)
def forward(self, x, key_padding_mask, condition=None):
if len(x.shape) == 2 and self.use_enc_emb:
x = self.enc_emb_tokens(x)
x = self.position_emb(x)
else:
x = self.position_emb(x) # (B, T, d)
for layer in self.layers:
x = layer(x, key_padding_mask, condition)
if self.use_cln:
x = self.last_ln(x, condition)
else:
x = self.last_ln(x)
return x
