261 lines
8.9 KiB
Python
261 lines
8.9 KiB
Python
from torch import nn
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import torch
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from ..layers import LayerNorm
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class ConvolutionModule(nn.Module):
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"""ConvolutionModule in Conformer model.
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Args:
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channels (int): The number of channels of conv layers.
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kernel_size (int): Kernerl size of conv layers.
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"""
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def __init__(self, channels, kernel_size, activation=nn.ReLU(), bias=True):
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"""Construct an ConvolutionModule object."""
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super(ConvolutionModule, self).__init__()
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# kernerl_size should be a odd number for 'SAME' padding
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assert (kernel_size - 1) % 2 == 0
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self.pointwise_conv1 = nn.Conv1d(
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channels,
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2 * channels,
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kernel_size=1,
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stride=1,
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padding=0,
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bias=bias,
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)
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self.depthwise_conv = nn.Conv1d(
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channels,
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channels,
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kernel_size,
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stride=1,
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padding=(kernel_size - 1) // 2,
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groups=channels,
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bias=bias,
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)
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self.norm = nn.BatchNorm1d(channels)
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self.pointwise_conv2 = nn.Conv1d(
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channels,
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channels,
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kernel_size=1,
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stride=1,
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padding=0,
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bias=bias,
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)
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self.activation = activation
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def forward(self, x):
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"""Compute convolution module.
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Args:
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x (torch.Tensor): Input tensor (#batch, time, channels).
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Returns:
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torch.Tensor: Output tensor (#batch, time, channels).
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"""
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# exchange the temporal dimension and the feature dimension
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x = x.transpose(1, 2)
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# GLU mechanism
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x = self.pointwise_conv1(x) # (batch, 2*channel, dim)
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x = nn.functional.glu(x, dim=1) # (batch, channel, dim)
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# 1D Depthwise Conv
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x = self.depthwise_conv(x)
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x = self.activation(self.norm(x))
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x = self.pointwise_conv2(x)
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return x.transpose(1, 2)
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class MultiLayeredConv1d(torch.nn.Module):
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"""Multi-layered conv1d for Transformer block.
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This is a module of multi-leyered conv1d designed
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to replace positionwise feed-forward network
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in Transforner block, which is introduced in
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`FastSpeech: Fast, Robust and Controllable Text to Speech`_.
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.. _`FastSpeech: Fast, Robust and Controllable Text to Speech`:
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https://arxiv.org/pdf/1905.09263.pdf
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"""
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def __init__(self, in_chans, hidden_chans, kernel_size, dropout_rate):
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"""Initialize MultiLayeredConv1d module.
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Args:
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in_chans (int): Number of input channels.
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hidden_chans (int): Number of hidden channels.
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kernel_size (int): Kernel size of conv1d.
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dropout_rate (float): Dropout rate.
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"""
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super(MultiLayeredConv1d, self).__init__()
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self.w_1 = torch.nn.Conv1d(
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in_chans,
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hidden_chans,
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kernel_size,
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stride=1,
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padding=(kernel_size - 1) // 2,
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)
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self.w_2 = torch.nn.Conv1d(
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hidden_chans,
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in_chans,
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kernel_size,
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stride=1,
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padding=(kernel_size - 1) // 2,
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)
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self.dropout = torch.nn.Dropout(dropout_rate)
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def forward(self, x):
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"""Calculate forward propagation.
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Args:
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x (torch.Tensor): Batch of input tensors (B, T, in_chans).
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Returns:
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torch.Tensor: Batch of output tensors (B, T, hidden_chans).
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"""
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x = torch.relu(self.w_1(x.transpose(-1, 1))).transpose(-1, 1)
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return self.w_2(self.dropout(x).transpose(-1, 1)).transpose(-1, 1)
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class Swish(torch.nn.Module):
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"""Construct an Swish object."""
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def forward(self, x):
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"""Return Swich activation function."""
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return x * torch.sigmoid(x)
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class EncoderLayer(nn.Module):
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"""Encoder layer module.
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Args:
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size (int): Input dimension.
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self_attn (torch.nn.Module): Self-attention module instance.
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`MultiHeadedAttention` or `RelPositionMultiHeadedAttention` instance
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can be used as the argument.
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feed_forward (torch.nn.Module): Feed-forward module instance.
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`PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance
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can be used as the argument.
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feed_forward_macaron (torch.nn.Module): Additional feed-forward module instance.
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`PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance
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can be used as the argument.
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conv_module (torch.nn.Module): Convolution module instance.
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`ConvlutionModule` instance can be used as the argument.
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dropout_rate (float): Dropout rate.
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normalize_before (bool): Whether to use layer_norm before the first block.
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concat_after (bool): Whether to concat attention layer's input and output.
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if True, additional linear will be applied.
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i.e. x -> x + linear(concat(x, att(x)))
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if False, no additional linear will be applied. i.e. x -> x + att(x)
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"""
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def __init__(
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self,
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size,
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self_attn,
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feed_forward,
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feed_forward_macaron,
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conv_module,
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dropout_rate,
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normalize_before=True,
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concat_after=False,
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):
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"""Construct an EncoderLayer object."""
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super(EncoderLayer, self).__init__()
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self.self_attn = self_attn
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self.feed_forward = feed_forward
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self.feed_forward_macaron = feed_forward_macaron
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self.conv_module = conv_module
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self.norm_ff = LayerNorm(size) # for the FNN module
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self.norm_mha = LayerNorm(size) # for the MHA module
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if feed_forward_macaron is not None:
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self.norm_ff_macaron = LayerNorm(size)
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self.ff_scale = 0.5
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else:
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self.ff_scale = 1.0
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if self.conv_module is not None:
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self.norm_conv = LayerNorm(size) # for the CNN module
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self.norm_final = LayerNorm(size) # for the final output of the block
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self.dropout = nn.Dropout(dropout_rate)
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self.size = size
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self.normalize_before = normalize_before
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self.concat_after = concat_after
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if self.concat_after:
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self.concat_linear = nn.Linear(size + size, size)
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def forward(self, x_input, mask, cache=None):
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"""Compute encoded features.
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Args:
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x_input (Union[Tuple, torch.Tensor]): Input tensor w/ or w/o pos emb.
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- w/ pos emb: Tuple of tensors [(#batch, time, size), (1, time, size)].
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- w/o pos emb: Tensor (#batch, time, size).
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mask (torch.Tensor): Mask tensor for the input (#batch, time).
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cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size).
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Returns:
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torch.Tensor: Output tensor (#batch, time, size).
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torch.Tensor: Mask tensor (#batch, time).
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"""
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if isinstance(x_input, tuple):
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x, pos_emb = x_input[0], x_input[1]
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else:
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x, pos_emb = x_input, None
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# whether to use macaron style
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if self.feed_forward_macaron is not None:
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residual = x
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if self.normalize_before:
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x = self.norm_ff_macaron(x)
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x = residual + self.ff_scale * self.dropout(self.feed_forward_macaron(x))
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if not self.normalize_before:
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x = self.norm_ff_macaron(x)
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# multi-headed self-attention module
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residual = x
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if self.normalize_before:
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x = self.norm_mha(x)
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if cache is None:
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x_q = x
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else:
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assert cache.shape == (x.shape[0], x.shape[1] - 1, self.size)
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x_q = x[:, -1:, :]
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residual = residual[:, -1:, :]
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mask = None if mask is None else mask[:, -1:, :]
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if pos_emb is not None:
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x_att = self.self_attn(x_q, x, x, pos_emb, mask)
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else:
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x_att = self.self_attn(x_q, x, x, mask)
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if self.concat_after:
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x_concat = torch.cat((x, x_att), dim=-1)
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x = residual + self.concat_linear(x_concat)
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else:
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x = residual + self.dropout(x_att)
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if not self.normalize_before:
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x = self.norm_mha(x)
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# convolution module
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if self.conv_module is not None:
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residual = x
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if self.normalize_before:
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x = self.norm_conv(x)
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x = residual + self.dropout(self.conv_module(x))
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if not self.normalize_before:
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x = self.norm_conv(x)
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# feed forward module
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residual = x
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if self.normalize_before:
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x = self.norm_ff(x)
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x = residual + self.ff_scale * self.dropout(self.feed_forward(x))
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if not self.normalize_before:
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x = self.norm_ff(x)
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if self.conv_module is not None:
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x = self.norm_final(x)
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if cache is not None:
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x = torch.cat([cache, x], dim=1)
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if pos_emb is not None:
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return (x, pos_emb), mask
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return x, mask
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