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# Copyright 2020 Johns Hopkins University (Shinji Watanabe)
# Northwestern Polytechnical University (Pengcheng Guo)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
# Adapted by Florian Lux 2021
import torch
from torch import nn
from Layers.LayerNorm import LayerNorm
class EncoderLayer(nn.Module):
"""
Encoder layer module.
Args:
size (int): Input dimension.
self_attn (torch.nn.Module): Self-attention module instance.
`MultiHeadedAttention` or `RelPositionMultiHeadedAttention` instance
can be used as the argument.
feed_forward (torch.nn.Module): Feed-forward module instance.
`PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance
can be used as the argument.
feed_forward_macaron (torch.nn.Module): Additional feed-forward module instance.
`PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance
can be used as the argument.
conv_module (torch.nn.Module): Convolution module instance.
`ConvlutionModule` instance can be used as the argument.
dropout_rate (float): Dropout rate.
normalize_before (bool): Whether to use layer_norm before the first block.
concat_after (bool): Whether to concat attention layer's input and output.
if True, additional linear will be applied.
i.e. x -> x + linear(concat(x, att(x)))
if False, no additional linear will be applied. i.e. x -> x + att(x)
"""
def __init__(self, size, self_attn, feed_forward, feed_forward_macaron, conv_module, dropout_rate, normalize_before=True, concat_after=False, ):
super(EncoderLayer, self).__init__()
self.self_attn = self_attn
self.feed_forward = feed_forward
self.feed_forward_macaron = feed_forward_macaron
self.conv_module = conv_module
self.norm_ff = LayerNorm(size) # for the FNN module
self.norm_mha = LayerNorm(size) # for the MHA module
if feed_forward_macaron is not None:
self.norm_ff_macaron = LayerNorm(size)
self.ff_scale = 0.5
else:
self.ff_scale = 1.0
if self.conv_module is not None:
self.norm_conv = LayerNorm(size) # for the CNN module
self.norm_final = LayerNorm(size) # for the final output of the block
self.dropout = nn.Dropout(dropout_rate)
self.size = size
self.normalize_before = normalize_before
self.concat_after = concat_after
if self.concat_after:
self.concat_linear = nn.Linear(size + size, size)
def forward(self, x_input, mask, cache=None):
"""
Compute encoded features.
Args:
x_input (Union[Tuple, torch.Tensor]): Input tensor w/ or w/o pos emb.
- w/ pos emb: Tuple of tensors [(#batch, time, size), (1, time, size)].
- w/o pos emb: Tensor (#batch, time, size).
mask (torch.Tensor): Mask tensor for the input (#batch, time).
cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size).
Returns:
torch.Tensor: Output tensor (#batch, time, size).
torch.Tensor: Mask tensor (#batch, time).
"""
if isinstance(x_input, tuple):
x, pos_emb = x_input[0], x_input[1]
else:
x, pos_emb = x_input, None
# whether to use macaron style
if self.feed_forward_macaron is not None:
residual = x
if self.normalize_before:
x = self.norm_ff_macaron(x)
x = residual + self.ff_scale * self.dropout(self.feed_forward_macaron(x))
if not self.normalize_before:
x = self.norm_ff_macaron(x)
# multi-headed self-attention module
residual = x
if self.normalize_before:
x = self.norm_mha(x)
if cache is None:
x_q = x
else:
assert cache.shape == (x.shape[0], x.shape[1] - 1, self.size)
x_q = x[:, -1:, :]
residual = residual[:, -1:, :]
mask = None if mask is None else mask[:, -1:, :]
if pos_emb is not None:
x_att = self.self_attn(x_q, x, x, pos_emb, mask)
else:
x_att = self.self_attn(x_q, x, x, mask)
if self.concat_after:
x_concat = torch.cat((x, x_att), dim=-1)
x = residual + self.concat_linear(x_concat)
else:
x = residual + self.dropout(x_att)
if not self.normalize_before:
x = self.norm_mha(x)
# convolution module
if self.conv_module is not None:
residual = x
if self.normalize_before:
x = self.norm_conv(x)
x = residual + self.dropout(self.conv_module(x))
if not self.normalize_before:
x = self.norm_conv(x)
# feed forward module
residual = x
if self.normalize_before:
x = self.norm_ff(x)
x = residual + self.ff_scale * self.dropout(self.feed_forward(x))
if not self.normalize_before:
x = self.norm_ff(x)
if self.conv_module is not None:
x = self.norm_final(x)
if cache is not None:
x = torch.cat([cache, x], dim=1)
if pos_emb is not None:
return (x, pos_emb), mask
return x, mask
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