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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2020 Johns Hopkins University (Shinji Watanabe)
# Northwestern Polytechnical University (Pengcheng Guo)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Encoder self-attention layer definition."""
import torch
from torch import nn
from funasr_detach.models.transformer.layer_norm import LayerNorm
from torch.autograd import Variable
class Encoder_Conformer_Layer(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,
cca_pos=0,
):
"""Construct an Encoder_Conformer_Layer object."""
super(Encoder_Conformer_Layer, 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
self.cca_pos = cca_pos
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 self.cca_pos < 2:
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)
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
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_cros_channel,
self_attn_conformer,
feed_forward_csa,
feed_forward_macaron_csa,
conv_module_csa,
dropout_rate,
normalize_before=True,
concat_after=False,
):
"""Construct an EncoderLayer object."""
super(EncoderLayer, self).__init__()
self.encoder_cros_channel_atten = self_attn_cros_channel
self.encoder_csa = Encoder_Conformer_Layer(
size,
self_attn_conformer,
feed_forward_csa,
feed_forward_macaron_csa,
conv_module_csa,
dropout_rate,
normalize_before,
concat_after,
cca_pos=0,
)
self.norm_mha = LayerNorm(size) # for the MHA module
self.dropout = nn.Dropout(dropout_rate)
def forward(self, x_input, mask, channel_size, 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
residual = x
x = self.norm_mha(x)
t_leng = x.size(1)
d_dim = x.size(2)
x_new = x.reshape(-1, channel_size, t_leng, d_dim).transpose(
1, 2
) # x_new B*T * C * D
x_k_v = x_new.new(x_new.size(0), x_new.size(1), 5, x_new.size(2), x_new.size(3))
pad_before = Variable(
torch.zeros(x_new.size(0), 2, x_new.size(2), x_new.size(3))
).type(x_new.type())
pad_after = Variable(
torch.zeros(x_new.size(0), 2, x_new.size(2), x_new.size(3))
).type(x_new.type())
x_pad = torch.cat([pad_before, x_new, pad_after], 1)
x_k_v[:, :, 0, :, :] = x_pad[:, 0:-4, :, :]
x_k_v[:, :, 1, :, :] = x_pad[:, 1:-3, :, :]
x_k_v[:, :, 2, :, :] = x_pad[:, 2:-2, :, :]
x_k_v[:, :, 3, :, :] = x_pad[:, 3:-1, :, :]
x_k_v[:, :, 4, :, :] = x_pad[:, 4:, :, :]
x_new = x_new.reshape(-1, channel_size, d_dim)
x_k_v = x_k_v.reshape(-1, 5 * channel_size, d_dim)
x_att = self.encoder_cros_channel_atten(x_new, x_k_v, x_k_v, None)
x_att = (
x_att.reshape(-1, t_leng, channel_size, d_dim)
.transpose(1, 2)
.reshape(-1, t_leng, d_dim)
)
x = residual + self.dropout(x_att)
if pos_emb is not None:
x_input = (x, pos_emb)
else:
x_input = x
x_input, mask = self.encoder_csa(x_input, mask)
return x_input, mask, channel_size
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