#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2020 Emiru Tsunoo
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Encoder self-attention layer definition."""
import torch
from torch import nn
from espnet.nets.pytorch_backend.transformer.layer_norm import LayerNorm
[docs]class ContextualBlockEncoderLayer(nn.Module):
"""Contexutal Block 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.
dropout_rate (float): Dropout rate.
total_layer_num (int): Total number of layers
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,
dropout_rate,
total_layer_num,
normalize_before=True,
concat_after=False,
):
"""Construct an EncoderLayer object."""
super(ContextualBlockEncoderLayer, self).__init__()
self.self_attn = self_attn
self.feed_forward = feed_forward
self.norm1 = LayerNorm(size)
self.norm2 = LayerNorm(size)
self.dropout = nn.Dropout(dropout_rate)
self.size = size
self.normalize_before = normalize_before
self.concat_after = concat_after
self.total_layer_num = total_layer_num
if self.concat_after:
self.concat_linear = nn.Linear(size + size, size)
[docs] def forward(
self,
x,
mask,
infer_mode=False,
past_ctx=None,
next_ctx=None,
is_short_segment=False,
layer_idx=0,
cache=None,
):
"""Calculate forward propagation."""
if self.training or not infer_mode:
return self.forward_train(x, mask, past_ctx, next_ctx, layer_idx, cache)
else:
return self.forward_infer(
x, mask, past_ctx, next_ctx, is_short_segment, layer_idx, cache
)
[docs] def forward_train(
self, x, mask, past_ctx=None, next_ctx=None, layer_idx=0, cache=None
):
"""Compute encoded features.
Args:
x_input (torch.Tensor): Input tensor (#batch, time, size).
mask (torch.Tensor): Mask tensor for the input (#batch, 1, time).
past_ctx (torch.Tensor): Previous contexutal vector
next_ctx (torch.Tensor): Next contexutal vector
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, 1, time).
cur_ctx (torch.Tensor): Current contexutal vector
next_ctx (torch.Tensor): Next contexutal vector
layer_idx (int): layer index number
"""
nbatch = x.size(0)
nblock = x.size(1)
if past_ctx is not None:
if next_ctx is None:
# store all context vectors in one tensor
next_ctx = past_ctx.new_zeros(
nbatch, nblock, self.total_layer_num, x.size(-1)
)
else:
x[:, :, 0] = past_ctx[:, :, layer_idx]
# reshape ( nbatch, nblock, block_size + 2, dim )
# -> ( nbatch * nblock, block_size + 2, dim )
x = x.view(-1, x.size(-2), x.size(-1))
if mask is not None:
mask = mask.view(-1, mask.size(-2), mask.size(-1))
residual = x
if self.normalize_before:
x = self.norm1(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.concat_after:
x_concat = torch.cat((x, self.self_attn(x_q, x, x, mask)), dim=-1)
x = residual + self.concat_linear(x_concat)
else:
x = residual + self.dropout(self.self_attn(x_q, x, x, mask))
if not self.normalize_before:
x = self.norm1(x)
residual = x
if self.normalize_before:
x = self.norm2(x)
x = residual + self.dropout(self.feed_forward(x))
if not self.normalize_before:
x = self.norm2(x)
if cache is not None:
x = torch.cat([cache, x], dim=1)
layer_idx += 1
# reshape ( nbatch * nblock, block_size + 2, dim )
# -> ( nbatch, nblock, block_size + 2, dim )
x = x.view(nbatch, -1, x.size(-2), x.size(-1)).squeeze(1)
if mask is not None:
mask = mask.view(nbatch, -1, mask.size(-2), mask.size(-1)).squeeze(1)
if next_ctx is not None and layer_idx < self.total_layer_num:
next_ctx[:, 0, layer_idx, :] = x[:, 0, -1, :]
next_ctx[:, 1:, layer_idx, :] = x[:, 0:-1, -1, :]
return x, mask, False, next_ctx, next_ctx, False, layer_idx
[docs] def forward_infer(
self,
x,
mask,
past_ctx=None,
next_ctx=None,
is_short_segment=False,
layer_idx=0,
cache=None,
):
"""Compute encoded features.
Args:
x_input (torch.Tensor): Input tensor (#batch, time, size).
mask (torch.Tensor): Mask tensor for the input (#batch, 1, time).
past_ctx (torch.Tensor): Previous contexutal vector
next_ctx (torch.Tensor): Next contexutal vector
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, 1, time).
cur_ctx (torch.Tensor): Current contexutal vector
next_ctx (torch.Tensor): Next contexutal vector
layer_idx (int): layer index number
"""
nbatch = x.size(0)
nblock = x.size(1)
# if layer_idx == 0, next_ctx has to be None
if layer_idx == 0:
assert next_ctx is None
next_ctx = x.new_zeros(nbatch, self.total_layer_num, x.size(-1))
# reshape ( nbatch, nblock, block_size + 2, dim )
# -> ( nbatch * nblock, block_size + 2, dim )
x = x.view(-1, x.size(-2), x.size(-1))
if mask is not None:
mask = mask.view(-1, mask.size(-2), mask.size(-1))
residual = x
if self.normalize_before:
x = self.norm1(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.concat_after:
x_concat = torch.cat((x, self.self_attn(x_q, x, x, mask)), dim=-1)
x = residual + self.concat_linear(x_concat)
else:
x = residual + self.dropout(self.self_attn(x_q, x, x, mask))
if not self.normalize_before:
x = self.norm1(x)
residual = x
if self.normalize_before:
x = self.norm2(x)
x = residual + self.dropout(self.feed_forward(x))
if not self.normalize_before:
x = self.norm2(x)
if cache is not None:
x = torch.cat([cache, x], dim=1)
# reshape ( nbatch * nblock, block_size + 2, dim )
# -> ( nbatch, nblock, block_size + 2, dim )
x = x.view(nbatch, nblock, x.size(-2), x.size(-1))
if mask is not None:
mask = mask.view(nbatch, nblock, mask.size(-2), mask.size(-1))
# Propagete context information (the last frame of each block)
# to the first frame
# of the next block
if not is_short_segment:
if past_ctx is None:
# First block of an utterance
x[:, 0, 0, :] = x[:, 0, -1, :]
else:
x[:, 0, 0, :] = past_ctx[:, layer_idx, :]
if nblock > 1:
x[:, 1:, 0, :] = x[:, 0:-1, -1, :]
next_ctx[:, layer_idx, :] = x[:, -1, -1, :]
else:
next_ctx = None
return x, mask, True, past_ctx, next_ctx, is_short_segment, layer_idx + 1