Source code for espnet.nets.pytorch_backend.conformer.contextual_block_encoder_layer

# -*- coding: utf-8 -*-
"""
Created on Sat Aug 21 16:57:31 2021.

@author: Keqi Deng (UCAS)
"""

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. 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. 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, feed_forward_macaron, conv_module, 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.feed_forward_macaron = feed_forward_macaron self.conv_module = conv_module self.norm1 = LayerNorm(size) self.norm2 = LayerNorm(size) 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.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, 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, 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)) # 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) 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) # 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) residual = x if self.normalize_before: x = self.norm2(x) x = residual + self.ff_scale * self.dropout(self.feed_forward(x)) if not self.normalize_before: x = self.norm2(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) 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)) # 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) 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) # 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) residual = x if self.normalize_before: x = self.norm2(x) x = residual + self.ff_scale * self.dropout(self.feed_forward(x)) if not self.normalize_before: x = self.norm2(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) # 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