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| # -*- coding: utf-8 -*- | |
| """ | |
| Created on Sat Aug 21 16:57:31 2021. | |
| @author: Keqi Deng (UCAS) | |
| """ | |
| from espnet.nets.pytorch_backend.transformer.layer_norm import LayerNorm | |
| import torch | |
| from torch import nn | |
| 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) | |
| 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 | |
| ) | |
| 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, layer_idx | |
| 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, 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 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 | |