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| #!/usr/bin/env python3 | |
| # -*- coding: utf-8 -*- | |
| # Copyright 2019 Shigeki Karita | |
| # Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) | |
| """Encoder definition.""" | |
| import logging | |
| import torch | |
| from espnet.nets.pytorch_backend.conformer.convolution import ConvolutionModule | |
| from espnet.nets.pytorch_backend.conformer.encoder_layer import EncoderLayer | |
| from espnet.nets.pytorch_backend.nets_utils import get_activation | |
| from espnet.nets.pytorch_backend.transducer.vgg import VGG2L | |
| from espnet.nets.pytorch_backend.transformer.attention import ( | |
| MultiHeadedAttention, # noqa: H301 | |
| RelPositionMultiHeadedAttention, # noqa: H301 | |
| ) | |
| from espnet.nets.pytorch_backend.transformer.embedding import ( | |
| PositionalEncoding, # noqa: H301 | |
| ScaledPositionalEncoding, # noqa: H301 | |
| RelPositionalEncoding, # noqa: H301 | |
| ) | |
| from espnet.nets.pytorch_backend.transformer.layer_norm import LayerNorm | |
| from espnet.nets.pytorch_backend.transformer.multi_layer_conv import Conv1dLinear | |
| from espnet.nets.pytorch_backend.transformer.multi_layer_conv import MultiLayeredConv1d | |
| from espnet.nets.pytorch_backend.transformer.positionwise_feed_forward import ( | |
| PositionwiseFeedForward, # noqa: H301 | |
| ) | |
| from espnet.nets.pytorch_backend.transformer.repeat import repeat | |
| from espnet.nets.pytorch_backend.transformer.subsampling import Conv2dSubsampling | |
| class Encoder(torch.nn.Module): | |
| """Conformer encoder module. | |
| :param int idim: input dim | |
| :param int attention_dim: dimention of attention | |
| :param int attention_heads: the number of heads of multi head attention | |
| :param int linear_units: the number of units of position-wise feed forward | |
| :param int num_blocks: the number of decoder blocks | |
| :param float dropout_rate: dropout rate | |
| :param float attention_dropout_rate: dropout rate in attention | |
| :param float positional_dropout_rate: dropout rate after adding positional encoding | |
| :param str or torch.nn.Module input_layer: input layer type | |
| :param bool normalize_before: whether to use layer_norm before the first block | |
| :param bool concat_after: 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) | |
| :param str positionwise_layer_type: linear of conv1d | |
| :param int positionwise_conv_kernel_size: kernel size of positionwise conv1d layer | |
| :param str encoder_pos_enc_layer_type: encoder positional encoding layer type | |
| :param str encoder_attn_layer_type: encoder attention layer type | |
| :param str activation_type: encoder activation function type | |
| :param bool macaron_style: whether to use macaron style for positionwise layer | |
| :param bool use_cnn_module: whether to use convolution module | |
| :param int cnn_module_kernel: kernerl size of convolution module | |
| :param int padding_idx: padding_idx for input_layer=embed | |
| """ | |
| def __init__( | |
| self, | |
| idim, | |
| attention_dim=256, | |
| attention_heads=4, | |
| linear_units=2048, | |
| num_blocks=6, | |
| dropout_rate=0.1, | |
| positional_dropout_rate=0.1, | |
| attention_dropout_rate=0.0, | |
| input_layer="conv2d", | |
| normalize_before=True, | |
| concat_after=False, | |
| positionwise_layer_type="linear", | |
| positionwise_conv_kernel_size=1, | |
| macaron_style=False, | |
| pos_enc_layer_type="abs_pos", | |
| selfattention_layer_type="selfattn", | |
| activation_type="swish", | |
| use_cnn_module=False, | |
| cnn_module_kernel=31, | |
| padding_idx=-1, | |
| ): | |
| """Construct an Encoder object.""" | |
| super(Encoder, self).__init__() | |
| activation = get_activation(activation_type) | |
| if pos_enc_layer_type == "abs_pos": | |
| pos_enc_class = PositionalEncoding | |
| elif pos_enc_layer_type == "scaled_abs_pos": | |
| pos_enc_class = ScaledPositionalEncoding | |
| elif pos_enc_layer_type == "rel_pos": | |
| assert selfattention_layer_type == "rel_selfattn" | |
| pos_enc_class = RelPositionalEncoding | |
| else: | |
| raise ValueError("unknown pos_enc_layer: " + pos_enc_layer_type) | |
| if input_layer == "linear": | |
| self.embed = torch.nn.Sequential( | |
| torch.nn.Linear(idim, attention_dim), | |
| torch.nn.LayerNorm(attention_dim), | |
| torch.nn.Dropout(dropout_rate), | |
| pos_enc_class(attention_dim, positional_dropout_rate), | |
| ) | |
| elif input_layer == "conv2d": | |
| self.embed = Conv2dSubsampling( | |
| idim, | |
| attention_dim, | |
| dropout_rate, | |
| pos_enc_class(attention_dim, positional_dropout_rate), | |
| ) | |
| elif input_layer == "vgg2l": | |
| self.embed = VGG2L(idim, attention_dim) | |
| elif input_layer == "embed": | |
| self.embed = torch.nn.Sequential( | |
| torch.nn.Embedding(idim, attention_dim, padding_idx=padding_idx), | |
| pos_enc_class(attention_dim, positional_dropout_rate), | |
| ) | |
| elif isinstance(input_layer, torch.nn.Module): | |
| self.embed = torch.nn.Sequential( | |
| input_layer, | |
| pos_enc_class(attention_dim, positional_dropout_rate), | |
| ) | |
| elif input_layer is None: | |
| self.embed = torch.nn.Sequential( | |
| pos_enc_class(attention_dim, positional_dropout_rate) | |
| ) | |
| else: | |
| raise ValueError("unknown input_layer: " + input_layer) | |
| self.normalize_before = normalize_before | |
| if positionwise_layer_type == "linear": | |
| positionwise_layer = PositionwiseFeedForward | |
| positionwise_layer_args = ( | |
| attention_dim, | |
| linear_units, | |
| dropout_rate, | |
| activation, | |
| ) | |
| elif positionwise_layer_type == "conv1d": | |
| positionwise_layer = MultiLayeredConv1d | |
| positionwise_layer_args = ( | |
| attention_dim, | |
| linear_units, | |
| positionwise_conv_kernel_size, | |
| dropout_rate, | |
| ) | |
| elif positionwise_layer_type == "conv1d-linear": | |
| positionwise_layer = Conv1dLinear | |
| positionwise_layer_args = ( | |
| attention_dim, | |
| linear_units, | |
| positionwise_conv_kernel_size, | |
| dropout_rate, | |
| ) | |
| else: | |
| raise NotImplementedError("Support only linear or conv1d.") | |
| if selfattention_layer_type == "selfattn": | |
| logging.info("encoder self-attention layer type = self-attention") | |
| encoder_selfattn_layer = MultiHeadedAttention | |
| encoder_selfattn_layer_args = ( | |
| attention_heads, | |
| attention_dim, | |
| attention_dropout_rate, | |
| ) | |
| elif selfattention_layer_type == "rel_selfattn": | |
| assert pos_enc_layer_type == "rel_pos" | |
| encoder_selfattn_layer = RelPositionMultiHeadedAttention | |
| encoder_selfattn_layer_args = ( | |
| attention_heads, | |
| attention_dim, | |
| attention_dropout_rate, | |
| ) | |
| else: | |
| raise ValueError("unknown encoder_attn_layer: " + selfattention_layer_type) | |
| convolution_layer = ConvolutionModule | |
| convolution_layer_args = (attention_dim, cnn_module_kernel, activation) | |
| self.encoders = repeat( | |
| num_blocks, | |
| lambda lnum: EncoderLayer( | |
| attention_dim, | |
| encoder_selfattn_layer(*encoder_selfattn_layer_args), | |
| positionwise_layer(*positionwise_layer_args), | |
| positionwise_layer(*positionwise_layer_args) if macaron_style else None, | |
| convolution_layer(*convolution_layer_args) if use_cnn_module else None, | |
| dropout_rate, | |
| normalize_before, | |
| concat_after, | |
| ), | |
| ) | |
| if self.normalize_before: | |
| self.after_norm = LayerNorm(attention_dim) | |
| def forward(self, xs, masks): | |
| """Encode input sequence. | |
| :param torch.Tensor xs: input tensor | |
| :param torch.Tensor masks: input mask | |
| :return: position embedded tensor and mask | |
| :rtype Tuple[torch.Tensor, torch.Tensor]: | |
| """ | |
| if isinstance(self.embed, (Conv2dSubsampling, VGG2L)): | |
| xs, masks = self.embed(xs, masks) | |
| else: | |
| xs = self.embed(xs) | |
| xs, masks = self.encoders(xs, masks) | |
| if isinstance(xs, tuple): | |
| xs = xs[0] | |
| if self.normalize_before: | |
| xs = self.after_norm(xs) | |
| return xs, masks | |