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