Source code for espnet.nets.chainer_backend.transformer.encoder

# encoding: utf-8
"""Class Declaration of Transformer's Encoder."""

import logging

import chainer
import numpy as np
from chainer import links as L

from espnet.nets.chainer_backend.transformer.embedding import PositionalEncoding
from espnet.nets.chainer_backend.transformer.encoder_layer import EncoderLayer
from espnet.nets.chainer_backend.transformer.layer_norm import LayerNorm
from espnet.nets.chainer_backend.transformer.mask import make_history_mask
from espnet.nets.chainer_backend.transformer.subsampling import (

[docs]class Encoder(chainer.Chain): """Encoder. Args: input_type(str): Sampling type. `input_type` must be `conv2d` or 'linear' currently. idim (int): Dimension of inputs. n_layers (int): Number of encoder layers. n_units (int): Number of input/output dimension of a FeedForward layer. d_units (int): Number of units of hidden layer in a FeedForward layer. h (int): Number of attention heads. dropout (float): Dropout rate """ 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", pos_enc_class=PositionalEncoding, initialW=None, initial_bias=None, ): """Initialize Encoder. Args: idim (int): Input dimension. args (Namespace): Training config. initialW (int, optional): Initializer to initialize the weight. initial_bias (bool, optional): Initializer to initialize the bias. """ super(Encoder, self).__init__() initialW = chainer.initializers.Uniform if initialW is None else initialW initial_bias = ( chainer.initializers.Uniform if initial_bias is None else initial_bias ) self.do_history_mask = False with self.init_scope(): self.conv_subsampling_factor = 1 channels = 64 # Based in paper if input_layer == "conv2d": idim = int(np.ceil(np.ceil(idim / 2) / 2)) * channels self.input_layer = Conv2dSubsampling( channels, idim, attention_dim, dropout=dropout_rate, initialW=initialW, initial_bias=initial_bias, ) self.conv_subsampling_factor = 4 elif input_layer == "linear": self.input_layer = LinearSampling( idim, attention_dim, initialW=initialW, initial_bias=initial_bias ) elif input_layer == "embed": self.input_layer = chainer.Sequential( L.EmbedID(idim, attention_dim, ignore_label=-1), pos_enc_class(attention_dim, positional_dropout_rate), ) self.do_history_mask = True else: raise ValueError("unknown input_layer: " + input_layer) self.norm = LayerNorm(attention_dim) for i in range(num_blocks): name = "encoders." + str(i) layer = EncoderLayer( attention_dim, d_units=linear_units, h=attention_heads, dropout=attention_dropout_rate, initialW=initialW, initial_bias=initial_bias, ) self.add_link(name, layer) self.n_layers = num_blocks
[docs] def forward(self, e, ilens): """Compute Encoder layer. Args: e (chainer.Variable): Batch of padded character. (B, Tmax) ilens (chainer.Variable): Batch of length of each input batch. (B,) Returns: chainer.Variable: Computed variable of encoder. numpy.array: Mask. chainer.Variable: Batch of lengths of each encoder outputs. """ if isinstance(self.input_layer, Conv2dSubsampling): e, ilens = self.input_layer(e, ilens) else: e = self.input_layer(e) batch, length, dims = e.shape x_mask = np.ones([batch, length]) for j in range(batch): x_mask[j, ilens[j] :] = -1 xx_mask = (x_mask[:, None, :] >= 0) * (x_mask[:, :, None] >= 0) xx_mask = self.xp.array(xx_mask) if self.do_history_mask: history_mask = make_history_mask(self.xp, x_mask) xx_mask *= history_mask logging.debug("encoders size: " + str(e.shape)) e = e.reshape(-1, dims) for i in range(self.n_layers): e = self["encoders." + str(i)](e, xx_mask, batch) return self.norm(e).reshape(batch, length, -1), x_mask, ilens