encoder_layer.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

# Copyright 2020 Johns Hopkins University (Shinji Watanabe)
#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)

"""Encoder self-attention layer definition."""

import torch

from torch import nn

from .layer_norm import LayerNorm


class EncoderLayer(nn.Module):
    """Encoder layer module.

    :param int size: input dim
    :param espnet.nets.pytorch_backend.transformer.attention.
        MultiHeadedAttention self_attn: self attention module
        RelPositionMultiHeadedAttention self_attn: self attention module
    :param espnet.nets.pytorch_backend.transformer.positionwise_feed_forward.
        PositionwiseFeedForward feed_forward:
        feed forward module
    :param espnet.nets.pytorch_backend.transformer.positionwise_feed_forward
    for macaron style
    PositionwiseFeedForward feed_forward:
    feed forward module
    :param espnet.nets.pytorch_backend.conformer.convolution.
        ConvolutionModule feed_foreard:
        feed forward module
    :param float dropout_rate: dropout rate
    :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)

    """

    def __init__(
        self,
        size,
        self_attn,
        feed_forward,
        feed_forward_macaron,
        conv_module,
        dropout_rate,
        normalize_before=True,
        concat_after=False,
    ):
        """Construct an EncoderLayer object."""
        super(EncoderLayer, 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.norm_ff = LayerNorm(size)  # for the FNN module
        self.norm_mha = LayerNorm(size)  # for the MHA module
        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
        if self.concat_after:
            self.concat_linear = nn.Linear(size + size, size)

    def forward(self, x_input, mask, cache=None):
        """Compute encoded features.

        :param torch.Tensor x_input: encoded source features, w/o pos_emb
        tuple((batch, max_time_in, size), (1, max_time_in, size))
        or (batch, max_time_in, size)
        :param torch.Tensor mask: mask for x (batch, max_time_in)
        :param torch.Tensor cache: cache for x (batch, max_time_in - 1, size)
        :rtype: Tuple[torch.Tensor, torch.Tensor]
        """
        if isinstance(x_input, tuple):
            x, pos_emb = x_input[0], x_input[1]
        else:
            x, pos_emb = x_input, None

        # 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)

        # multi-headed self-attention module
        residual = x
        if self.normalize_before:
            x = self.norm_mha(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 pos_emb is not None:
            x_att = self.self_attn(x_q, x, x, pos_emb, mask)
        else:
            x_att = self.self_attn(x_q, x, x, mask)

        if self.concat_after:
            x_concat = torch.cat((x, x_att), dim=-1)
            x = residual + self.concat_linear(x_concat)
        else:
            x = residual + self.dropout(x_att)
        if not self.normalize_before:
            x = self.norm_mha(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)

        # feed forward module
        residual = x
        if self.normalize_before:
            x = self.norm_ff(x)
        x = residual + self.ff_scale * self.dropout(self.feed_forward(x))
        if not self.normalize_before:
            x = self.norm_ff(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)

        if pos_emb is not None:
            return (x, pos_emb), mask

        return x, mask