153 lines
5.2 KiB
Python
153 lines
5.2 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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# Copyright 2020 Johns Hopkins University (Shinji Watanabe)
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# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
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"""Encoder self-attention layer definition."""
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import torch
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from torch import nn
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from .layer_norm import LayerNorm
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class EncoderLayer(nn.Module):
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"""Encoder layer module.
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:param int size: input dim
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:param espnet.nets.pytorch_backend.transformer.attention.
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MultiHeadedAttention self_attn: self attention module
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RelPositionMultiHeadedAttention self_attn: self attention module
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:param espnet.nets.pytorch_backend.transformer.positionwise_feed_forward.
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PositionwiseFeedForward feed_forward:
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feed forward module
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:param espnet.nets.pytorch_backend.transformer.positionwise_feed_forward
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for macaron style
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PositionwiseFeedForward feed_forward:
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feed forward module
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:param espnet.nets.pytorch_backend.conformer.convolution.
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ConvolutionModule feed_foreard:
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feed forward module
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:param float dropout_rate: dropout rate
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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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"""
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def __init__(
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self,
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size,
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self_attn,
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feed_forward,
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feed_forward_macaron,
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conv_module,
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dropout_rate,
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normalize_before=True,
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concat_after=False,
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):
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"""Construct an EncoderLayer object."""
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super(EncoderLayer, self).__init__()
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self.self_attn = self_attn
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self.feed_forward = feed_forward
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self.feed_forward_macaron = feed_forward_macaron
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self.conv_module = conv_module
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self.norm_ff = LayerNorm(size) # for the FNN module
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self.norm_mha = LayerNorm(size) # for the MHA module
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if feed_forward_macaron is not None:
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self.norm_ff_macaron = LayerNorm(size)
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self.ff_scale = 0.5
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else:
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self.ff_scale = 1.0
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if self.conv_module is not None:
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self.norm_conv = LayerNorm(size) # for the CNN module
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self.norm_final = LayerNorm(size) # for the final output of the block
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self.dropout = nn.Dropout(dropout_rate)
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self.size = size
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self.normalize_before = normalize_before
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self.concat_after = concat_after
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if self.concat_after:
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self.concat_linear = nn.Linear(size + size, size)
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def forward(self, x_input, mask, cache=None):
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"""Compute encoded features.
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:param torch.Tensor x_input: encoded source features, w/o pos_emb
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tuple((batch, max_time_in, size), (1, max_time_in, size))
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or (batch, max_time_in, size)
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:param torch.Tensor mask: mask for x (batch, max_time_in)
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:param torch.Tensor cache: cache for x (batch, max_time_in - 1, size)
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:rtype: Tuple[torch.Tensor, torch.Tensor]
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"""
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if isinstance(x_input, tuple):
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x, pos_emb = x_input[0], x_input[1]
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else:
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x, pos_emb = x_input, None
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# whether to use macaron style
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if self.feed_forward_macaron is not None:
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residual = x
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if self.normalize_before:
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x = self.norm_ff_macaron(x)
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x = residual + self.ff_scale * self.dropout(self.feed_forward_macaron(x))
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if not self.normalize_before:
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x = self.norm_ff_macaron(x)
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# multi-headed self-attention module
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residual = x
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if self.normalize_before:
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x = self.norm_mha(x)
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if cache is None:
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x_q = x
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else:
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assert cache.shape == (x.shape[0], x.shape[1] - 1, self.size)
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x_q = x[:, -1:, :]
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residual = residual[:, -1:, :]
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mask = None if mask is None else mask[:, -1:, :]
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if pos_emb is not None:
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x_att = self.self_attn(x_q, x, x, pos_emb, mask)
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else:
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x_att = self.self_attn(x_q, x, x, mask)
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if self.concat_after:
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x_concat = torch.cat((x, x_att), dim=-1)
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x = residual + self.concat_linear(x_concat)
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else:
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x = residual + self.dropout(x_att)
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if not self.normalize_before:
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x = self.norm_mha(x)
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# convolution module
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if self.conv_module is not None:
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residual = x
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if self.normalize_before:
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x = self.norm_conv(x)
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x = residual + self.dropout(self.conv_module(x))
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if not self.normalize_before:
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x = self.norm_conv(x)
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# feed forward module
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residual = x
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if self.normalize_before:
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x = self.norm_ff(x)
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x = residual + self.ff_scale * self.dropout(self.feed_forward(x))
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if not self.normalize_before:
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x = self.norm_ff(x)
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if self.conv_module is not None:
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x = self.norm_final(x)
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if cache is not None:
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x = torch.cat([cache, x], dim=1)
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if pos_emb is not None:
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return (x, pos_emb), mask
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return x, mask
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