269 lines
10 KiB
Python
269 lines
10 KiB
Python
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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# Copyright 2019 Mobvoi Inc. All Rights Reserved.
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# Author: di.wu@mobvoi.com (DI WU)
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"""Encoder self-attention layer definition."""
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from typing import Optional, Tuple
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import torch
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from torch import nn
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class TransformerEncoderLayer(nn.Module):
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"""Encoder layer module.
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Args:
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size (int): Input dimension.
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self_attn (torch.nn.Module): Self-attention module instance.
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`MultiHeadedAttention` or `RelPositionMultiHeadedAttention`
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instance can be used as the argument.
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feed_forward (torch.nn.Module): Feed-forward module instance.
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`PositionwiseFeedForward`, instance can be used as the argument.
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dropout_rate (float): Dropout rate.
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normalize_before (bool):
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True: use layer_norm before each sub-block.
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False: to use layer_norm after each sub-block.
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concat_after (bool): Whether to concat attention layer's input and
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output.
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True: x -> x + linear(concat(x, att(x)))
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False: x -> x + att(x)
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"""
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def __init__(
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self,
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size: int,
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self_attn: torch.nn.Module,
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feed_forward: torch.nn.Module,
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dropout_rate: float,
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normalize_before: bool = True,
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concat_after: bool = False,
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):
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"""Construct an EncoderLayer object."""
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super().__init__()
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self.self_attn = self_attn
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self.feed_forward = feed_forward
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self.norm1 = nn.LayerNorm(size, eps=1e-12)
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self.norm2 = nn.LayerNorm(size, eps=1e-12)
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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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# concat_linear may be not used in forward fuction,
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# but will be saved in the *.pt
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self.concat_linear = nn.Linear(size + size, size)
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def forward(
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self,
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x: torch.Tensor,
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mask: torch.Tensor,
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pos_emb: torch.Tensor,
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mask_pad: Optional[torch.Tensor] = None,
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output_cache: Optional[torch.Tensor] = None,
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cnn_cache: Optional[torch.Tensor] = None,
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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"""Compute encoded features.
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Args:
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x (torch.Tensor): Input tensor (#batch, time, size).
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mask (torch.Tensor): Mask tensor for the input (#batch, time).
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pos_emb (torch.Tensor): just for interface compatibility
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to ConformerEncoderLayer
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mask_pad (torch.Tensor): does not used in transformer layer,
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just for unified api with conformer.
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output_cache (torch.Tensor): Cache tensor of the output
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(#batch, time2, size), time2 < time in x.
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cnn_cache (torch.Tensor): not used here, it's for interface
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compatibility to ConformerEncoderLayer
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Returns:
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torch.Tensor: Output tensor (#batch, time, size).
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torch.Tensor: Mask tensor (#batch, time).
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"""
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residual = x
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if self.normalize_before:
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x = self.norm1(x)
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if output_cache is None:
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x_q = x
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else:
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assert output_cache.size(0) == x.size(0)
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assert output_cache.size(2) == self.size
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assert output_cache.size(1) < x.size(1)
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chunk = x.size(1) - output_cache.size(1)
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x_q = x[:, -chunk:, :]
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residual = residual[:, -chunk:, :]
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mask = mask[:, -chunk:, :]
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if self.concat_after:
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x_concat = torch.cat((x, self.self_attn(x_q, x, x, mask)), 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(self.self_attn(x_q, x, x, mask))
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if not self.normalize_before:
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x = self.norm1(x)
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residual = x
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if self.normalize_before:
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x = self.norm2(x)
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x = residual + self.dropout(self.feed_forward(x))
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if not self.normalize_before:
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x = self.norm2(x)
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if output_cache is not None:
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x = torch.cat([output_cache, x], dim=1)
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fake_cnn_cache = torch.tensor([0.0], dtype=x.dtype, device=x.device)
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return x, mask, fake_cnn_cache
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class ConformerEncoderLayer(nn.Module):
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"""Encoder layer module.
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Args:
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size (int): Input dimension.
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self_attn (torch.nn.Module): Self-attention module instance.
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`MultiHeadedAttention` or `RelPositionMultiHeadedAttention`
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instance can be used as the argument.
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feed_forward (torch.nn.Module): Feed-forward module instance.
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`PositionwiseFeedForward` instance can be used as the argument.
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feed_forward_macaron (torch.nn.Module): Additional feed-forward module
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instance.
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`PositionwiseFeedForward` instance can be used as the argument.
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conv_module (torch.nn.Module): Convolution module instance.
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`ConvlutionModule` instance can be used as the argument.
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dropout_rate (float): Dropout rate.
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normalize_before (bool):
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True: use layer_norm before each sub-block.
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False: use layer_norm after each sub-block.
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concat_after (bool): Whether to concat attention layer's input and
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output.
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True: x -> x + linear(concat(x, att(x)))
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False: x -> x + att(x)
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"""
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def __init__(
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self,
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size: int,
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self_attn: torch.nn.Module,
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feed_forward: Optional[nn.Module] = None,
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feed_forward_macaron: Optional[nn.Module] = None,
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conv_module: Optional[nn.Module] = None,
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dropout_rate: float = 0.1,
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normalize_before: bool = True,
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concat_after: bool = False,
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):
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"""Construct an EncoderLayer object."""
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super().__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 = nn.LayerNorm(size, eps=1e-12) # for the FNN module
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self.norm_mha = nn.LayerNorm(size, eps=1e-12) # for the MHA module
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if feed_forward_macaron is not None:
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self.norm_ff_macaron = nn.LayerNorm(size, eps=1e-12)
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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 = nn.LayerNorm(size,
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eps=1e-12) # for the CNN module
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self.norm_final = nn.LayerNorm(
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size, eps=1e-12) # 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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self.concat_linear = nn.Linear(size + size, size)
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def forward(
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self,
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x: torch.Tensor,
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mask: torch.Tensor,
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pos_emb: torch.Tensor,
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mask_pad: Optional[torch.Tensor] = None,
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output_cache: Optional[torch.Tensor] = None,
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cnn_cache: Optional[torch.Tensor] = None,
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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"""Compute encoded features.
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Args:
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x (torch.Tensor): (#batch, time, size)
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mask (torch.Tensor): Mask tensor for the input (#batch, time，time).
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pos_emb (torch.Tensor): positional encoding, must not be None
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for ConformerEncoderLayer.
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mask_pad (torch.Tensor): batch padding mask used for conv module.
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(#batch, 1，time)
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output_cache (torch.Tensor): Cache tensor of the output
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(#batch, time2, size), time2 < time in x.
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cnn_cache (torch.Tensor): Convolution cache in conformer layer
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Returns:
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torch.Tensor: Output tensor (#batch, time, size).
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torch.Tensor: Mask tensor (#batch, time).
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"""
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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(
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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 output_cache is None:
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x_q = x
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else:
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assert output_cache.size(0) == x.size(0)
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assert output_cache.size(2) == self.size
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assert output_cache.size(1) < x.size(1)
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chunk = x.size(1) - output_cache.size(1)
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x_q = x[:, -chunk:, :]
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residual = residual[:, -chunk:, :]
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mask = mask[:, -chunk:, :]
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x_att = self.self_attn(x_q, x, x, mask, pos_emb)
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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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# Fake new cnn cache here, and then change it in conv_module
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new_cnn_cache = torch.tensor([0.0], dtype=x.dtype, device=x.device)
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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, new_cnn_cache = self.conv_module(x, mask_pad, cnn_cache)
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x = residual + self.dropout(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 output_cache is not None:
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x = torch.cat([output_cache, x], dim=1)
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return x, mask, new_cnn_cache
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