237 lines
9.4 KiB
Python
237 lines
9.4 KiB
Python
# Copyright (c) 2021 Mobvoi Inc (Binbin Zhang, Di Wu)
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# 2022 Xingchen Song (sxc19@mails.tsinghua.edu.cn)
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Modified from ESPnet(https://github.com/espnet/espnet)
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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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"""
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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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):
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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-5)
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self.norm2 = nn.LayerNorm(size, eps=1e-5)
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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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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: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
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att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)),
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cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)),
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) -> Tuple[torch.Tensor, 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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(0, 0, 0) means fake mask.
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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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att_cache (torch.Tensor): Cache tensor of the KEY & VALUE
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(#batch=1, head, cache_t1, d_k * 2), head * d_k == size.
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cnn_cache (torch.Tensor): Convolution cache in conformer layer
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(#batch=1, size, cache_t2), 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, time).
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torch.Tensor: att_cache tensor,
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(#batch=1, head, cache_t1 + time, d_k * 2).
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torch.Tensor: cnn_cahce tensor (#batch=1, size, cache_t2).
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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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x_att, new_att_cache = self.self_attn(x, x, x, mask, pos_emb=pos_emb, cache=att_cache)
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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.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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fake_cnn_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device)
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return x, mask, new_att_cache, 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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"""
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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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):
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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-5) # for the FNN module
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self.norm_mha = nn.LayerNorm(size, eps=1e-5) # 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-5)
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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, eps=1e-5) # for the CNN module
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self.norm_final = nn.LayerNorm(
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size, eps=1e-5) # 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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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: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
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att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)),
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cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)),
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) -> Tuple[torch.Tensor, 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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(0, 0, 0) means fake mask.
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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), (0, 0, 0) means fake mask.
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att_cache (torch.Tensor): Cache tensor of the KEY & VALUE
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(#batch=1, head, cache_t1, d_k * 2), head * d_k == size.
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cnn_cache (torch.Tensor): Convolution cache in conformer layer
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(#batch=1, size, cache_t2)
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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, time).
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torch.Tensor: att_cache tensor,
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(#batch=1, head, cache_t1 + time, d_k * 2).
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torch.Tensor: cnn_cahce tensor (#batch, size, cache_t2).
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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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x_att, new_att_cache = self.self_attn(x, x, x, mask, pos_emb,
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att_cache)
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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.zeros((0, 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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return x, mask, new_att_cache, new_cnn_cache
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