Source code for paddlespeech.s2t.modules.encoder_layer
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2019 Mobvoi Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Modified from wenet(https://github.com/wenet-e2e/wenet)
"""Encoder self-attention layer definition."""
from typing import Optional
from typing import Tuple
import paddle
from paddle import nn
from paddlespeech.s2t.modules.align import LayerNorm
from paddlespeech.s2t.modules.align import Linear
from paddlespeech.s2t.utils.log import Log
logger = Log(__name__).getlog()
__all__ = [
"TransformerEncoderLayer", "ConformerEncoderLayer",
"SqueezeformerEncoderLayer"
]
[docs]class TransformerEncoderLayer(nn.Layer):
"""Encoder layer module."""
def __init__(
self,
size: int,
self_attn: nn.Layer,
feed_forward: nn.Layer,
dropout_rate: float,
normalize_before: bool=True,
concat_after: bool=False, ):
"""Construct an EncoderLayer object.
Args:
size (int): Input dimension.
self_attn (nn.Layer): Self-attention module instance.
`MultiHeadedAttention`, `RelPositionMultiHeadedAttention` or `RoPERelPositionMultiHeadedAttention`
instance can be used as the argument.
feed_forward (nn.Layer): Feed-forward module instance.
`PositionwiseFeedForward`, instance can be used as the argument.
dropout_rate (float): Dropout rate.
normalize_before (bool):
True: use layer_norm before each sub-block.
False: to use layer_norm after each sub-block.
concat_after (bool): Whether to concat attention layer's input and
output.
True: x -> x + linear(concat(x, att(x)))
False: x -> x + att(x)
"""
super().__init__()
self.self_attn = self_attn
self.feed_forward = feed_forward
self.norm1 = LayerNorm(size, epsilon=1e-12)
self.norm2 = LayerNorm(size, epsilon=1e-12)
self.dropout = nn.Dropout(dropout_rate)
self.size = size
self.normalize_before = normalize_before
self.concat_after = concat_after
# concat_linear may be not used in forward fuction,
# but will be saved in the *.pt
self.concat_linear = Linear(size + size, size)
[docs] def forward(
self,
x: paddle.Tensor,
mask: paddle.Tensor,
pos_emb: paddle.Tensor,
mask_pad: paddle.Tensor=paddle.ones([0, 0, 0], dtype=paddle.bool),
att_cache: paddle.Tensor=paddle.zeros([0, 0, 0, 0]),
cnn_cache: paddle.Tensor=paddle.zeros([0, 0, 0, 0])
) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor, paddle.Tensor]:
"""Compute encoded features.
Args:
x (paddle.Tensor): (#batch, time, size)
mask (paddle.Tensor): Mask tensor for the input (#batch, time，time),
(0, 0, 0) means fake mask.
pos_emb (paddle.Tensor): just for interface compatibility
to ConformerEncoderLayer
mask_pad (paddle.Tensor): does not used in transformer layer,
just for unified api with conformer.
att_cache (paddle.Tensor): Cache tensor of the KEY & VALUE
(#batch=1, head, cache_t1, d_k * 2), head * d_k == size.
cnn_cache (paddle.Tensor): Convolution cache in conformer layer
(#batch=1, size, cache_t2), not used here, it's for interface
compatibility to ConformerEncoderLayer.
Returns:
paddle.Tensor: Output tensor (#batch, time, size).
paddle.Tensor: Mask tensor (#batch, time, time).
paddle.Tensor: att_cache tensor,
(#batch=1, head, cache_t1 + time, d_k * 2).
paddle.Tensor: cnn_cahce tensor (#batch=1, size, cache_t2).
"""
residual = x
if self.normalize_before:
x = self.norm1(x)
x_att, new_att_cache = self.self_attn(x, x, x, mask, cache=att_cache)
if self.concat_after:
x_concat = paddle.concat((x, x_att), axis=-1)
x = residual + self.concat_linear(x_concat)
else:
x = residual + self.dropout(x_att)
if not self.normalize_before:
x = self.norm1(x)
residual = x
if self.normalize_before:
x = self.norm2(x)
x = residual + self.dropout(self.feed_forward(x))
if not self.normalize_before:
x = self.norm2(x)
fake_cnn_cache = paddle.zeros([0, 0, 0], dtype=x.dtype)
return x, mask, new_att_cache, fake_cnn_cache
[docs]class ConformerEncoderLayer(nn.Layer):
"""Encoder layer module."""
def __init__(
self,
size: int,
self_attn: nn.Layer,
feed_forward: Optional[nn.Layer]=None,
feed_forward_macaron: Optional[nn.Layer]=None,
conv_module: Optional[nn.Layer]=None,
dropout_rate: float=0.1,
normalize_before: bool=True,
concat_after: bool=False, ):
"""Construct an EncoderLayer object.
Args:
size (int): Input dimension.
self_attn (nn.Layer): Self-attention module instance.
`MultiHeadedAttention`, `RelPositionMultiHeadedAttention` or `RoPERelPositionMultiHeadedAttention`
instance can be used as the argument.
feed_forward (nn.Layer): Feed-forward module instance.
`PositionwiseFeedForward` instance can be used as the argument.
feed_forward_macaron (nn.Layer): Additional feed-forward module
instance.
`PositionwiseFeedForward` instance can be used as the argument.
conv_module (nn.Layer): Convolution module instance.
`ConvlutionModule` instance can be used as the argument.
dropout_rate (float): Dropout rate.
normalize_before (bool):
True: use layer_norm before each sub-block.
False: use layer_norm after each sub-block.
concat_after (bool): Whether to concat attention layer's input and
output.
True: x -> x + linear(concat(x, att(x)))
False: x -> x + att(x)
"""
super().__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, epsilon=1e-12) # for the FNN module
self.norm_mha = LayerNorm(size, epsilon=1e-12) # for the MHA module
if feed_forward_macaron is not None:
self.norm_ff_macaron = LayerNorm(size, epsilon=1e-12)
self.ff_scale = 0.5
else:
self.ff_scale = 1.0
if self.conv_module is not None:
self.norm_conv = LayerNorm(
size, epsilon=1e-12) # for the CNN module
self.norm_final = LayerNorm(
size, epsilon=1e-12) # 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 = Linear(size + size, size)
else:
self.concat_linear = nn.Identity()
[docs] def forward(
self,
x: paddle.Tensor,
mask: paddle.Tensor,
pos_emb: paddle.Tensor,
mask_pad: paddle.Tensor=paddle.ones([0, 0, 0], dtype=paddle.bool),
att_cache: paddle.Tensor=paddle.zeros([0, 0, 0, 0]),
cnn_cache: paddle.Tensor=paddle.zeros([0, 0, 0, 0])
) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor, paddle.Tensor]:
"""Compute encoded features.
Args:
x (paddle.Tensor): Input tensor (#batch, time, size).
mask (paddle.Tensor): Mask tensor for the input (#batch, time, time).
(0,0,0) means fake mask.
pos_emb (paddle.Tensor): postional encoding, must not be None
for ConformerEncoderLayer
mask_pad (paddle.Tensor): batch padding mask used for conv module.
(#batch, 1，time), (0, 0, 0) means fake mask.
att_cache (paddle.Tensor): Cache tensor of the KEY & VALUE
(#batch=1, head, cache_t1, d_k * 2), head * d_k == size.
cnn_cache (paddle.Tensor): Convolution cache in conformer layer
(1, #batch=1, size, cache_t2). First dim will not be used, just
for dy2st.
Returns:
paddle.Tensor: Output tensor (#batch, time, size).
paddle.Tensor: Mask tensor (#batch, time, time).
paddle.Tensor: att_cache tensor,
(#batch=1, head, cache_t1 + time, d_k * 2).
paddle.Tensor: cnn_cahce tensor (#batch, size, cache_t2).
"""
# (1, #batch=1, size, cache_t2) -> (#batch=1, size, cache_t2)
cnn_cache = paddle.squeeze(cnn_cache, axis=0)
# whether to use macaron style FFN
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)
x_att, new_att_cache = self.self_attn(
x, x, x, mask, pos_emb, cache=att_cache)
if self.concat_after:
x_concat = paddle.concat((x, x_att), axis=-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
# Fake new cnn cache here, and then change it in conv_module
new_cnn_cache = paddle.zeros([0, 0, 0], dtype=x.dtype)
if self.conv_module is not None:
residual = x
if self.normalize_before:
x = self.norm_conv(x)
x, new_cnn_cache = self.conv_module(x, mask_pad, cnn_cache)
x = residual + self.dropout(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)
return x, mask, new_att_cache, new_cnn_cache
[docs]class SqueezeformerEncoderLayer(nn.Layer):
"""Encoder layer module."""
def __init__(self,
size: int,
self_attn: paddle.nn.Layer,
feed_forward1: Optional[nn.Layer]=None,
conv_module: Optional[nn.Layer]=None,
feed_forward2: Optional[nn.Layer]=None,
normalize_before: bool=False,
dropout_rate: float=0.1,
concat_after: bool=False):
"""Construct an EncoderLayer object.
Args:
size (int): Input dimension.
self_attn (paddle.nn.Layer): Self-attention module instance.
`MultiHeadedAttention`, `RelPositionMultiHeadedAttention` or `RoPERelPositionMultiHeadedAttention`
instance can be used as the argument.
feed_forward1 (paddle.nn.Layer): Feed-forward module instance.
`PositionwiseFeedForward` instance can be used as the argument.
conv_module (paddle.nn.Layer): Convolution module instance.
`ConvlutionLayer` instance can be used as the argument.
feed_forward2 (paddle.nn.Layer): Feed-forward module instance.
`PositionwiseFeedForward` instance can be used as the argument.
dropout_rate (float): Dropout rate.
normalize_before (bool):
True: use layer_norm before each sub-block.
False: use layer_norm after each sub-block.
"""
super().__init__()
self.size = size
self.self_attn = self_attn
self.layer_norm1 = LayerNorm(size)
self.ffn1 = feed_forward1
self.layer_norm2 = LayerNorm(size)
self.conv_module = conv_module
self.layer_norm3 = LayerNorm(size)
self.ffn2 = feed_forward2
self.layer_norm4 = LayerNorm(size)
self.normalize_before = normalize_before
self.dropout = nn.Dropout(dropout_rate)
self.concat_after = concat_after
if concat_after:
self.concat_linear = Linear(size + size, size)
else:
self.concat_linear = nn.Identity()
[docs] def forward(
self,
x: paddle.Tensor,
mask: paddle.Tensor,
pos_emb: paddle.Tensor,
mask_pad: paddle.Tensor=paddle.ones([0, 0, 0], dtype=paddle.bool),
att_cache: paddle.Tensor=paddle.zeros([0, 0, 0, 0]),
cnn_cache: paddle.Tensor=paddle.zeros([0, 0, 0, 0]),
) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor, paddle.Tensor]:
"""Compute encoded features.
Args:
x (paddle.Tensor): Input tensor (#batch, time, size).
mask (paddle.Tensor): Mask tensor for the input (#batch, time, time).
(0,0,0) means fake mask.
pos_emb (paddle.Tensor): postional encoding, must not be None
for ConformerEncoderLayer
mask_pad (paddle.Tensor): batch padding mask used for conv module.
(#batch, 1，time), (0, 0, 0) means fake mask.
att_cache (paddle.Tensor): Cache tensor of the KEY & VALUE
(#batch=1, head, cache_t1, d_k * 2), head * d_k == size.
cnn_cache (paddle.Tensor): Convolution cache in conformer layer
(1, #batch=1, size, cache_t2). First dim will not be used, just
for dy2st.
Returns:
paddle.Tensor: Output tensor (#batch, time, size).
paddle.Tensor: Mask tensor (#batch, time, time).
paddle.Tensor: att_cache tensor,
(#batch=1, head, cache_t1 + time, d_k * 2).
paddle.Tensor: cnn_cahce tensor (#batch, size, cache_t2).
"""
# self attention module
residual = x
if self.normalize_before:
x = self.layer_norm1(x)
x_att, new_att_cache = self.self_attn(x, x, x, mask, pos_emb, att_cache)
if self.concat_after:
x_concat = paddle.concat((x, x_att), axis=-1)
x = residual + self.concat_linear(x_concat)
else:
x = residual + self.dropout(x_att)
if not self.normalize_before:
x = self.layer_norm1(x)
# ffn module
residual = x
if self.normalize_before:
x = self.layer_norm2(x)
x = self.ffn1(x)
x = residual + self.dropout(x)
if not self.normalize_before:
x = self.layer_norm2(x)
# conv module
residual = x
if self.normalize_before:
x = self.layer_norm3(x)
x, new_cnn_cache = self.conv_module(x, mask_pad, cnn_cache)
x = residual + self.dropout(x)
if not self.normalize_before:
x = self.layer_norm3(x)
# ffn module
residual = x
if self.normalize_before:
x = self.layer_norm4(x)
x = self.ffn2(x)
# we do not use dropout here since it is inside feed forward function
x = residual + self.dropout(x)
if not self.normalize_before:
x = self.layer_norm4(x)
return x, mask, new_att_cache, new_cnn_cache