You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
PaddleSpeech/deepspeech/modules/encoder_layer.py

285 lines
11 KiB

This file contains ambiguous Unicode characters!

This file contains ambiguous Unicode characters that may be confused with others in your current locale. If your use case is intentional and legitimate, you can safely ignore this warning. Use the Escape button to highlight these characters.

# Copyright (c) 2021 PaddlePaddle Authors. 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.
"""Encoder self-attention layer definition."""
from typing import Optional
from typing import Tuple
import paddle
from paddle import nn
from deepspeech.utils.log import Log
logger = Log(__name__).getlog()
__all__ = ["TransformerEncoderLayer", "ConformerEncoderLayer"]
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` or `RelPositionMultiHeadedAttention`
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 = nn.LayerNorm(size, epsilon=1e-12)
self.norm2 = nn.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 = nn.Linear(size + size, size)
def forward(
self,
x: paddle.Tensor,
mask: paddle.Tensor,
pos_emb: paddle.Tensor,
mask_pad: Optional[paddle.Tensor]=None,
output_cache: Optional[paddle.Tensor]=None,
cnn_cache: Optional[paddle.Tensor]=None,
) -> Tuple[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).
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.
output_cache (paddle.Tensor): Cache tensor of the output
(#batch, time2, size), time2 < time in x.
cnn_cache (paddle.Tensor): not used here, it's for interface
compatibility to ConformerEncoderLayer
Returns:
paddle.Tensor: Output tensor (#batch, time, size).
paddle.Tensor: Mask tensor (#batch, time).
paddle.Tensor: Fake cnn cache tensor for api compatibility with Conformer (#batch, channels, time').
"""
residual = x
if self.normalize_before:
x = self.norm1(x)
if output_cache is None:
x_q = x
else:
assert output_cache.shape[0] == x.shape[0]
assert output_cache.shape[1] < x.shape[1]
assert output_cache.shape[2] == self.size
chunk = x.shape[1] - output_cache.shape[1]
x_q = x[:, -chunk:, :]
residual = residual[:, -chunk:, :]
mask = mask[:, -chunk:, :]
if self.concat_after:
x_concat = paddle.concat(
(x, self.self_attn(x_q, x, x, mask)), axis=-1)
x = residual + self.concat_linear(x_concat)
else:
x = residual + self.dropout(self.self_attn(x_q, x, x, mask))
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)
if output_cache is not None:
x = paddle.concat([output_cache, x], axis=1)
fake_cnn_cache = paddle.zeros([1], dtype=x.dtype)
return x, mask, fake_cnn_cache
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` or `RelPositionMultiHeadedAttention`
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 = nn.LayerNorm(size, epsilon=1e-12) # for the FNN module
self.norm_mha = nn.LayerNorm(size, epsilon=1e-12) # for the MHA module
if feed_forward_macaron is not None:
self.norm_ff_macaron = nn.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 = nn.LayerNorm(
size, epsilon=1e-12) # for the CNN module
self.norm_final = nn.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
self.concat_linear = nn.Linear(size + size, size)
def forward(
self,
x: paddle.Tensor,
mask: paddle.Tensor,
pos_emb: paddle.Tensor,
mask_pad: Optional[paddle.Tensor]=None,
output_cache: Optional[paddle.Tensor]=None,
cnn_cache: Optional[paddle.Tensor]=None,
) -> Tuple[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, timetime).
pos_emb (paddle.Tensor): positional encoding, must not be None
for ConformerEncoderLayer.
mask_pad (paddle.Tensor): batch padding mask used for conv module, (B, 1, T).
output_cache (paddle.Tensor): Cache tensor of the encoder output
(#batch, time2, size), time2 < time in x.
cnn_cache (paddle.Tensor): Convolution cache in conformer layer
Returns:
paddle.Tensor: Output tensor (#batch, time, size).
paddle.Tensor: Mask tensor (#batch, time).
paddle.Tensor: New cnn cache tensor (#batch, channels, time').
"""
# 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)
if output_cache is None:
x_q = x
else:
assert output_cache.shape[0] == x.shape[0]
assert output_cache.shape[1] < x.shape[1]
assert output_cache.shape[2] == self.size
chunk = x.shape[1] - output_cache.shape[1]
x_q = x[:, -chunk:, :]
residual = residual[:, -chunk:, :]
mask = mask[:, -chunk:, :]
x_att = self.self_attn(x_q, x, x, pos_emb, mask)
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([1], 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)
if output_cache is not None:
x = paddle.concat([output_cache, x], axis=1)
return x, mask, new_cnn_cache