# 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)
"""Decoder 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__ = ["DecoderLayer"]
[docs]class DecoderLayer(nn.Layer):
"""Single decoder layer module.
Args:
size (int): Input dimension.
self_attn (nn.Layer): Self-attention module instance.
`MultiHeadedAttention` instance can be used as the argument.
src_attn (nn.Layer): Self-attention module instance.
`MultiHeadedAttention` 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)
"""
def __init__(
self,
size: int,
self_attn: nn.Layer,
src_attn: nn.Layer,
feed_forward: nn.Layer,
dropout_rate: float,
normalize_before: bool=True,
concat_after: bool=False, ):
"""Construct an DecoderLayer object."""
super().__init__()
self.size = size
self.self_attn = self_attn
self.src_attn = src_attn
self.feed_forward = feed_forward
self.norm1 = LayerNorm(size, epsilon=1e-12)
self.norm2 = LayerNorm(size, epsilon=1e-12)
self.norm3 = LayerNorm(size, epsilon=1e-12)
self.dropout = nn.Dropout(dropout_rate)
self.normalize_before = normalize_before
self.concat_after = concat_after
self.concat_linear1 = Linear(size + size, size)
self.concat_linear2 = Linear(size + size, size)
[docs] def forward(
self,
tgt: paddle.Tensor,
tgt_mask: paddle.Tensor,
memory: paddle.Tensor,
memory_mask: paddle.Tensor,
cache: Optional[paddle.Tensor]=None
) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor, paddle.Tensor]:
"""Compute decoded features.
Args:
tgt (paddle.Tensor): Input tensor (#batch, maxlen_out, size).
tgt_mask (paddle.Tensor): Mask for input tensor
(#batch, maxlen_out).
memory (paddle.Tensor): Encoded memory
(#batch, maxlen_in, size).
memory_mask (paddle.Tensor): Encoded memory mask
(#batch, maxlen_in).
cache (paddle.Tensor): cached tensors.
(#batch, maxlen_out - 1, size).
Returns:
paddle.Tensor: Output tensor (#batch, maxlen_out, size).
paddle.Tensor: Mask for output tensor (#batch, maxlen_out).
paddle.Tensor: Encoded memory (#batch, maxlen_in, size).
paddle.Tensor: Encoded memory mask (#batch, maxlen_in).
"""
residual = tgt
if self.normalize_before:
tgt = self.norm1(tgt)
if cache is None:
tgt_q = tgt
tgt_q_mask = tgt_mask
else:
# compute only the last frame query keeping dim: max_time_out -> 1
assert cache.shape == [
tgt.shape[0],
tgt.shape[1] - 1,
self.size,
], f"{cache.shape} == {[tgt.shape[0], tgt.shape[1] - 1, self.size]}"
tgt_q = tgt[:, -1:, :]
residual = residual[:, -1:, :]
tgt_q_mask = tgt_mask[:, -1:, :]
if self.concat_after:
tgt_concat = paddle.cat(
(tgt_q, self.self_attn(tgt_q, tgt, tgt, tgt_q_mask)[0]), dim=-1)
x = residual + self.concat_linear1(tgt_concat)
else:
x = residual + self.dropout(
self.self_attn(tgt_q, tgt, tgt, tgt_q_mask)[0])
if not self.normalize_before:
x = self.norm1(x)
residual = x
if self.normalize_before:
x = self.norm2(x)
if self.concat_after:
x_concat = paddle.cat(
(x, self.src_attn(x, memory, memory, memory_mask)[0]), dim=-1)
x = residual + self.concat_linear2(x_concat)
else:
x = residual + self.dropout(
self.src_attn(x, memory, memory, memory_mask)[0])
if not self.normalize_before:
x = self.norm2(x)
residual = x
if self.normalize_before:
x = self.norm3(x)
x = residual + self.dropout(self.feed_forward(x))
if not self.normalize_before:
x = self.norm3(x)
if cache is not None:
x = paddle.cat([cache, x], dim=1)
return x, tgt_mask, memory, memory_mask