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PaddleSpeech/paddlespeech/t2s/modules/transformer/decoder_layer.py

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# 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.
# Modified from espnet(https://github.com/espnet/espnet)
"""Decoder self-attention layer definition."""
import paddle
from paddle import nn
from paddlespeech.t2s.modules.layer_norm import LayerNorm
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`, `MultiLayeredConv1d`, or `Conv1dLinear` instance can be used as the argument.
dropout_rate (float):
Dropout rate.
normalize_before (bool):
Whether to use layer_norm before the first block.
concat_after (bool):
Whether to concat attention layer's input and output.
if True, additional linear will be applied.
i.e. x -> x + linear(concat(x, att(x)))
if False, no additional linear will be applied. i.e. x -> x + att(x)
"""
def __init__(
self,
size,
self_attn,
src_attn,
feed_forward,
dropout_rate,
normalize_before=True,
concat_after=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)
self.norm2 = LayerNorm(size)
self.norm3 = LayerNorm(size)
self.dropout = nn.Dropout(dropout_rate)
self.normalize_before = normalize_before
self.concat_after = concat_after
if self.concat_after:
self.concat_linear1 = nn.Linear(size + size, size)
self.concat_linear2 = nn.Linear(size + size, size)
def forward(self, tgt, tgt_mask, memory, memory_mask, cache=None):
"""Compute decoded features.
Args:
tgt(Tensor):
Input tensor (#batch, maxlen_out, size).
tgt_mask(Tensor):
Mask for input tensor (#batch, maxlen_out).
memory(Tensor):
Encoded memory, float32 (#batch, maxlen_in, size).
memory_mask(Tensor):
Encoded memory mask (#batch, maxlen_in).
cache(List[Tensor], optional):
List of cached tensors.
Each tensor shape should be (#batch, maxlen_out - 1, size). (Default value = None)
Returns:
Tensor
Output tensor(#batch, maxlen_out, size).
Tensor
Mask for output tensor (#batch, maxlen_out).
Tensor
Encoded memory (#batch, maxlen_in, size).
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 = None
if tgt_mask is not None:
tgt_mask = paddle.cast(tgt_mask, dtype="int64")
tgt_q_mask = tgt_mask[:, -1:, :]
tgt_q_mask = paddle.cast(tgt_q_mask, dtype="bool")
if self.concat_after:
tgt_concat = paddle.concat(
(tgt_q, self.self_attn(tgt_q, tgt, tgt, tgt_q_mask)), axis=-1)
x = residual + self.concat_linear1(tgt_concat)
else:
x = residual + self.dropout(
self.self_attn(tgt_q, tgt, tgt, tgt_q_mask))
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.concat(
(x, self.src_attn(x, memory, memory, memory_mask)), axis=-1)
x = residual + self.concat_linear2(x_concat)
else:
x = residual + self.dropout(
self.src_attn(x, memory, memory, memory_mask))
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.concat([cache, x], axis=1)
return x, tgt_mask, memory, memory_mask