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PaddleSpeech/paddlespeech/t2s/modules/fastspeech2_transformer/embedding.py

142 lines
4.1 KiB

# 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)
"""Positional Encoding Module."""
import math
import paddle
from paddle import nn
class PositionalEncoding(nn.Layer):
"""Positional encoding.
Parameters
----------
d_model : int
Embedding dimension.
dropout_rate : float
Dropout rate.
max_len : int
Maximum input length.
reverse : bool
Whether to reverse the input position.
type : str
dtype of param
"""
def __init__(self,
d_model,
dropout_rate,
max_len=5000,
dtype="float32",
reverse=False):
"""Construct an PositionalEncoding object."""
super(PositionalEncoding, self).__init__()
self.d_model = d_model
self.reverse = reverse
self.xscale = math.sqrt(self.d_model)
self.dropout = nn.Dropout(p=dropout_rate)
self.pe = None
self.dtype = dtype
self.extend_pe(paddle.expand(paddle.zeros([1]), (1, max_len)))
def extend_pe(self, x):
"""Reset the positional encodings."""
x_shape = paddle.shape(x)
pe = paddle.zeros([x_shape[1], self.d_model])
if self.reverse:
position = paddle.arange(
x_shape[1] - 1, -1, -1.0, dtype=self.dtype).unsqueeze(1)
else:
position = paddle.arange(
0, x_shape[1], dtype=self.dtype).unsqueeze(1)
div_term = paddle.exp(
paddle.arange(0, self.d_model, 2, dtype=self.dtype) *
-(math.log(10000.0) / self.d_model))
pe[:, 0::2] = paddle.sin(position * div_term)
pe[:, 1::2] = paddle.cos(position * div_term)
pe = pe.unsqueeze(0)
self.pe = pe
def forward(self, x: paddle.Tensor):
"""Add positional encoding.
Parameters
----------
x : paddle.Tensor
Input tensor (batch, time, `*`).
Returns
----------
paddle.Tensor
Encoded tensor (batch, time, `*`).
"""
self.extend_pe(x)
T = paddle.shape(x)[1]
x = x * self.xscale + self.pe[:, :T]
return self.dropout(x)
class ScaledPositionalEncoding(PositionalEncoding):
"""Scaled positional encoding module.
See Sec. 3.2 https://arxiv.org/abs/1809.08895
Parameters
----------
d_model : int
Embedding dimension.
dropout_rate : float
Dropout rate.
max_len : int
Maximum input length.
dtype : str
dtype of param
"""
def __init__(self, d_model, dropout_rate, max_len=5000, dtype="float32"):
"""Initialize class."""
super().__init__(
d_model=d_model,
dropout_rate=dropout_rate,
max_len=max_len,
dtype=dtype)
x = paddle.ones([1], dtype=self.dtype)
self.alpha = paddle.create_parameter(
shape=x.shape,
dtype=self.dtype,
default_initializer=paddle.nn.initializer.Assign(x))
def reset_parameters(self):
"""Reset parameters."""
self.alpha = paddle.ones([1])
def forward(self, x):
"""Add positional encoding.
Parameters
----------
x : paddle.Tensor
Input tensor (batch, time, `*`).
Returns
----------
paddle.Tensor
Encoded tensor (batch, time, `*`).
"""
self.extend_pe(x)
T = paddle.shape(x)[1]
x = x + self.alpha * self.pe[:, :T]
return self.dropout(x)