# 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
[docs]class PositionalEncoding(nn.Layer):
"""Positional encoding.
Args:
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().__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)))
[docs] 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
[docs] def forward(self, x: paddle.Tensor):
"""Add positional encoding.
Args:
x (Tensor):
Input tensor (batch, time, `*`).
Returns:
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)
[docs]class ScaledPositionalEncoding(PositionalEncoding):
"""Scaled positional encoding module.
See Sec. 3.2 https://arxiv.org/abs/1809.08895
Args:
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=nn.initializer.Assign(x))
[docs] def reset_parameters(self):
"""Reset parameters."""
self.alpha = paddle.ones([1])
[docs] def forward(self, x):
"""Add positional encoding.
Args:
x (Tensor):
Input tensor (batch, time, `*`).
Returns:
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)
[docs]class RelPositionalEncoding(nn.Layer):
"""Relative positional encoding module (new implementation).
Details can be found in https://github.com/espnet/espnet/pull/2816.
See : Appendix B in https://arxiv.org/abs/1901.02860
Args:
d_model (int):
Embedding dimension.
dropout_rate (float):
Dropout rate.
max_len (int):
Maximum input length.
"""
def __init__(self, d_model, dropout_rate, max_len=5000, dtype="float32"):
"""Construct an PositionalEncoding object."""
super().__init__()
self.d_model = d_model
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)))
[docs] def extend_pe(self, x):
"""Reset the positional encodings."""
if self.pe is not None:
# self.pe contains both positive and negative parts
# the length of self.pe is 2 * input_len - 1
if paddle.shape(self.pe)[1] >= paddle.shape(x)[1] * 2 - 1:
return
# Suppose `i` means to the position of query vecotr and `j` means the
# position of key vector. We use position relative positions when keys
# are to the left (i>j) and negative relative positions otherwise (i<j).
x_shape = paddle.shape(x)
pe_positive = paddle.zeros([x_shape[1], self.d_model])
pe_negative = paddle.zeros([x_shape[1], self.d_model])
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_positive[:, 0::2] = paddle.sin(position * div_term)
pe_positive[:, 1::2] = paddle.cos(position * div_term)
pe_negative[:, 0::2] = paddle.sin(-1 * position * div_term)
pe_negative[:, 1::2] = paddle.cos(-1 * position * div_term)
# Reserve the order of positive indices and concat both positive and
# negative indices. This is used to support the shifting trick
# as in https://arxiv.org/abs/1901.02860
pe_positive = paddle.flip(pe_positive, [0]).unsqueeze(0)
pe_negative = pe_negative[1:].unsqueeze(0)
pe = paddle.concat([pe_positive, pe_negative], axis=1)
self.pe = pe
[docs] def forward(self, x: paddle.Tensor):
"""Add positional encoding.
Args:
x (Tensor):
Input tensor (batch, time, `*`).
Returns:
Tensor: Encoded tensor (batch, time, `*`).
"""
self.extend_pe(x)
x = x * self.xscale
T = paddle.shape(x)[1]
pe_size = paddle.shape(self.pe)
tmp = paddle.cast(paddle.floor(pe_size[1] / 2), dtype='int32')
pos_emb = self.pe[:, tmp - T + 1:tmp + T, ]
return self.dropout(x), self.dropout(pos_emb)
[docs]class LegacyRelPositionalEncoding(PositionalEncoding):
"""Relative positional encoding module (old version).
Details can be found in https://github.com/espnet/espnet/pull/2816.
See : Appendix B in https://arxiv.org/abs/1901.02860
Args:
d_model (int):
Embedding dimension.
dropout_rate (float):
Dropout rate.
max_len (int):
Maximum input length.
"""
def __init__(self, d_model: int, dropout_rate: float, max_len: int=5000):
"""
Args:
d_model (int):
Embedding dimension.
dropout_rate (float):
Dropout rate.
max_len (int, optional):
[Maximum input length.]. Defaults to 5000.
"""
super().__init__(d_model, dropout_rate, max_len, reverse=True)
[docs] def extend_pe(self, x):
"""Reset the positional encodings."""
if self.pe is not None:
if paddle.shape(self.pe)[1] >= paddle.shape(x)[1]:
return
pe = paddle.zeros((paddle.shape(x)[1], self.d_model))
if self.reverse:
position = paddle.arange(
paddle.shape(x)[1] - 1, -1, -1.0,
dtype=paddle.float32).unsqueeze(1)
else:
position = paddle.arange(
0, paddle.shape(x)[1], dtype=paddle.float32).unsqueeze(1)
div_term = paddle.exp(
paddle.arange(0, self.d_model, 2, dtype=paddle.float32) *
-(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
[docs] def forward(self, x: paddle.Tensor):
"""Compute positional encoding.
Args:
x (Tensor):
Input tensor (batch, time, `*`).
Returns:
Tensor:
Encoded tensor (batch, time, `*`).
Tensor:
Positional embedding tensor (1, time, `*`).
"""
self.extend_pe(x)
x = x * self.xscale
pos_emb = self.pe[:, :paddle.shape(x)[1]]
return self.dropout(x), self.dropout(pos_emb)