# 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)
"""Positonal Encoding Module."""
import math
from typing import Tuple
import paddle
from paddle import nn
from paddlespeech.s2t.utils.log import Log
logger = Log(__name__).getlog()
__all__ = [
"PositionalEncodingInterface", "NoPositionalEncoding", "PositionalEncoding",
"RelPositionalEncoding"
]
[docs]class PositionalEncodingInterface:
[docs] def forward(self, x: paddle.Tensor,
offset: int=0) -> Tuple[paddle.Tensor, paddle.Tensor]:
"""Compute positional encoding.
Args:
x (paddle.Tensor): Input tensor (batch, time, `*`).
Returns:
paddle.Tensor: Encoded tensor (batch, time, `*`).
paddle.Tensor: Positional embedding tensor (1, time, `*`).
"""
raise NotImplementedError("forward method is not implemented")
[docs] def position_encoding(self, offset: int, size: int) -> paddle.Tensor:
""" For getting encoding in a streaming fashion
Args:
offset (int): start offset
size (int): requried size of position encoding
Returns:
paddle.Tensor: Corresponding position encoding
"""
raise NotImplementedError("position_encoding method is not implemented")
[docs]class NoPositionalEncoding(nn.Layer, PositionalEncodingInterface):
def __init__(self,
d_model: int,
dropout_rate: float,
max_len: int=5000,
reverse: bool=False):
nn.Layer.__init__(self)
[docs] def forward(self, x: paddle.Tensor,
offset: int=0) -> Tuple[paddle.Tensor, paddle.Tensor]:
return x, None
[docs] def position_encoding(self, offset: int, size: int) -> paddle.Tensor:
return None
[docs]class PositionalEncoding(nn.Layer, PositionalEncodingInterface):
def __init__(self,
d_model: int,
dropout_rate: float,
max_len: int=5000,
reverse: bool=False):
"""Positional encoding.
PE(pos, 2i) = sin(pos/(10000^(2i/dmodel)))
PE(pos, 2i+1) = cos(pos/(10000^(2i/dmodel)))
Args:
d_model (int): embedding dim.
dropout_rate (float): dropout rate.
max_len (int, optional): maximum input length. Defaults to 5000.
reverse (bool, optional): Not used. Defaults to False.
"""
nn.Layer.__init__(self)
self.d_model = paddle.to_tensor(d_model)
self.max_len = max_len
self.xscale = paddle.to_tensor(math.sqrt(self.d_model))
self.dropout = nn.Dropout(p=dropout_rate)
self.base = paddle.to_tensor(10000.0)
self.pe = paddle.zeros([1, self.max_len, self.d_model]) #[B=1,T,D]
position = paddle.arange(
0, self.max_len, dtype=paddle.float32).unsqueeze(1) #[T, 1]
# base^{-2(i-1)/d)}, i \in (1,2...,d/2)
div_term = paddle.exp(
-paddle.arange(0, self.d_model, 2, dtype=paddle.float32) *
(paddle.log(self.base) / self.d_model))
# [B,T,D]
self.pe[:, :, 0::2] = paddle.sin(position * div_term)
self.pe[:, :, 1::2] = paddle.cos(position * div_term)
[docs] def forward(self, x: paddle.Tensor,
offset: int=0) -> Tuple[paddle.Tensor, paddle.Tensor]:
"""Add positional encoding.
Args:
x (paddle.Tensor): Input. Its shape is (batch, time, ...)
offset (int): position offset
Returns:
paddle.Tensor: Encoded tensor. Its shape is (batch, time, ...)
paddle.Tensor: for compatibility to RelPositionalEncoding, (batch=1, time, ...)
"""
assert offset + x.shape[
1] < self.max_len, "offset: {} + x.shape[1]: {} is larger than the max_len: {}".format(
offset, x.shape[1], self.max_len)
pos_emb = self.pe[:, offset:offset + x.shape[1]]
x = x * self.xscale + pos_emb
return self.dropout(x), self.dropout(pos_emb)
[docs] def position_encoding(self, offset: int, size: int) -> paddle.Tensor:
""" For getting encoding in a streaming fashion
Attention!!!!!
we apply dropout only once at the whole utterance level in a none
streaming way, but will call this function several times with
increasing input size in a streaming scenario, so the dropout will
be applied several times.
Args:
offset (int): start offset
size (int): requried size of position encoding
Returns:
paddle.Tensor: Corresponding position encoding, #[1, T, D].
"""
assert offset + size < self.max_len
return self.dropout(self.pe[:, offset:offset + size])
[docs]class RelPositionalEncoding(PositionalEncoding):
"""Relative positional encoding module.
See : Appendix B in https://arxiv.org/abs/1901.02860
"""
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)
logger.info(f"max len: {max_len}")
[docs] def forward(self, x: paddle.Tensor,
offset: int=0) -> Tuple[paddle.Tensor, paddle.Tensor]:
"""Compute positional encoding.
Args:
x (paddle.Tensor): Input tensor (batch, time, `*`).
Returns:
paddle.Tensor: Encoded tensor (batch, time, `*`).
paddle.Tensor: Positional embedding tensor (1, time, `*`).
"""
assert offset + x.shape[
1] < self.max_len, "offset: {} + x.shape[1]: {} is larger than the max_len: {}".format(
offset, x.shape[1], self.max_len)
x = x * self.xscale
pos_emb = self.pe[:, offset:offset + x.shape[1]]
return self.dropout(x), self.dropout(pos_emb)
# RotaryRelPositionalEncoding is same to RelPositionalEncoding
class ScaledRotaryRelPositionalEncoding(RelPositionalEncoding):
"""Scaled Rotary Relative positional encoding module.
POSITION INTERPOLATION: : https://arxiv.org/pdf/2306.15595v2.pdf
"""
def __init__(self,
d_model: int,
dropout_rate: float,
max_len: int=5000,
scale=1):
"""
Args:
d_model (int): Embedding dimension.
dropout_rate (float): Dropout rate.
max_len (int, optional): [Maximum input length.]. Defaults to 5000.
scale (int): Interpolation max input length to `scale * max_len` positions.
"""
super().__init__(d_model, dropout_rate, max_len, reverse=True)
self.pscale = paddle.to_tensor(scale)
self.max_len = max_len * scale
def sinusoidal_embeddings(self,
pos: paddle.Tensor,
dim: paddle.Tensor,
base=10000) -> paddle.Tensor:
"""计算pos位置的dim维sinusoidal编码"""
assert dim % 2 == 0
# (d/2,)
indices = paddle.arange(0, dim // 2, dtype=pos.dtype)
indices = paddle.pow(paddle.cast(base, pos.dtype), -2 * indices / dim)
# pos (1, T), indices (d/2,) -> (1, T, d/2)
embeddings = paddle.einsum('...,d->...d', pos, indices)
# (1, T, d/2, 2)
embeddings = paddle.stack(
[paddle.sin(embeddings), paddle.cos(embeddings)], axis=-1)
# (1, T, d)
embeddings = paddle.flatten(embeddings, start_axis=-2, stop_axis=-1)
return embeddings
def forward(self, x: paddle.Tensor,
offset: int=0) -> Tuple[paddle.Tensor, paddle.Tensor]:
"""Compute positional encoding.
Args:
x (paddle.Tensor): Input tensor (batch, time, `*`).
Returns:
paddle.Tensor: Encoded tensor (batch, time, `*`).
paddle.Tensor: Positional embedding tensor (1, time, `*`).
"""
x = x * self.xscale
B, T, D = x.shape
assert D == self.d_model
# postion interploation
start = 0
end = T * self.pscale
assert end <= self.max_len
position = paddle.arange(start, end, dtype=x.dtype).unsqueeze(0)
position *= 1.0 / self.pscale
pe = self.sinusoidal_embeddings(position, self.d_model, base=self.base)
pos_emb = pe[:, offset:offset + x.shape[1]]
return self.dropout(x), self.dropout(pos_emb)
def position_encoding(self, offset: int, size: int) -> paddle.Tensor:
""" For getting encoding in a streaming fashion
Attention!!!!!
we apply dropout only once at the whole utterance level in a none
streaming way, but will call this function several times with
increasing input size in a streaming scenario, so the dropout will
be applied several times.
Args:
offset (int): start offset
size (int): requried size of position encoding
Returns:
paddle.Tensor: Corresponding position encoding, #[1, T, D].
"""
# postion interploation
start = offset
end = (offset + size) * self.pscale
assert end <= self.max_len
position = paddle.arange(
start, end, dtype=paddle.get_default_dtype()).unsqueeze(0)
position *= 1.0 / self.pscale
pe = self.sinusoidal_embeddings(position, self.d_model, base=self.base)
return self.dropout(pe)