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PaddleSpeech/deepspeech/modules/embedding.py

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4.9 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.
"""Positonal Encoding Module."""
import math
import logging
import numpy as np
from typing import Tuple
import paddle
from paddle import nn
from paddle.nn import functional as F
from paddle.nn import initializer as I
logger = logging.getLogger(__name__)
__all__ = ["PositionalEncoding", "RelPositionalEncoding"]
# TODO(Hui Zhang): remove this hack
paddle.float32 = 'float32'
class PositionalEncoding(nn.Layer):
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.
"""
super().__init__()
self.d_model = 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.pe = paddle.zeros(self.max_len, self.d_model) #[T,D]
position = paddle.arange(
0, self.max_len, 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))
self.pe[:, 0::2] = paddle.sin(position * div_term)
self.pe[:, 1::2] = paddle.cos(position * div_term)
self.pe = self.pe.unsqueeze(0) #[1, T, D]
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
"""
T = paddle.shape(x)[1]
assert offset + T < self.max_len
#assert offset + x.size(1) < self.max_len
#self.pe = self.pe.to(x.device)
#pos_emb = self.pe[:, offset:offset + x.size(1)]
pos_emb = self.pe[:, offset:offset + T]
x = x * self.xscale + pos_emb
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 encoding
"""
assert offset + size < self.max_len
return self.dropout(self.pe[:, offset:offset + size])
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)
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, `*`).
"""
T = paddle.shape()[1]
assert offset + T < self.max_len
#assert offset + x.size(1) < self.max_len
#self.pe = self.pe.to(x.device)
x = x * self.xscale
#pos_emb = self.pe[:, offset:offset + x.size(1)]
pos_emb = self.pe[:, offset:offset + T]
return self.dropout(x), self.dropout(pos_emb)