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122 lines
4.7 KiB
122 lines
4.7 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Positonal Encoding Module."""
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import math
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from typing import Tuple
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import paddle
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from paddle import nn
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from deepspeech.utils.log import Log
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logger = Log(__name__).getlog()
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__all__ = ["PositionalEncoding", "RelPositionalEncoding"]
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class PositionalEncoding(nn.Layer):
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def __init__(self,
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d_model: int,
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dropout_rate: float,
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max_len: int=5000,
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reverse: bool=False):
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"""Positional encoding.
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PE(pos, 2i) = sin(pos/(10000^(2i/dmodel)))
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PE(pos, 2i+1) = cos(pos/(10000^(2i/dmodel)))
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Args:
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d_model (int): embedding dim.
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dropout_rate (float): dropout rate.
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max_len (int, optional): maximum input length. Defaults to 5000.
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reverse (bool, optional): Not used. Defaults to False.
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"""
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super().__init__()
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self.d_model = d_model
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self.max_len = max_len
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self.xscale = paddle.to_tensor(math.sqrt(self.d_model))
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self.dropout = nn.Dropout(p=dropout_rate)
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self.pe = paddle.zeros([self.max_len, self.d_model]) #[T,D]
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position = paddle.arange(
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0, self.max_len, dtype=paddle.float32).unsqueeze(1) #[T, 1]
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div_term = paddle.exp(
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paddle.arange(0, self.d_model, 2, dtype=paddle.float32) *
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-(math.log(10000.0) / self.d_model))
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self.pe[:, 0::2] = paddle.sin(position * div_term)
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self.pe[:, 1::2] = paddle.cos(position * div_term)
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self.pe = self.pe.unsqueeze(0) #[1, T, D]
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def forward(self, x: paddle.Tensor,
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offset: int=0) -> Tuple[paddle.Tensor, paddle.Tensor]:
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"""Add positional encoding.
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Args:
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x (paddle.Tensor): Input. Its shape is (batch, time, ...)
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offset (int): position offset
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Returns:
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paddle.Tensor: Encoded tensor. Its shape is (batch, time, ...)
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paddle.Tensor: for compatibility to RelPositionalEncoding, (batch=1, time, ...)
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"""
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T = x.shape[1]
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assert offset + x.shape[1] < self.max_len
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#TODO(Hui Zhang): using T = x.size(1), __getitem__ not support Tensor
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pos_emb = self.pe[:, offset:offset + T]
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x = x * self.xscale + pos_emb
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return self.dropout(x), self.dropout(pos_emb)
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def position_encoding(self, offset: int, size: int) -> paddle.Tensor:
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""" For getting encoding in a streaming fashion
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Attention!!!!!
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we apply dropout only once at the whole utterance level in a none
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streaming way, but will call this function several times with
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increasing input size in a streaming scenario, so the dropout will
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be applied several times.
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Args:
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offset (int): start offset
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size (int): requried size of position encoding
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Returns:
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paddle.Tensor: Corresponding encoding
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"""
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assert offset + size < self.max_len
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return self.dropout(self.pe[:, offset:offset + size])
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class RelPositionalEncoding(PositionalEncoding):
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"""Relative positional encoding module.
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See : Appendix B in https://arxiv.org/abs/1901.02860
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"""
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def __init__(self, d_model: int, dropout_rate: float, max_len: int=5000):
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"""
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Args:
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d_model (int): Embedding dimension.
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dropout_rate (float): Dropout rate.
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max_len (int, optional): [Maximum input length.]. Defaults to 5000.
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"""
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super().__init__(d_model, dropout_rate, max_len, reverse=True)
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def forward(self, x: paddle.Tensor,
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offset: int=0) -> Tuple[paddle.Tensor, paddle.Tensor]:
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"""Compute positional encoding.
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Args:
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x (paddle.Tensor): Input tensor (batch, time, `*`).
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Returns:
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paddle.Tensor: Encoded tensor (batch, time, `*`).
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paddle.Tensor: Positional embedding tensor (1, time, `*`).
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"""
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assert offset + x.shape[1] < self.max_len
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x = x * self.xscale
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#TODO(Hui Zhang): using x.size(1), __getitem__ not support Tensor
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pos_emb = self.pe[:, offset:offset + x.shape[1]]
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return self.dropout(x), self.dropout(pos_emb)
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