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127 lines
3.8 KiB
127 lines
3.8 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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"""Positional Encoding Module."""
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import math
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import paddle
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from paddle import nn
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class PositionalEncoding(nn.Layer):
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"""Positional encoding.
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Parameters
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----------
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d_model : int
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Embedding dimension.
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dropout_rate : float
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Dropout rate.
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max_len : int
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Maximum input length.
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reverse : bool
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Whether to reverse the input position.
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"""
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def __init__(self, d_model, dropout_rate, max_len=5000, reverse=False):
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"""Construct an PositionalEncoding object."""
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super(PositionalEncoding, self).__init__()
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self.d_model = d_model
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self.reverse = reverse
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self.xscale = math.sqrt(self.d_model)
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self.dropout = nn.Dropout(p=dropout_rate)
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self.pe = None
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self.extend_pe(paddle.expand(paddle.to_tensor(0.0), (1, max_len)))
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def extend_pe(self, x):
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"""Reset the positional encodings."""
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pe = paddle.zeros([x.shape[1], self.d_model])
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if self.reverse:
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position = paddle.arange(
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x.shape[1] - 1, -1, -1.0, dtype=paddle.float32).unsqueeze(1)
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else:
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position = paddle.arange(
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0, x.shape[1], dtype=paddle.float32).unsqueeze(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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pe[:, 0::2] = paddle.sin(position * div_term)
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pe[:, 1::2] = paddle.cos(position * div_term)
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pe = pe.unsqueeze(0)
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self.pe = pe
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def forward(self, x: paddle.Tensor):
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"""Add positional encoding.
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Parameters
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----------
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x : paddle.Tensor
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Input tensor (batch, time, `*`).
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Returns
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----------
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paddle.Tensor
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Encoded tensor (batch, time, `*`).
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"""
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self.extend_pe(x)
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x = x * self.xscale + self.pe[:, :x.shape[1]]
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return self.dropout(x)
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class ScaledPositionalEncoding(PositionalEncoding):
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"""Scaled positional encoding module.
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See Sec. 3.2 https://arxiv.org/abs/1809.08895
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Parameters
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----------
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d_model : int
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Embedding dimension.
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dropout_rate : float
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Dropout rate.
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max_len : int
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Maximum input length.
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"""
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def __init__(self, d_model, dropout_rate, max_len=5000):
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"""Initialize class."""
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super().__init__(
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d_model=d_model, dropout_rate=dropout_rate, max_len=max_len)
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x = paddle.ones([1], dtype="float32")
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self.alpha = paddle.create_parameter(
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shape=x.shape,
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dtype=str(x.numpy().dtype),
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default_initializer=paddle.nn.initializer.Assign(x))
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def reset_parameters(self):
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"""Reset parameters."""
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self.alpha = paddle.to_tensor(1.0)
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def forward(self, x):
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"""Add positional encoding.
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Parameters
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----------
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x : paddle.Tensor
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Input tensor (batch, time, `*`).
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Returns
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----------
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paddle.Tensor
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Encoded tensor (batch, time, `*`).
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"""
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self.extend_pe(x)
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x = x + self.alpha * self.pe[:, :x.shape[1]]
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return self.dropout(x)
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