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246 lines
8.7 KiB
246 lines
8.7 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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# Modified from espnet(https://github.com/espnet/espnet)
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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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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): Maximum input length.
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reverse (bool): Whether to reverse the input position.
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type (str): dtype of param
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"""
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def __init__(self,
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d_model,
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dropout_rate,
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max_len=5000,
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dtype="float32",
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reverse=False):
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"""Construct an PositionalEncoding object."""
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super().__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.dtype = dtype
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self.extend_pe(paddle.expand(paddle.zeros([1]), (1, max_len)))
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def extend_pe(self, x):
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"""Reset the positional encodings."""
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x_shape = paddle.shape(x)
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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=self.dtype).unsqueeze(1)
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else:
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position = paddle.arange(
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0, x_shape[1], dtype=self.dtype).unsqueeze(1)
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div_term = paddle.exp(
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paddle.arange(0, self.d_model, 2, dtype=self.dtype) *
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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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Args:
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x (Tensor): Input tensor (batch, time, `*`).
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Returns:
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Tensor: Encoded tensor (batch, time, `*`).
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"""
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self.extend_pe(x)
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T = paddle.shape(x)[1]
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x = x * self.xscale + self.pe[:, :T]
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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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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): Maximum input length.
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dtype (str): dtype of param
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"""
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def __init__(self, d_model, dropout_rate, max_len=5000, dtype="float32"):
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"""Initialize class."""
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super().__init__(
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d_model=d_model,
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dropout_rate=dropout_rate,
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max_len=max_len,
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dtype=dtype)
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x = paddle.ones([1], dtype=self.dtype)
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self.alpha = paddle.create_parameter(
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shape=x.shape,
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dtype=self.dtype,
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default_initializer=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.ones([1])
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def forward(self, x):
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"""Add positional encoding.
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Args:
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x (Tensor): Input tensor (batch, time, `*`).
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Returns:
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Tensor: Encoded tensor (batch, time, `*`).
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"""
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self.extend_pe(x)
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T = paddle.shape(x)[1]
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x = x + self.alpha * self.pe[:, :T]
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return self.dropout(x)
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class RelPositionalEncoding(nn.Layer):
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"""Relative positional encoding module (new implementation).
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Details can be found in https://github.com/espnet/espnet/pull/2816.
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See : Appendix B in https://arxiv.org/abs/1901.02860
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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): Maximum input length.
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"""
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def __init__(self, d_model, dropout_rate, max_len=5000, dtype="float32"):
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"""Construct an PositionalEncoding object."""
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super().__init__()
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self.d_model = d_model
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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.dtype = dtype
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self.extend_pe(paddle.expand(paddle.zeros([1]), (1, max_len)))
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def extend_pe(self, x):
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"""Reset the positional encodings."""
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if self.pe is not None:
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# self.pe contains both positive and negative parts
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# the length of self.pe is 2 * input_len - 1
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if paddle.shape(self.pe)[1] >= paddle.shape(x)[1] * 2 - 1:
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return
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# Suppose `i` means to the position of query vecotr and `j` means the
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# position of key vector. We use position relative positions when keys
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# are to the left (i>j) and negative relative positions otherwise (i<j).
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x_shape = paddle.shape(x)
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pe_positive = paddle.zeros([x_shape[1], self.d_model])
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pe_negative = paddle.zeros([x_shape[1], self.d_model])
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position = paddle.arange(0, x_shape[1], dtype=self.dtype).unsqueeze(1)
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div_term = paddle.exp(
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paddle.arange(0, self.d_model, 2, dtype=self.dtype) *
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-(math.log(10000.0) / self.d_model))
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pe_positive[:, 0::2] = paddle.sin(position * div_term)
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pe_positive[:, 1::2] = paddle.cos(position * div_term)
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pe_negative[:, 0::2] = paddle.sin(-1 * position * div_term)
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pe_negative[:, 1::2] = paddle.cos(-1 * position * div_term)
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# Reserve the order of positive indices and concat both positive and
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# negative indices. This is used to support the shifting trick
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# as in https://arxiv.org/abs/1901.02860
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pe_positive = paddle.flip(pe_positive, [0]).unsqueeze(0)
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pe_negative = pe_negative[1:].unsqueeze(0)
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pe = paddle.concat([pe_positive, pe_negative], axis=1)
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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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Args:
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x (Tensor):Input tensor (batch, time, `*`).
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Returns:
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Tensor: Encoded tensor (batch, time, `*`).
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"""
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self.extend_pe(x)
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x = x * self.xscale
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T = paddle.shape(x)[1]
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pe_size = paddle.shape(self.pe)
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pos_emb = self.pe[:, pe_size[1] // 2 - T + 1:pe_size[1] // 2 + T, ]
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return self.dropout(x), self.dropout(pos_emb)
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class LegacyRelPositionalEncoding(PositionalEncoding):
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"""Relative positional encoding module (old version).
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Details can be found in https://github.com/espnet/espnet/pull/2816.
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See : Appendix B in https://arxiv.org/abs/1901.02860
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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): Maximum input length.
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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 extend_pe(self, x):
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"""Reset the positional encodings."""
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if self.pe is not None:
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if paddle.shape(self.pe)[1] >= paddle.shape(x)[1]:
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return
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pe = paddle.zeros((paddle.shape(x)[1], self.d_model))
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if self.reverse:
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position = paddle.arange(
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paddle.shape(x)[1] - 1, -1, -1.0,
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dtype=paddle.float32).unsqueeze(1)
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else:
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position = paddle.arange(
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0, paddle.shape(x)[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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"""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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self.extend_pe(x)
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x = x * self.xscale
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pos_emb = self.pe[:, :paddle.shape(x)[1]]
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return self.dropout(x), self.dropout(pos_emb)
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