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# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import math
class SinePositionalEmbedding(nn.Module):
def __init__(
self,
dim_model: int,
dropout: float = 0.0,
scale: bool = False,
alpha: bool = False,
):
super().__init__()
self.dim_model = dim_model
self.x_scale = math.sqrt(dim_model) if scale else 1.0
self.alpha = nn.Parameter(torch.ones(1), requires_grad=alpha)
self.dropout = torch.nn.Dropout(p=dropout)
self.reverse = False
self.pe = None
self.extend_pe(torch.tensor(0.0).expand(1, 4000))
def extend_pe(self, x):
"""Reset the positional encodings."""
if self.pe is not None:
if self.pe.size(1) >= x.size(1):
if self.pe.dtype != x.dtype or self.pe.device != x.device:
self.pe = self.pe.to(dtype=x.dtype, device=x.device)
return
pe = torch.zeros(x.size(1), self.dim_model)
if self.reverse:
position = torch.arange(
x.size(1) - 1, -1, -1.0, dtype=torch.float32
).unsqueeze(1)
else:
position = torch.arange(
0, x.size(1), dtype=torch.float32
).unsqueeze(1)
div_term = torch.exp(
torch.arange(0, self.dim_model, 2, dtype=torch.float32)
* -(math.log(10000.0) / self.dim_model)
)
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0)
self.pe = pe.to(device=x.device, dtype=x.dtype).detach()
def forward(self, x: torch.Tensor) -> torch.Tensor:
self.extend_pe(x)
output = x.unsqueeze(-1) if x.ndim == 2 else x
output = output * self.x_scale + self.alpha * self.pe[:, : x.size(1)]
return self.dropout(output)
# import torch
# import torch.nn as nn
# import math
# class SinePositionalEmbedding(nn.Module):
# def __init__(
# self,
# dim_model: int,
# dropout: float = 0.0,
# scale: bool = False,
# alpha: bool = False,
# ):
# super().__init__()
# self.dim_model = dim_model
# self.x_scale = math.sqrt(dim_model) if scale else 1.0
# self.alpha = nn.Parameter(torch.ones(1), requires_grad=alpha)
# self.dropout = torch.nn.Dropout(p=dropout)
# self.reverse = False
# self.pe = None
# self.extend_pe(torch.zeros(1, 4000))
# def extend_pe(self, x):
# """Reset the positional encodings."""
# if self._pe_needs_extension(x):
# self.pe = self._generate_positional_encodings(x)
# def _pe_needs_extension(self, x):
# return self.pe is None or self.pe.size(1) < x.size(1) or self.pe.dtype != x.dtype or self.pe.device != x.device
# def _generate_positional_encodings(self, x):
# pe = torch.zeros(x.size(1), self.dim_model)
# position = self._get_position_tensor(x)
# div_term = self._get_div_term()
# pe[:, 0::2] = torch.sin(position * div_term)
# pe[:, 1::2] = torch.cos(position * div_term)
# return pe.unsqueeze(0).to(device=x.device, dtype=x.dtype).detach()
# def _get_position_tensor(self, x):
# position = torch.arange(x.size(1), dtype=torch.float32).unsqueeze(1)
# return position.flip(0) if self.reverse else position
# def _get_div_term(self):
# return torch.exp(torch.arange(0, self.dim_model, 2, dtype=torch.float32) * -(math.log(10000.0) / self.dim_model))
# def forward(self, x: torch.Tensor) -> torch.Tensor:
# self.extend_pe(x)
# output = x.unsqueeze(-1) if x.ndim == 2 else x
# output = output * self.x_scale + self.alpha * self.pe[:, : x.size(1)]
# return self.dropout(output)
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