| """
|
| Various positional encodings for the transformer.
|
| """
|
| import math
|
| import torch
|
| from torch import nn
|
|
|
| def PE1d_sincos(seq_length, dim):
|
| """
|
| :param d_model: dimension of the model
|
| :param length: length of positions
|
| :return: length*d_model position matrix
|
| """
|
| if dim % 2 != 0:
|
| raise ValueError("Cannot use sin/cos positional encoding with "
|
| "odd dim (got dim={:d})".format(dim))
|
| pe = torch.zeros(seq_length, dim)
|
| position = torch.arange(0, seq_length).unsqueeze(1)
|
| div_term = torch.exp((torch.arange(0, dim, 2, dtype=torch.float) *
|
| -(math.log(10000.0) / dim)))
|
| pe[:, 0::2] = torch.sin(position.float() * div_term)
|
| pe[:, 1::2] = torch.cos(position.float() * div_term)
|
|
|
| return pe.unsqueeze(1)
|
|
|
|
|
| class PositionEmbedding(nn.Module):
|
| """
|
| Absolute pos embedding (standard), learned.
|
| """
|
| def __init__(self, seq_length, dim, dropout, grad=False):
|
| super().__init__()
|
| self.embed = nn.Parameter(data=PE1d_sincos(seq_length, dim), requires_grad=grad)
|
| self.dropout = nn.Dropout(p=dropout)
|
|
|
| def forward(self, x):
|
|
|
| l = x.shape[1]
|
| x = x.permute(1, 0, 2) + self.embed[:l].expand(x.permute(1, 0, 2).shape)
|
| x = self.dropout(x.permute(1, 0, 2))
|
| return x
|
|
|
| |
