| | """ |
| | 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 |
| |
|
| | |
