| """ |
| 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 |
|
|
| |
