| import torch |
| import torch.nn as nn |
|
|
|
|
| class FixedPositionalEncoding(nn.Module): |
| def __init__(self, embedding_dim, max_length=5000): |
| super(FixedPositionalEncoding, self).__init__() |
|
|
| pe = torch.zeros(max_length, embedding_dim) |
| position = torch.arange(0, max_length, dtype=torch.float).unsqueeze(1) |
| div_term = torch.exp( |
| torch.arange(0, embedding_dim, 2).float() |
| * (-torch.log(torch.tensor(10000.0)) / embedding_dim) |
| ) |
| pe[:, 0::2] = torch.sin(position * div_term) |
| pe[:, 1::2] = torch.cos(position * div_term) |
| pe = pe.unsqueeze(0).transpose(0, 1) |
| self.register_buffer('pe', pe) |
|
|
| def forward(self, x): |
| x = x + self.pe[: x.size(0), :] |
| return x |
|
|
|
|
| class LearnedPositionalEncoding(nn.Module): |
| def __init__(self, max_position_embeddings, embedding_dim, seq_length): |
| super(LearnedPositionalEncoding, self).__init__() |
| self.pe = nn.Embedding(max_position_embeddings, embedding_dim) |
| self.seq_length = seq_length |
|
|
| self.register_buffer( |
| "position_ids", |
| torch.arange(max_position_embeddings).expand((1, -1)), |
| ) |
|
|
| def forward(self, x, position_ids=None): |
| if position_ids is None: |
| position_ids = self.position_ids[:, : self.seq_length] |
|
|
| position_embeddings = self.pe(position_ids) |
| return x + position_embeddings |
