| import torch |
| from torch import nn, Tensor |
| import math |
|
|
|
|
| class PositionalEmbedding(nn.Module): |
| def __init__(self, seq_len: int = 32, d_model: int = 96): |
| super().__init__() |
| self.pos_emb = nn.Embedding(seq_len + 1, d_model) |
|
|
| def forward(self, inputs): |
| positions = ( |
| torch.arange(inputs.size(0), device=inputs.device) |
| .expand(inputs.size(1), inputs.size(0)) |
| .contiguous() |
| + 1 |
| ) |
| outputs = inputs + self.pos_emb(positions).permute(1, 0, 2) |
| return outputs |
|
|
|
|
| class PositionalEncoding(nn.Module): |
| def __init__(self, d_model: int, dropout: float = 0.1, max_len: int = 5000): |
| super().__init__() |
| self.dropout = nn.Dropout(p=dropout) |
|
|
| position = torch.arange(max_len).unsqueeze(1) |
| div_term = torch.exp( |
| torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model) |
| ) |
| pe = torch.zeros(max_len, 1, d_model) |
| pe[:, 0, 0::2] = torch.sin(position * div_term) |
| pe[:, 0, 1::2] = torch.cos(position * div_term) |
| self.register_buffer("pe", pe) |
|
|
| def forward(self, x: Tensor) -> Tensor: |
| """ |
| Args: |
| x: Tensor, shape [seq_len, batch_size, embedding_dim] |
| """ |
| x = x + self.pe[: x.size(0)] |
| return self.dropout(x) |
|
|
