| import math |
| import torch |
|
|
|
|
| class PositionalEncoding(torch.nn.Module): |
| """Positional encoding. |
| Args: |
| d_model (int): Embedding dimension. |
| dropout_rate (float): Dropout rate. |
| max_len (int): Maximum input length. |
| reverse (bool): Whether to reverse the input position. |
| """ |
|
|
| def __init__(self, d_model, dropout_rate, max_len=5000, reverse=False): |
| """Construct an PositionalEncoding object.""" |
| super(PositionalEncoding, self).__init__() |
| self.d_model = d_model |
| self.reverse = reverse |
| self.xscale = math.sqrt(self.d_model) |
| self.dropout = torch.nn.Dropout(p=dropout_rate) |
| self.pe = None |
| self.extend_pe(torch.tensor(0.0).expand(1, max_len)) |
|
|
| def extend_pe(self, x): |
| """Reset the positional encodings.""" |
| if self.pe is not None: |
| if self.pe.size(1) >= x.size(1): |
| if self.pe.dtype != x.dtype or self.pe.device != x.device: |
| self.pe = self.pe.to(dtype=x.dtype, device=x.device) |
| return |
| pe = torch.zeros(x.size(1), self.d_model) |
| if self.reverse: |
| position = torch.arange( |
| x.size(1) - 1, -1, -1.0, dtype=torch.float32 |
| ).unsqueeze(1) |
| else: |
| position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1) |
| div_term = torch.exp( |
| torch.arange(0, self.d_model, 2, dtype=torch.float32) |
| * -(math.log(10000.0) / self.d_model) |
| ) |
| pe[:, 0::2] = torch.sin(position * div_term) |
| pe[:, 1::2] = torch.cos(position * div_term) |
| pe = pe.unsqueeze(0) |
| self.pe = pe.to(device=x.device, dtype=x.dtype) |
|
|
| def forward(self, x: torch.Tensor): |
| """Add positional encoding. |
| Args: |
| x (torch.Tensor): Input tensor (batch, time, `*`). |
| Returns: |
| torch.Tensor: Encoded tensor (batch, time, `*`). |
| """ |
| self.extend_pe(x) |
| x = x * self.xscale + self.pe[:, : x.size(1)] |
| return self.dropout(x) |
|
|
|
|
| class ScaledPositionalEncoding(PositionalEncoding): |
| """Scaled positional encoding module. |
| See Sec. 3.2 https://arxiv.org/abs/1809.08895 |
| Args: |
| d_model (int): Embedding dimension. |
| dropout_rate (float): Dropout rate. |
| max_len (int): Maximum input length. |
| """ |
|
|
| def __init__(self, d_model, dropout_rate, max_len=5000): |
| """Initialize class.""" |
| super().__init__(d_model=d_model, dropout_rate=dropout_rate, max_len=max_len) |
| self.alpha = torch.nn.Parameter(torch.tensor(1.0)) |
|
|
| def reset_parameters(self): |
| """Reset parameters.""" |
| self.alpha.data = torch.tensor(1.0) |
|
|
| def forward(self, x): |
| """Add positional encoding. |
| Args: |
| x (torch.Tensor): Input tensor (batch, time, `*`). |
| Returns: |
| torch.Tensor: Encoded tensor (batch, time, `*`). |
| """ |
| self.extend_pe(x) |
| x = x + self.alpha * self.pe[:, : x.size(1)] |
| return self.dropout(x) |
|
|
|
|
| class RelPositionalEncoding(PositionalEncoding): |
| """Relative positional encoding module. |
| See : Appendix B in https://arxiv.org/abs/1901.02860 |
| Args: |
| d_model (int): Embedding dimension. |
| dropout_rate (float): Dropout rate. |
| max_len (int): Maximum input length. |
| """ |
|
|
| def __init__(self, d_model, dropout_rate, max_len=5000): |
| """Initialize class.""" |
| super().__init__(d_model, dropout_rate, max_len, reverse=True) |
|
|
| def forward(self, x): |
| """Compute positional encoding. |
| Args: |
| x (torch.Tensor): Input tensor (batch, time, `*`). |
| Returns: |
| torch.Tensor: Encoded tensor (batch, time, `*`). |
| torch.Tensor: Positional embedding tensor (1, time, `*`). |
| """ |
| self.extend_pe(x) |
| x = x * self.xscale |
| pos_emb = self.pe[:, : x.size(1)] |
| return self.dropout(x) + self.dropout(pos_emb) |
