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)