espnet_positional_embedding.py

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)