Source code for fairseq.modules.positional_encoding

# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

import torch.nn as nn
import math
import torch


class PositionalEncoding(nn.Module):
    """Positional encoding.

    Args:
        d_model: Embedding dimension.
        dropout_rate: Dropout rate.
        max_len: Maximum input length.
        reverse: 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 = 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 B X T X C
        Returns:
            torch.Tensor: Encoded tensor B X T X C
        """
        self.extend_pe(x)
        x = x * self.xscale + self.pe[:, : x.size(1)]
        return self.dropout(x)


[docs]class RelPositionalEncoding(nn.Module): """Relative positional encoding module (new implementation). Args: d_model: Embedding dimension. dropout_rate: Dropout rate. max_len: Maximum input length. """ def __init__(self, max_len, d_model): """Construct an PositionalEncoding object.""" super(RelPositionalEncoding, self).__init__() self.d_model = d_model self.pe = None self.extend_pe(torch.tensor(0.0).expand(1, max_len))
[docs] def extend_pe(self, x): """Reset the positional encodings.""" if self.pe is not None: # self.pe contains both positive and negative parts # the length of self.pe is 2 * input_len - 1 if self.pe.size(1) >= x.size(1) * 2 - 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 # Suppose `i` means to the position of query vecotr and `j` means the # position of key vector. We use position relative positions when keys # are to the left (i>j) and negative relative positions otherwise (i<j). pe_positive = torch.zeros(x.size(1), self.d_model) pe_negative = torch.zeros(x.size(1), self.d_model) 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_positive[:, 0::2] = torch.sin(position * div_term) pe_positive[:, 1::2] = torch.cos(position * div_term) pe_negative[:, 0::2] = torch.sin(-1 * position * div_term) pe_negative[:, 1::2] = torch.cos(-1 * position * div_term) # Reserve the order of positive indices and concat both positive and # negative indices. This is used to support the shifting trick # as in https://arxiv.org/abs/1901.02860 pe_positive = torch.flip(pe_positive, [0]).unsqueeze(0) pe_negative = pe_negative[1:].unsqueeze(0) pe = torch.cat([pe_positive, pe_negative], dim=1) self.pe = pe.to(device=x.device, dtype=x.dtype)
[docs] def forward(self, x: torch.Tensor): """Add positional encoding. Args: x : Input tensor T X B X C. Returns: torch.Tensor: Encoded tensor T X B X C. """ x = x.transpose(0, 1) # Change TBC to BTC self.extend_pe(x) pos_emb = self.pe[ :, self.pe.size(1) // 2 - x.size(1) + 1 : self.pe.size(1) // 2 + x.size(1), ] pos_emb = pos_emb.transpose(0, 1) # change to TBC return pos_emb