import math
from typing import Literal
import torch
from torch import Tensor, nn
from .base import PositionalEncoding
[docs]
class SinusoidalPE(PositionalEncoding):
"""Standard sinusoidal positional encoding (Vaswani et al.)"""
@property
def injection_point(self) -> Literal['embedding']:
return 'embedding'
def __init__(self, d_model: int, max_len: int = 5000, dropout: float = 0.1):
super().__init__()
self.dropout = nn.Dropout(dropout)
pe = torch.zeros(max_len, d_model)
position = torch.arange(max_len).unsqueeze(1).float()
div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
self.register_buffer('pe', pe.unsqueeze(0)) # [1, max_len, d_model]
[docs]
def apply_to_embedding(self, x: Tensor) -> Tensor:
"""x: [batch, seq_len, d_model]"""
x = x + self.pe[:, : x.size(1)] # type: ignore
return self.dropout(x)
[docs]
class LearnedPE(PositionalEncoding):
"""Learned positional embeddings."""
@property
def injection_point(self) -> Literal['embedding']:
return 'embedding'
def __init__(self, d_model: int, max_len: int = 5000, dropout: float = 0.1):
super().__init__()
self.dropout = nn.Dropout(dropout)
self.pe = nn.Embedding(max_len, d_model)
[docs]
def apply_to_embedding(self, x: Tensor) -> Tensor:
positions = torch.arange(x.size(1), device=x.device)
x = x + self.pe(positions)
return self.dropout(x)