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0efcf9c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | # positional.py
import torch
import torch.nn as nn
from math import log
class PositionalEncoding(nn.Module):
def __init__(self, d_model: int, dropout: float = 0.1, max_len: int = 500):
super().__init__()
self.dropout = nn.Dropout(p=dropout)
position = torch.arange(max_len, dtype=torch.float).unsqueeze(1) # (max_len, 1)
div_term = torch.exp(
torch.arange(0, d_model, 2, dtype=torch.float) * (-log(10000.0) / d_model)
) # (d_model/2,)
pe = torch.zeros(max_len, d_model, dtype=torch.float) # (max_len, d_model)
pe[:, 0::2] = torch.sin(position * div_term) # (max_len, d_model/2)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0)
self.register_buffer("pe", pe, persistent=False) # buffer, pas paramètre
def forward(self, x: torch.Tensor) -> torch.Tensor:
# x: (B, S, D)
s = x.size(1)
x = x + self.pe[:, :s, :] # (1,S,D) broadcast -> (B,S,D)
return self.dropout(x) |