File size: 1,461 Bytes
134df9b | 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 28 29 30 31 32 33 34 35 | import torch
import torch.nn as nn
class PositionEncoding(nn.Module):
def __init__(self, d_model: int = 2, max_len: int = 6) -> None:
super().__init__()
# ---------------------------------------------------------
# Precompute sinusoidal positions once so token embeddings
# can be shifted cheaply during training and inference.
# ---------------------------------------------------------
pe = torch.zeros(max_len, d_model)
position = torch.arange(start=0, end=max_len, step=1).float().unsqueeze(1)
embedding_index = torch.arange(start=0, end=d_model, step=2).float()
div_term = 1 / torch.tensor(10000.0) ** (embedding_index / d_model)
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
self.register_buffer("pe", pe)
def forward(self, word_embeddings: torch.Tensor, position_offset: int = 0) -> torch.Tensor:
# ---------------------------------------------------------
# Add positions for the visible slice, starting at the cache
# length when incremental inference supplies an offset.
# ---------------------------------------------------------
seq_len = word_embeddings.size(1)
position_end = position_offset + seq_len
return word_embeddings + self.pe[position_offset:position_end, :].unsqueeze(0)
if __name__ == "__main__":
n = PositionEncoding()
|