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8ce1de8 | 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 36 37 38 39 40 41 42 | """Positional information: sinusoidal (required) and learned (optional ablation)."""
from __future__ import annotations
import math
import torch
import torch.nn as nn
def sinusoidal_table(max_len: int, d_model: int) -> torch.Tensor:
"""PE[pos, 2i] = sin(pos / 10000^(2i/d)), PE[pos, 2i+1] = cos(pos / 10000^(2i/d)). Shape (max_len, d_model)."""
pe = torch.zeros(max_len, d_model)
position = torch.arange(0, max_len, dtype=torch.float32).unsqueeze(1) # (L, 1)
div_term = torch.exp(torch.arange(0, d_model, 2, dtype=torch.float32) * (-math.log(10000.0) / d_model))
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term[: d_model // 2])
return pe
class SinusoidalPositionalEncoding(nn.Module):
"""Fixed (non-learned) encoding, stored as a buffer so it moves with .to(device) but has no parameters."""
def __init__(self, d_model: int, max_len: int = 1024):
super().__init__()
self.register_buffer("pe", sinusoidal_table(max_len, d_model), persistent=False)
def forward(self, x: torch.Tensor) -> torch.Tensor: # x: (B, T, D)
return x + self.pe[: x.size(1)].unsqueeze(0)
class LearnedPositionalEmbedding(nn.Module):
"""One trainable vector per position (optional experiment)."""
def __init__(self, d_model: int, max_len: int = 1024):
super().__init__()
self.emb = nn.Embedding(max_len, d_model)
nn.init.normal_(self.emb.weight, std=d_model ** -0.5)
def forward(self, x: torch.Tensor) -> torch.Tensor:
pos = torch.arange(x.size(1), device=x.device)
return x + self.emb(pos).unsqueeze(0)
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