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import torch
import torch.nn as nn
class SinusoidPositionalEncoding(nn.Module):
def __init__(self, token_dim, max_len=5000):
super(SinusoidPositionalEncoding, self).__init__()
pe = torch.zeros(max_len, token_dim) # shape: (max_len, token_dim)
position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(
1
) # shape: (max_len, 1)
div_term = torch.exp(
torch.arange(0, token_dim, 2).float()
* (-torch.log(torch.tensor(10000.0)) / token_dim)
) # shape: (token_dim // 2)
pe[:, 0::2] = torch.sin(position * div_term) # shape: (max_len, token_dim // 2)
pe[:, 1::2] = torch.cos(position * div_term) # shape: (max_len, token_dim // 2)
pe = pe.unsqueeze(0) # shape: (1, max_len, token_dim)
self.register_buffer("pe", pe)
def forward(self, x):
x = x + self.pe[:, : x.size(1), :] # shape: (batch_size, seq_len, token_dim)
return x
class LearnedPositionalEncoding(nn.Module):
def __init__(self, token_dim, num_tokens):
super(LearnedPositionalEncoding, self).__init__()
self.pe = nn.Parameter(torch.randn(1, num_tokens, token_dim) * 0.02)
def forward(self, x):
x = x + self.pe
return x
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