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File size: 1,179 Bytes
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import torch
import torch.nn as nn
#实现了位置编码
class FixedPositionalEncoding(nn.Module):
def __init__(self, embedding_dim, max_length=512):
super(FixedPositionalEncoding, self).__init__()
pe = torch.zeros(max_length, embedding_dim)
position = torch.arange(0, max_length, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(
torch.arange(0, embedding_dim, 2).float()
* (-torch.log(torch.tensor(10000.0)) / embedding_dim)
)
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0).transpose(0, 1)
self.register_buffer('pe', pe)
def forward(self, x):
x = x + self.pe[: x.size(0), :]
return x
class LearnedPositionalEncoding(nn.Module):
def __init__(self, max_position_embeddings, embedding_dim, seq_length):
super(LearnedPositionalEncoding, self).__init__()
self.position_embeddings = nn.Parameter(torch.zeros(1, 256, 512)) #8x
def forward(self, x, position_ids=None):
position_embeddings = self.position_embeddings
return x + position_embeddings
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