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Running
on
L4
File size: 1,414 Bytes
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import math
import torch
import torch.nn as nn
class PositionalEncodingSinCos(nn.Module):
def __init__(self, d_model, dropout=0.1, max_len=250):
super(PositionalEncodingSinCos, self).__init__()
self.dropout = nn.Dropout(p=dropout)
pe = torch.zeros(max_len, d_model)
position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
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 self.dropout(x)
class PositionalEncodingLUT(nn.Module):
def __init__(self, d_model, dropout=0.1, max_len=250):
super(PositionalEncodingLUT, self).__init__()
self.dropout = nn.Dropout(p=dropout)
position = torch.arange(0, max_len, dtype=torch.long).unsqueeze(1)
self.register_buffer('position', position)
self.pos_embed = nn.Embedding(max_len, d_model)
self._init_embeddings()
def _init_embeddings(self):
nn.init.kaiming_normal_(self.pos_embed.weight, mode="fan_in")
def forward(self, x):
pos = self.position[:x.size(0)]
x = x + self.pos_embed(pos)
return self.dropout(x)
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