36 lines
1.1 KiB
Python
36 lines
1.1 KiB
Python
#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.02 #
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#####################################################
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import torch
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import torch.nn as nn
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import math
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class PositionalEncoder(nn.Module):
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# Attention Is All You Need: https://arxiv.org/pdf/1706.03762.pdf
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# https://github.com/pytorch/examples/blob/master/word_language_model/model.py#L65
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def __init__(self, d_model, max_seq_len, dropout=0.1):
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super(PositionalEncoder, self).__init__()
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self.d_model = d_model
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# create constant 'pe' matrix with values dependant on
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# pos and i
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pe = torch.zeros(max_seq_len, d_model)
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for pos in range(max_seq_len):
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for i in range(0, d_model):
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div = 10000 ** ((i // 2) * 2 / d_model)
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value = pos / div
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if i % 2 == 0:
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pe[pos, i] = math.sin(value)
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else:
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pe[pos, i] = math.cos(value)
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pe = pe.unsqueeze(0)
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self.dropout = nn.Dropout(p=dropout)
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self.register_buffer('pe', pe)
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def forward(self, x):
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batch, seq, fdim = x.shape[:3]
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embeddings = self.pe[:, :seq, :fdim]
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outs = self.dropout(x + embeddings)
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return outs
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