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transformer.py
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transformer.py
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import math
import torch
import torch.nn as nn
class TransformerModel(nn.Module):
def __init__(self, ntoken, ninp, nhid, nhead=2, nlayers=2, dropout=0.5):
super(TransformerModel, self).__init__()
from torch.nn import TransformerEncoder, TransformerEncoderLayer
self.model_type = "Transformer"
self.src_mask = None
self.pos_encoder = PositionalEncoding(ninp, dropout)
encoder_layers = TransformerEncoderLayer(ninp, nhead, nhid, dropout)
self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers)
# self.encoder = nn.Embedding(ntoken, ninp)
self.ninp = ninp
self.decoder = nn.Linear(ninp, ntoken)
self.init_weights()
@staticmethod
def _generate_square_subsequent_mask(sz):
mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
mask = (
mask.float()
.masked_fill(mask == 0, float("-inf"))
.masked_fill(mask == 1, float(0.0))
)
return mask
def init_weights(self):
initrange = 0.1
# self.encoder.weight.data.uniform_(-initrange, initrange)
self.decoder.bias.data.zero_()
self.decoder.weight.data.uniform_(-initrange, initrange)
def forward(self, src):
# if self.src_mask is None or self.src_mask.size(0) != len(src):
# device = src.device
# mask = self._generate_square_subsequent_mask(len(src)).to(device)
# self.src_mask = mask
# src = self.encoder(src) * math.sqrt(self.ninp)
src = src * math.sqrt(self.ninp)
src = self.pos_encoder(src)
output = self.transformer_encoder(src, self.src_mask)
output = self.decoder(output)
return output
class PositionalEncoding(nn.Module):
def __init__(self, d_model, dropout=0.1, max_len=5000):
super(PositionalEncoding, 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)