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import math
import torch
import torch.nn as nn
class TransformerModel(nn.Module):
def __init__(self, vocab_size, d_model, nhead, num_encoder_layers, num_decoder_layers, dim_feedforward, dropout=0.1):
super(TransformerModel, self).__init__()
self.model_type = 'Transformer'
self.src_mask = None
self.pos_encoder = PositionalEncoding(d_model, dropout)
self.encoder = nn.Embedding(vocab_size, d_model)
self.transformer = nn.Transformer(d_model, nhead, num_encoder_layers, num_decoder_layers, dim_feedforward, dropout)
self.decoder = nn.Linear(d_model, vocab_size)
def forward(self, src, tgt, src_mask=None, tgt_mask=None):
src = self.encoder(src) * math.sqrt(self.d_model)
src = self.pos_encoder(src)
tgt = self.encoder(tgt) * math.sqrt(self.d_model)
tgt = self.pos_encoder(tgt)
output = self.transformer(src, tgt, src_mask, tgt_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)
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