| 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) | |