transformer.py

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)
        self.d_model = d_model
        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 * math.sqrt(self.d_model)
        x = x + self.pe[:x.size(0), :]
        return self.dropout(x)

    
class MyTransformer(nn.Module):
    def __init__(self, d_model: int = 512, nhead: int = 8, num_encoder_layers: int = 6,
                 num_decoder_layers: int = 6, dim_feedforward: int = 2048, dropout: float = 0.1,
                 activation: str = "relu",source_vocab_length: int = 60000,target_vocab_length: int = 60000) -> None:
        super(MyTransformer, self).__init__()
        self.source_embedding = nn.Embedding(source_vocab_length, d_model)
        self.pos_encoder = PositionalEncoding(d_model)
        encoder_layer = nn.TransformerEncoderLayer(d_model, nhead, dim_feedforward, dropout, activation)
        encoder_norm = nn.LayerNorm(d_model)
        self.encoder = nn.TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm)
        self.target_embedding = nn.Embedding(target_vocab_length, d_model)
        decoder_layer = nn.TransformerDecoderLayer(d_model, nhead, dim_feedforward, dropout, activation)
        decoder_norm = nn.LayerNorm(d_model)
        self.decoder = nn.TransformerDecoder(decoder_layer, num_decoder_layers, decoder_norm)
        self.out = nn.Linear(512, target_vocab_length)
        self._reset_parameters()
        self.d_model = d_model
        self.nhead = nhead

    def forward(self, src: Tensor, tgt: Tensor, src_mask: Optional[Tensor] = None, tgt_mask: Optional[Tensor] = None,
                memory_mask: Optional[Tensor] = None, src_key_padding_mask: Optional[Tensor] = None,
                tgt_key_padding_mask: Optional[Tensor] = None, memory_key_padding_mask: Optional[Tensor] = None) -> Tensor:
        if src.size(1) != tgt.size(1):
            raise RuntimeError("the batch number of src and tgt must be equal")
        src = self.source_embedding(src)
        src = self.pos_encoder(src)
        memory = self.encoder(src, mask=src_mask, src_key_padding_mask=src_key_padding_mask)
        tgt = self.target_embedding(tgt)
        tgt = self.pos_encoder(tgt)
        output = self.decoder(tgt, memory, tgt_mask=tgt_mask, memory_mask=memory_mask,
                              tgt_key_padding_mask=tgt_key_padding_mask,
                              memory_key_padding_mask=memory_key_padding_mask)
        output = self.out(output)
        return output


    def _reset_parameters(self):
        r"""Initiate parameters in the transformer model."""
        for p in self.parameters():
            if p.dim() > 1:
                xavier_uniform_(p)