sinusoid position embedding in pytorch

position_embedding.py

class PositionalEncoding(nn.Module):

    def __init__(self, d_model: int, dropout: float = 0.1, max_len: int = 5000):
        super().__init__()
        self.dropout = nn.Dropout(p=dropout)

        position = torch.arange(max_len).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model))
        pe = torch.zeros(max_len, 1, d_model)
        pe[:, 0, 0::2] = torch.sin(position * div_term)
        pe[:, 0, 1::2] = torch.cos(position * div_term)
        self.register_buffer('pe', pe)

    def forward(self, x):
        """
        Args:
            x: Tensor, shape [batch_size, seq_len, embedding_dim]
            x: Tensor, shape [seq_len, batch_size, embedding_dim]
        """
        temp = torch.permute(x, (1, 0, 2))
        temp = self.pe[:temp.size(0)]
        temp = torch.permute(temp, (1, 0, 2))
        temp = temp.repeat(x.shape[0], 1, 1)
        #temp = torch.permute(x, (1, 0, 2))
        return self.dropout(temp)