| |
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
| from math import log |
|
|
| class PositionalEncoding(nn.Module): |
| |
| """Sinusoidal positional encoding module for sequence and transformer models.""" |
| def __init__(self, d_emb, dropout=0.2, max_len=5000): |
| """Initialize the sinusoidal positional encoding table. |
| |
| Args: |
| d_emb: Embedding dimension. |
| dropout: Dropout probability applied after adding positional encodings. |
| max_len: Maximum sequence length precomputed in the lookup buffer. |
| """ |
| super(PositionalEncoding, self).__init__() |
| self.dropout = nn.Dropout(p=dropout) |
| pe = torch.zeros(max_len, d_emb) |
| position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1) |
| div_term = torch.exp(torch.arange(0, d_emb, 2, dtype=torch.float) * (-log(10000.0) / d_emb)) |
| pe[:, 0::2] = torch.sin(position * div_term) |
| pe[:, 1::2] = torch.cos(position * div_term) |
| pe = pe.unsqueeze(0) |
| self.register_buffer('pe', pe) |
|
|
| def forward(self, x): |
| """Add sinusoidal positional encodings to input tensor ``x``. |
| |
| Args: |
| x: Input sequence tensor of shape ``[seq_len, batch_size, d_emb]``. |
| |
| Returns: |
| torch.Tensor: Position-encoded tensor of the same shape. |
| """ |
| x = x + self.pe[:, :x.shape[1], :] |
| return self.dropout(x) |
|
|
