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| import torch |
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| class PositionalEncoding(torch.nn.Module): |
| """ |
| Sinusoidal positional encoding module for Transformer models. |
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| This module injects information about the relative or absolute position of |
| tokens in a sequence by adding fixed sinusoidal embeddings to the input |
| embeddings. The positional encodings are non-learnable and follow the |
| formulation introduced in the original Transformer architecture. |
| """ |
| def __init__(self, emb_dim: int, max_len: int = 5000, **kwargs): |
| """ |
| Initialize the positional encoding module. |
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| Parameters |
| ---------- |
| emb_dim : int |
| Dimensionality of the embedding space. |
| max_len : int, optional |
| Maximum supported sequence length for which positional encodings |
| are precomputed. |
| """ |
| super().__init__(**kwargs) |
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| pe = torch.zeros(max_len, emb_dim) |
| position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1) |
| div_term = torch.exp(torch.arange(0, emb_dim, 2).float() * -(torch.log(torch.tensor(10000.0)) / emb_dim)) |
| pe[:, 0::2] = torch.sin(position * div_term) |
| pe[:, 1::2] = torch.cos(position * div_term) |
| pe = pe.unsqueeze(0) |
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| self.register_buffer('positional_encoding', pe) |
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| def forward(self, x: torch.Tensor) -> torch.Tensor: |
| """ |
| Add positional encodings to the input embeddings. |
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| Parameters |
| ---------- |
| x : torch.Tensor |
| Input tensor of shape (batch_size, sequence_length, emb_dim). |
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| Returns |
| ------- |
| torch.Tensor |
| Tensor of the same shape as the input with positional encodings added. |
| """ |
| return x + self.positional_encoding[:, :x.size(-2), :] |
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