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import torch
from torch import nn, Tensor
import math
class PositionalEmbedding(nn.Module):
def __init__(self, seq_len: int = 32, d_model: int = 96):
super().__init__()
self.pos_emb = nn.Embedding(seq_len + 1, d_model)
def forward(self, inputs):
positions = (
torch.arange(inputs.size(0), device=inputs.device)
.expand(inputs.size(1), inputs.size(0))
.contiguous()
+ 1
)
outputs = inputs + self.pos_emb(positions).permute(1, 0, 2)
return outputs
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: Tensor) -> Tensor:
"""
Args:
x: Tensor, shape [seq_len, batch_size, embedding_dim]
"""
x = x + self.pe[: x.size(0)]
return self.dropout(x)
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