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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from typing import Optional, Tuple
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from Model.multi_head_Attention import MultiHeadAttention
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class RecurrentBlock(nn.Module):
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def __init__(self, d_model: int, num_heads: int, dropout: float = 0.1):
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super().__init__()
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self.attention = MultiHeadAttention(d_model, num_heads, dropout)
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self.norm1, self.norm2 = nn.LayerNorm(d_model), nn.LayerNorm(d_model)
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self.feed_forward = nn.Sequential(
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nn.Linear(d_model, 4 * d_model),
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nn.GELU(),
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nn.Linear(4 * d_model, d_model),
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nn.Dropout(dropout)
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)
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self.state_proj = nn.Linear(d_model, d_model)
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def forward(self, x: torch.Tensor, recurrent_state: torch.Tensor, mask: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor, torch.Tensor]:
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recurrent_state = self.state_proj(recurrent_state)
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x = x + recurrent_state
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attended = self.attention(self.norm1(x), mask)
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return x + attended + self.feed_forward(self.norm2(x)), x |
