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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.prelude_Block import PreludeBlock
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from Model.recurrent_Block import RecurrentBlock
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from Model.codaBlock import CodaBlock
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class LatentRecurrentDepthLM(nn.Module):
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def __init__(self, vocab_size: int, d_model: int, num_heads: int, dropout: float = 0.1):
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super().__init__()
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self.prelude = PreludeBlock(vocab_size, d_model, num_heads, dropout)
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self.recurrent = RecurrentBlock(d_model, num_heads, dropout)
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self.coda = CodaBlock(d_model, vocab_size)
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def forward(self, x: torch.Tensor, num_iterations: int, mask: Optional[torch.Tensor] = None) -> torch.Tensor:
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hidden = self.prelude(x, mask)
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recurrent_state = torch.zeros_like(hidden)
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for _ in range(num_iterations):
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hidden, recurrent_state = self.recurrent(hidden, recurrent_state, mask)
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return self.coda(hidden) |
