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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 PreludeBlock(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.token_embedding = nn.Embedding(vocab_size, d_model)
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self.pos_encoding = nn.Parameter(torch.zeros(1, 1024, d_model))
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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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def forward(self, x: torch.Tensor, mask: Optional[torch.Tensor] = None) -> torch.Tensor:
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seq_len = x.size(1)
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x = self.token_embedding(x) + self.pos_encoding[:, :seq_len, :]
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attended = self.attention(self.norm1(x), mask)
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x = x + attended
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return x + self.feed_forward(self.norm2(x)) |
