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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 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)) |
