Update Model/prelude_Block.py

Model/prelude_Block.py

@@ -1,28 +1,28 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from typing import Optional, Tuple
-from Model.multi_head_Attention import MultiHeadAttention
-
-
-# Prelude Block (Initial Processing)
-class PreludeBlock(nn.Module):
-    def __init__(self, vocab_size: int, d_model: int, num_heads: int, dropout: float = 0.1):
-        super().__init__()
-        self.token_embedding = nn.Embedding(vocab_size, d_model)
-        self.pos_encoding = nn.Parameter(torch.zeros(1, 1024, d_model))
-        self.attention = MultiHeadAttention(d_model, num_heads, dropout)
-        self.norm1, self.norm2 = nn.LayerNorm(d_model), nn.LayerNorm(d_model)
-        self.feed_forward = nn.Sequential(
-            nn.Linear(d_model, 4 * d_model),
-            nn.GELU(),
-            nn.Linear(4 * d_model, d_model),
-            nn.Dropout(dropout)
-        )
-
-    def forward(self, x: torch.Tensor, mask: Optional[torch.Tensor] = None) -> torch.Tensor:
-        seq_len = x.size(1)
-        x = self.token_embedding(x) + self.pos_encoding[:, :seq_len, :]
-        attended = self.attention(self.norm1(x), mask)
-        x = x + attended
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import Optional, Tuple
+from multi_head_Attention import MultiHeadAttention
+
+
+# Prelude Block (Initial Processing)
+class PreludeBlock(nn.Module):
+    def __init__(self, vocab_size: int, d_model: int, num_heads: int, dropout: float = 0.1):
+        super().__init__()
+        self.token_embedding = nn.Embedding(vocab_size, d_model)
+        self.pos_encoding = nn.Parameter(torch.zeros(1, 1024, d_model))
+        self.attention = MultiHeadAttention(d_model, num_heads, dropout)
+        self.norm1, self.norm2 = nn.LayerNorm(d_model), nn.LayerNorm(d_model)
+        self.feed_forward = nn.Sequential(
+            nn.Linear(d_model, 4 * d_model),
+            nn.GELU(),
+            nn.Linear(4 * d_model, d_model),
+            nn.Dropout(dropout)
+        )
+
+    def forward(self, x: torch.Tensor, mask: Optional[torch.Tensor] = None) -> torch.Tensor:
+        seq_len = x.size(1)
+        x = self.token_embedding(x) + self.pos_encoding[:, :seq_len, :]
+        attended = self.attention(self.norm1(x), mask)
+        x = x + attended
         return x + self.feed_forward(self.norm2(x))