Divide the input into long heads

@@ -15,9 +15,31 @@ class MultiHeadAttentionLayer(nn.Module):
         self.k_linear = nn.Linear(embed_size, embed_size)
         self.k_linear = nn.Linear(embed_size, embed_size)
         self.v_linear = nn.Linear(embed_size, embed_size)
         self.v_linear = nn.Linear(embed_size, embed_size)
 
 
+    def forward(self, values, keys, query):
+        N = query.shape[0]  # batch_size
+        
+        # Linear transformations for Q, K, V
+        Q = self.q_linear(query)  # shape: (N, seq_len, embed_size)
+        K = self.k_linear(keys)   # shape: (N, seq_len, embed_size)
+        V = self.v_linear(values) # shape: (N, seq_len, embed_size)
+        
+        # Reshape Q, K, V into multiple heads
+        Q = Q.reshape(N, -1, self.num_heads, self.head_dim)
+        K = K.reshape(N, -1, self.num_heads, self.head_dim)
+        V = V.reshape(N, -1, self.num_heads, self.head_dim)
+        
+        return Q, K, V
+
 
 
 if __name__ == "__main__":
 if __name__ == "__main__":
     embed_size = 512
     embed_size = 512
     num_heads = 8
     num_heads = 8
     mha_layer = MultiHeadAttentionLayer(embed_size, num_heads)
     mha_layer = MultiHeadAttentionLayer(embed_size, num_heads)
-    print("Linear layers for Q, K, V initialized.")
+    
+    # Dummy data
+    values = torch.randn(2, 10, embed_size)
+    keys = torch.randn(2, 10, embed_size)
+    query = torch.randn(2, 10, embed_size)
+    
+    Q, K, V = mha_layer(values, keys, query)
+    print(f"Q shape: {Q.shape}, K shape: {K.shape}, V shape: {V.shape}")