Divide the input into long heads

2024-10-06 11:54:32 +08:00
parent f5e307d3f8
commit 4500b2719e
1 changed files with 23 additions and 1 deletions
@@ -15,9 +15,31 @@ class MultiHeadAttentionLayer(nn.Module):
        self.k_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__":
    embed_size = 512
    num_heads = 8
    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}")