fix: define missing split_token in HierarchicalEmbedding

model/module.py

@@ -440,9 +440,24 @@ class HierarchicalEmbedding(nn.Module):
         nn.init.normal_(self.emb_s1.weight, mean=0, std=d_model ** -0.5)
         nn.init.normal_(self.emb_s2.weight, mean=0, std=d_model ** -0.5)
 
+    def split_token(self, token_ids: torch.Tensor, s2_bits: int):
+        """Inputs:
+            token_ids (torch.Tensor): Composite token IDs of shape [batch_size, seq_len] or [N], each in range [0, 2^(s1_bits + s2_bits) - 1].
+            s2_bits (int): Number of low bits used for the fine token (s2).
+        """
+        assert isinstance(s2_bits, int) and s2_bits > 0, "s2_bits must be a positive integer"
+
+        t = token_ids.long()
+        mask = (1 << s2_bits) - 1
+        s2_ids = t & mask           # extract low bits
+        s1_ids = t >> s2_bits       # extract high bits
+        return s1_ids, s2_ids
+
     def forward(self, token_ids):
         """Inputs:
-        token_ids: [batch_size, seq_len] token ID
+        token_ids:
+            - tuple or list: (s1_ids, s2_ids), each of shape [batch_size, seq_len], or
+            - torch.Tensor: composite token IDs of shape [batch_size, seq_len], which will be split into (s1_ids, s2_ids) internally.
         Output: [batch_size, seq_len, d_model]
         """
         if isinstance(token_ids, tuple) or isinstance(token_ids, list):