Apply my suggestions

lit_gpt/lora.py

@@ -263,18 +263,18 @@ def __init__(
             # Compute the indices
             # Indices are needed to properly pad weight updates with zeros in `zero_pad` method.
             q_per_kv = self.n_head // self.n_query_groups
-            query_group_size = q_per_kv + 2
-            head_dim = out_features // (self.n_query_groups * query_group_size)
+            total_qkv = q_per_kv + 2
+            head_size = out_features // (self.n_query_groups * total_qkv)
             ind = range(out_features)
             self.lora_ind = []
             if enable_q:
-                q_ind = list(filter(lambda x: (x // head_dim) % query_group_size < query_group_size - 2, ind))
+                q_ind = [x for x in ind if (x // head_size) % total_qkv < total_qkv - 2]
                 self.lora_ind.extend(q_ind)
             if enable_k:
-                k_ind = list(filter(lambda x: (x // head_dim) % query_group_size == query_group_size - 2, ind))
+                k_ind = [x for x in ind if (x // head_size) % total_qkv == total_qkv - 2]
                 self.lora_ind.extend(k_ind)
             if enable_v:
-                v_ind = list(filter(lambda x: (x // head_dim) % query_group_size == query_group_size - 1, ind))
+                v_ind = [x for x in ind if (x // head_size) % total_qkv == total_qkv - 1]
                 self.lora_ind.extend(v_ind)
             self.reset_parameters()
 
@@ -298,7 +298,7 @@ def zero_pad(self, x: torch.Tensor) -> torch.Tensor:
 
         [Q, Q, Q, Q, K, V, Q, Q, Q, Q, K, V, Q, Q, Q, Q, K, V],
 
-        where each Q, K, and V has size head_dim.
+        where each Q, K, and V has size head_size.
 
         In this case, the previously-described weight update applies separately to each
         individual block, so the update will take the form