diff --git a/wit/meaning_dataset.py b/wit/meaning_dataset.py
index 7c7c3c1..b3c89fc 100644
--- a/wit/meaning_dataset.py
+++ b/wit/meaning_dataset.py
@@ -1,14 +1,11 @@
 import os
-import datasets
-import torch
-import math
-import random
+import torch, datasets
+import math, gc, time, random, copy
 from itertools import chain
 from typing import Dict, Tuple
 from torch.utils.data import ConcatDataset, DataLoader, Dataset, random_split
 import numpy as np
 from torch.utils.data import BatchSampler
-import copy
 
 
 class MeaningMap:
@@ -25,6 +22,7 @@ class MeaningMap:
         file += "_" + str(max_subitem) + "_" + str(min_subitem)
         file = path + file + ".npz"
 
+        start_time = time.time()
         if not os.path.exists(path):
             os.mkdir(path)
         if os.path.exists(file) and use_cache:
@@ -41,7 +39,7 @@ class MeaningMap:
             self.ms_rank_all = dlra[:, 3].astype(np.uint32)
             self.ms_height = slhwm[:, 2]
             self.ms_weight = slhwm[:, 3]
-            print("Load end")
+            print("Load end, elapsed:" + str(time.time() - start_time) + "s")
         else:
             print("Disk cache miss, build new one.")
 
@@ -75,10 +73,10 @@ class MeaningMap:
             ms_weight = []  # meaning tree weight
             index = 0
             for i in range(self.vocab_size):
-                ms_data.append(np.array([i]))
-                ms_level.append(np.array([0]))
-                ms_rank_idx.append(np.array([0]))
-                ms_rank_all.append(np.array([0]))
+                ms_data.append(np.array([i], dtype=np.int32))
+                ms_level.append(np.array([0], dtype=np.int32))
+                ms_rank_idx.append(np.array([0], dtype=np.uint32))
+                ms_rank_all.append(np.array([0], dtype=np.uint32))
                 ms_start.append(index)
                 ms_len.append(1)
                 ms_height.append(0)
@@ -100,13 +98,13 @@ class MeaningMap:
                         ([0xFFFFFFF0 + i] if newm < self.vocab_size else ms_rank_idx[newm] * 16 + i)
                         for i, newm in enumerate(m_list)
                     ]
-                )
+                ).astype(np.uint32)
                 mrl = np.concatenate(
                     [
                         ([0xFFFFFFF0 + m_len] if newm < self.vocab_size else ms_rank_all[newm] * 16 + m_len)
                         for i, newm in enumerate(m_list)
                     ]
-                )
+                ).astype(np.uint32)
                 ms_data.append(ma)
                 ms_level.append(ml)
                 ms_rank_idx.append(mr)
@@ -117,6 +115,7 @@ class MeaningMap:
                 ms_weight.append(sum(ms_weight[sub_m] for sub_m in m_list))
                 index = index + len(ma)
 
+            print("Mapping end, elapsed:" + str(time.time() - start_time) + "s")
             ms_data = np.array(list(chain(*ms_data))).astype(np.int32)
             ms_level = np.array(list(chain(*ms_level))).astype(np.int32)
             ms_rank_idx = np.array(list(chain(*ms_rank_idx))).astype(np.uint32)
@@ -175,7 +174,7 @@ class MeaningMap:
             self.ms_len = ms_len
             self.ms_height = ms_height
             self.ms_weight = ms_weight
-            print("Disk cache build end.")
+            print("Disk cache build end, elapsed:" + str(time.time() - start_time) + "s")
 
     def get_sequence(self, meaning):  # return sequence[meaning]
         start = self.ms_start[meaning]