diff --git a/fluid/PaddleNLP/language_model/lstm/_ce.py b/fluid/PaddleNLP/language_model/lstm/_ce.py
index f537f6aa62dd502c79174fadedda0da621c8eb7b..338d09b63468a982e47f740030bd5ca967d4d00e 100644
--- a/fluid/PaddleNLP/language_model/lstm/_ce.py
+++ b/fluid/PaddleNLP/language_model/lstm/_ce.py
@@ -35,9 +35,10 @@ def parse_log(log):
     for line in log.split('\n'):
         fs = line.strip().split('\t')
         print(fs)
-        kpi_name = fs[0]
-        kpi_value = float(fs[1])
-        yield kpi_name, kpi_value
+        if len(fs) == 3 and fs[0] == 'ptblm':
+            kpi_name = fs[1]
+            kpi_value = float(fs[2])
+            yield kpi_name, kpi_value
 
 
 def log_to_ce(log):
diff --git a/fluid/PaddleNLP/language_model/lstm/reader.py b/fluid/PaddleNLP/language_model/lstm/reader.py
index 50e8835ec8b96bf37a7b972700a588034d41425c..8c0551d84f253d9bfd8420e56185780bee7c3145 100644
--- a/fluid/PaddleNLP/language_model/lstm/reader.py
+++ b/fluid/PaddleNLP/language_model/lstm/reader.py
@@ -28,7 +28,10 @@ Py3 = sys.version_info[0] == 3
 def _read_words(filename):
     data = []
     with open(filename, "r") as f:
-        return f.read().decode("utf-8").replace("\n", "<eos>").split()
+        if Py3:
+            return f.read().replace("\n", "<eos>").split()
+        else:
+            return f.read().decode("utf-8").replace("\n", "<eos>").split()
 
 
 def _build_vocab(filename):
diff --git a/fluid/PaddleNLP/language_model/lstm/train.py b/fluid/PaddleNLP/language_model/lstm/train.py
index fc058c6a0e80f4aeba76656fe505207846d66e2f..42bab12bcbdafdabc5ab14370860b4b0ae269dda 100644
--- a/fluid/PaddleNLP/language_model/lstm/train.py
+++ b/fluid/PaddleNLP/language_model/lstm/train.py
@@ -258,7 +258,8 @@ def train():
                                  fetch_list=[
                                      loss.name, last_hidden.name,
                                      last_cell.name, 'learning_rate'
-                                 ])
+                                 ],
+                                 use_program_cache=True)
 
             cost_train = np.array(fetch_outs[0])
             init_hidden = np.array(fetch_outs[1])
@@ -282,8 +283,9 @@ def train():
         print("train ppl", ppl[0])
 
         if epoch_id == max_epoch - 1 and args.enable_ce:
-            print("lstm_language_model_duration\t%s" % (total_time / max_epoch))
-            print("lstm_language_model_loss\t%s" % ppl[0])
+            print("ptblm\tlstm_language_model_duration\t%s" %
+                  (total_time / max_epoch))
+            print("ptblm\tlstm_language_model_loss\t%s" % ppl[0])
 
         model_path = os.path.join("model_new/", str(epoch_id))
         if not os.path.isdir(model_path):