diff --git a/models/recall/word2vec/benchmark/static_model.py b/models/recall/word2vec/benchmark/static_model.py
index f8c4f4641..733e28d03 100644
--- a/models/recall/word2vec/benchmark/static_model.py
+++ b/models/recall/word2vec/benchmark/static_model.py
@@ -156,8 +156,7 @@ def net(self, inputs, is_infer=False):
 
         return self.metrics
 
-    def create_optimizer(self, strategy=None):
-        pure_bf16 = self.config.get("pure_bf16")
+    def create_optimizer(self, strategy=None, pure_bf16=False):
         lr = float(self.config.get("hyper_parameters.optimizer.learning_rate"))
         decay_rate = float(
             self.config.get("hyper_parameters.optimizer.decay_rate"))
diff --git a/tools/static_ps_trainer.py b/tools/static_ps_trainer.py
index c6298b2f1..d2612d91d 100644
--- a/tools/static_ps_trainer.py
+++ b/tools/static_ps_trainer.py
@@ -26,8 +26,6 @@
 import warnings
 import logging
 import ast
-import numpy as np
-import struct
 
 __dir__ = os.path.dirname(os.path.abspath(__file__))
 sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
@@ -62,10 +60,6 @@ def parse_args():
     return config
 
 
-def bf16_to_fp32(val):
-    return np.float32(struct.unpack('<f', struct.pack('<I', val << 16))[0])
-
-
 class Main(object):
     def __init__(self, config):
         self.metrics = {}
@@ -218,6 +212,7 @@ def dataloader_train_loop(self, epoch):
         batch_id = 0
         train_run_cost = 0.0
         total_examples = 0
+        from paddle.fluid.tests.unittests.op_test import convert_uint16_to_float
         self.reader.start()
         while True:
             try:
@@ -237,7 +232,7 @@ def dataloader_train_loop(self, epoch):
                         metrics_string += "{}: {}, ".format(
                             var_name, fetch_var[var_idx]
                             if var_name != "LOSS" or not config['pure_bf16']
-                            else bf16_to_fp32(fetch_var[var_idx][0]))
+                            else convert_uint16_to_float(fetch_var[var_idx]))
                     profiler_string = ""
                     profiler_string += "avg_batch_cost: {} sec, ".format(
                         format((train_run_cost) / print_step, '.5f'))