diff --git a/python/paddle/distributed/auto_parallel/constants.py b/python/paddle/distributed/auto_parallel/constants.py
index 800d36af3d02a..82b3d4554b76a 100644
--- a/python/paddle/distributed/auto_parallel/constants.py
+++ b/python/paddle/distributed/auto_parallel/constants.py
@@ -47,9 +47,7 @@ def set_field_default_config(category, field, default_value):
 set_field_default_config(BASE, "all_ranks", False)
 set_field_default_config(BASE, "split_data", False)
 set_field_default_config(BASE, "seed", None)
-set_field_default_config(
-    BASE, "reinit", False
-)  # Only for debug, and must be set with seed at the same time when the value is True.
+set_field_default_config(BASE, "reinit", False)  # Only for debug
 
 #########################################
 # recompute configuration
diff --git a/python/paddle/distributed/auto_parallel/engine.py b/python/paddle/distributed/auto_parallel/engine.py
index b280f4245f5b6..71dd54478c57d 100644
--- a/python/paddle/distributed/auto_parallel/engine.py
+++ b/python/paddle/distributed/auto_parallel/engine.py
@@ -283,9 +283,10 @@ def _build(self, mode):
         self._dist_contexts[mode].gradient_scale = self._strategy.gradient_scale
 
     def _optimization_tuning(self, mode, dataset, batch_size):
-        if not self._strategy.tuning.enable or mode != "train":
-            return
+        if not self._tuning.enable:
+            raise ValueError("Please set `tuning.enable=True`.")
 
+        assert mode == "train"
         # Do the build process
         self._build(mode)
         # Do the planning process
@@ -309,8 +310,6 @@ def _optimization_tuning(self, mode, dataset, batch_size):
             # update the strategy
             self._dist_contexts[
                 mode]._strategy = self._optimization_tuner.get_best_config()
-        else:
-            return
 
     def _plan(self, mode):
         if self._planned_mode is None:
@@ -620,6 +619,7 @@ def fit(self,
                 self.evaluate(valid_data, valid_sample_split, batch_size,
                               valid_steps, collate_fn, callbacks)
                 self._switch_mode("train")
+
             self._reset_metrics()
         return outputs
 
@@ -1134,7 +1134,10 @@ def _get_lr(self, optimizer):
         if isinstance(optimizer, paddle.optimizer.Optimizer):
             return optimizer.get_lr()
         elif isinstance(optimizer, paddle.fluid.optimizer.Optimizer):
-            return optimizer._learning_rate.get_lr()
+            if isinstance(optimizer._learning_rate, float):
+                return optimizer._learning_rate
+            else:
+                return optimizer._learning_rate()
         else:
             raise TypeError(
                     "'optimizer' must be object of class `paddle.optimizer.Optimizer`" \
diff --git a/python/paddle/fluid/tests/unittests/auto_parallel/CMakeLists.txt b/python/paddle/fluid/tests/unittests/auto_parallel/CMakeLists.txt
index d41bbcafc9fc4..bbccf452742a3 100644
--- a/python/paddle/fluid/tests/unittests/auto_parallel/CMakeLists.txt
+++ b/python/paddle/fluid/tests/unittests/auto_parallel/CMakeLists.txt
@@ -93,7 +93,7 @@ if(WITH_DISTRIBUTE AND WITH_GPU)
   py_test_modules(test_dist_matmul MODULES test_dist_matmul)
   py_test_modules(test_process_mesh MODULES test_process_mesh)
   py_test_modules(test_interface MODULES test_interface)
-  py_test_modules(test_stategy MODULES test_strategy)
+  py_test_modules(test_strategy MODULES test_strategy)
   py_test_modules(test_pass_quantization MODULES test_pass_quantization)
 
 endif()
diff --git a/python/paddle/fluid/tests/unittests/auto_parallel/engine_api.py b/python/paddle/fluid/tests/unittests/auto_parallel/engine_api.py
index 4621277a454b7..94677645ad4e8 100644
--- a/python/paddle/fluid/tests/unittests/auto_parallel/engine_api.py
+++ b/python/paddle/fluid/tests/unittests/auto_parallel/engine_api.py
@@ -127,7 +127,7 @@ def train(fetch):
     engine.fit(train_data=train_dataset,
                epochs=2,
                batch_size=batch_size,
-               valid_data=eval_dataset)
+               valid_data=eval_dataset1)
 
     # eval
     eval_dataset2 = MyDataset(batch_size)