diff --git a/mindspore/nn/optim/optimizer.py b/mindspore/nn/optim/optimizer.py
index 60aa4ad31c..d59eb0bc58 100755
--- a/mindspore/nn/optim/optimizer.py
+++ b/mindspore/nn/optim/optimizer.py
@@ -166,7 +166,8 @@ class Optimizer(Cell):
         if context.get_auto_parallel_context("enable_parallel_optimizer"):
             if _get_parallel_mode() == ParallelMode.DATA_PARALLEL and context.get_context("device_target") == "Ascend":
                 self.use_parallel = True
-            elif context.get_context("device_target") != "Ascend":
+            elif _get_parallel_mode() == ParallelMode.DATA_PARALLEL \
+                    and context.get_context("device_target") != "Ascend":
                 raise RuntimeError("Parallel optimizer only supports Ascend in data parallel mode.")
             elif _get_parallel_mode() in (ParallelMode.STAND_ALONE, ParallelMode.HYBRID_PARALLEL):
                 raise RuntimeError("Parallel optimizer is not supported in {}.".format(_get_parallel_mode()))
diff --git a/mindspore/parallel/_utils.py b/mindspore/parallel/_utils.py
index e82ac20ee5..0a1b9f2328 100644
--- a/mindspore/parallel/_utils.py
+++ b/mindspore/parallel/_utils.py
@@ -241,13 +241,8 @@ def _infer_rank_list(train_map, predict_map=None):
     ret = {}
     for param_name in train_map:
         train_layout = train_map[param_name]
-        predict_layout = predict_map[param_name]
         train_dev_mat = train_layout[0]
         dev_num = np.array(train_dev_mat).prod()
-        if _check_same_layout(train_layout, predict_layout):
-            dev_rank = _get_global_rank()
-            ret[param_name] = ([dev_rank], True)
-            continue
         new_train_layout = _remove_repeated_slices(train_layout)
         array = np.arange(dev_num).reshape(train_dev_mat)
         index = ()
@@ -263,7 +258,13 @@ def _infer_rank_list(train_map, predict_map=None):
         if param_name not in predict_map:
             logger.warning("predict_map does not contain %s", param_name)
             continue
+        predict_layout = predict_map[param_name]
+        dev_num = np.array(predict_layout[0].prod())
         # optimization pass
+        if _check_same_layout(train_layout, predict_layout):
+            dev_rank = _get_global_rank()
+            ret[param_name] = ([dev_rank], True)
+            continue
         if _check_similar_layout(train_layout, predict_layout):
             if len(rank_list) == 1:
                 ret[param_name] = (rank_list, True)