diff --git a/mindspore/common/api.py b/mindspore/common/api.py
index 7faa2089e04..2ede23a989d 100644
--- a/mindspore/common/api.py
+++ b/mindspore/common/api.py
@@ -517,6 +517,13 @@ class _CellGraphExecutor:
         else:
             _set_dataset_mode_config('normal')
 
+    @staticmethod
+    def _use_vm_mode():
+        enable_ge = context.get_context("enable_ge")
+        enable_debug_runtime = context.get_context("enable_debug_runtime")
+        exe_mode = context.get_context("mode") == context.PYNATIVE_MODE
+        return not enable_ge or (enable_debug_runtime and exe_mode)
+
     def compile(self, obj, *args, phase='predict', do_convert=True, auto_parallel_mode=False):
         """
         Compiles graph.
@@ -543,7 +550,7 @@ class _CellGraphExecutor:
         obj.arguments_key = str(key)
         phase = phase + '.' + str(obj.create_time) + '.' + str(id(obj)) + '.' + obj.arguments_key
 
-        if phase in obj.compile_cache:
+        if phase in obj.compile_cache and self.has_compiled(phase):
             logger.debug("%r graph has existed.", phase)
             return phase, False
 
@@ -556,9 +563,8 @@ class _CellGraphExecutor:
             args_full = _to_full_tensor(args, _get_device_num(), _get_global_rank())
             _, args_list = _generate_pip_args(obj, *args_full)
 
-        enable_debug_runtime = context.get_context("enable_debug_runtime")
         enable_ge = context.get_context("enable_ge")
-        use_vm = not enable_ge or (enable_debug_runtime and context.get_context("mode") == context.PYNATIVE_MODE)
+        use_vm = self._use_vm_mode()
         result = self._graph_executor.compile(obj, args_list, phase, use_vm, self.queue_name, self.enable_tuple_broaden)
         obj.compile_cache.add(phase)
         if not result:
diff --git a/mindspore/common/tensor.py b/mindspore/common/tensor.py
index e30bd1a8d53..51475075749 100644
--- a/mindspore/common/tensor.py
+++ b/mindspore/common/tensor.py
@@ -130,17 +130,19 @@ class Tensor(Tensor_):
             if isinstance(input_data, (tuple, list)):
                 if np.array(input_data).dtype not in valid_dtypes:
                     raise TypeError(f"For Tensor, the input_data is {input_data} that contain unsupported element.")
+
             if dtype is not None:
                 validator.check_type_name('dtype', dtype, mstype.number_type + (mstype.bool_, mstype.string), "Tensor")
+            else:
+                dtype = self._set_default_dtype(input_data, dtype)
 
             if isinstance(input_data, np.ndarray) and (not input_data.flags['FORC']):
                 input_data = np.ascontiguousarray(input_data)
-            if dtype is None:
-                Tensor_.__init__(self, input_data)
-            else:
-                Tensor_.__init__(self, input_data, dtype)
+
+            Tensor_.__init__(self, input_data, dtype)
         else:
             Tensor_.__init__(self, dtype, shape)
+
         self._virtual_flag = False
         self.init = init
         self.init_finished = True
@@ -151,6 +153,13 @@ class Tensor(Tensor_):
         self.parent_tensor_ = None
         self.index_of_parent_ = None
 
+    @staticmethod
+    def _set_default_dtype(input_data, dtype):
+        if isinstance(input_data, (float, list, tuple)):
+            if np.array(input_data).dtype == np.float64:
+                return mstype.float32
+        return dtype
+
     def __deepcopy__(self, memodict):
         new_obj = Tensor(self)
         new_obj.init = self.init
diff --git a/tests/ut/python/ir/test_tensor.py b/tests/ut/python/ir/test_tensor.py
index 9acb4254996..110772b8ed2 100644
--- a/tests/ut/python/ir/test_tensor.py
+++ b/tests/ut/python/ir/test_tensor.py
@@ -61,7 +61,7 @@ def test_tensor():
 
     t3 = ms.Tensor(0.1)
     assert isinstance(t3, ms.Tensor)
-    assert t3.dtype == ms.float64
+    assert t3.dtype == ms.float32
 
     t4 = ms.Tensor(1)
     assert isinstance(t4, ms.Tensor)
@@ -149,7 +149,7 @@ def test_tensor_type_float64():
     t = ms.Tensor([[1.0, 2, 3], [4, 5, 6]])
     assert isinstance(t, ms.Tensor)
     assert t.shape == (2, 3)
-    assert t.dtype == ms.float64
+    assert t.dtype == ms.float32
 
     t_zero = ms.Tensor(np.zeros([1, 2, 3]))
     assert isinstance(t_zero, ms.Tensor)