compile profiling

mindspore/common/api.py

@@ -383,8 +383,6 @@ class _Executor:
             Str, the full phase of the cell.
             Bool, if the graph has been compiled before, return False, else return True.
         """
-        obj.check_names()
-        _check_full_batch()
         args_names, args_list = _generate_pip_args(obj, *args)
         dic = dict(zip(args_names, args_list))
         key = generate_key(phase, dic)
@@ -393,22 +391,23 @@ class _Executor:
             phase = phase + '.' + self.phase_prefix + '.' + str(obj.create_time)
         else:
             phase = self.phase_prefix + phase + '.' + str(obj.create_time)
-        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)
-
-        self._set_dataset_mode(args_list)
 
         if phase in self.compile_cache.keys():
             logger.debug("%r graph has existed.", phase)
             return phase, False
 
+        obj.check_names()
+        _check_full_batch()
+        self._set_dataset_mode(args_list)
+
         is_sink_mode = args and isinstance(args[0], Tensor) and args[0].virtual_flag
         if auto_parallel_mode and _need_to_full() and not is_sink_mode and obj.auto_parallel_compile_and_run():
             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)
         result = self._executor.compile(obj, args_list, phase, use_vm)
         self.compile_cache[phase] = phase
         if not result: