amp add `best_choice` level

mindspore/train/amp.py

@@ -121,12 +121,15 @@ def build_train_network(network, optimizer, loss_fn=None, level='O0', **kwargs):
         loss_fn (Union[None, Cell]): Definition of the loss_fn. If None, the `network` should have the loss inside.
             Default: None.
         optimizer (Optimizer): Optimizer to update the Parameter.
-        level (str): Supports [O0, O2, O3]. Default: "O0".
+        level (str): Supports ["O0", "O2", "O3", "auto"]. Default: "O0".
 
             - O0: Do not change.
             - O2: Cast network to float16, keep batchnorm and `loss_fn` (if set) run in float32,
               using dynamic loss scale.
             - O3: Cast network to float16, with additional property 'keep_batchnorm_fp32=False'.
+            - auto: Set to level to recommended level in different devices. Set level to O2 on GPU, Set
+              level to O3 Ascend. The recommended level is choose by the export experience, cannot
+              always generalize. User should specify the level for special network.
 
             O2 is recommended on GPU, O3 is recommended on Ascend.
 
@@ -139,7 +142,17 @@ def build_train_network(network, optimizer, loss_fn=None, level='O0', **kwargs):
     """
     validator.check_value_type('network', network, nn.Cell, None)
     validator.check_value_type('optimizer', optimizer, nn.Optimizer, None)
-    validator.check('level', level, "", ['O0', 'O2', 'O3'], Rel.IN, None)
+    validator.check('level', level, "", ['O0', 'O2', 'O3', "auto"], Rel.IN, None)
+
+    if level == "auto":
+        device_target = context.get_context('device_target')
+        if device_target == "GPU":
+            level = "O2"
+        elif device_target == "Ascend":
+            level = "O3"
+        else:
+            raise ValueError("Level `auto` only support when `device_target` is GPU or Ascend.")
+
     _check_kwargs(kwargs)
     config = dict(_config_level[level], **kwargs)
     config = edict(config)