add mixedprecision O3 level

mindspore/train/amp.py

@@ -65,7 +65,11 @@ _config_level = {
     "O2": {
         "keep_batchnorm_fp32": True,
         "cast_model_type": mstype.float16,
-        "loss_scale_manager": DynamicLossScaleManager()}}
+        "loss_scale_manager": DynamicLossScaleManager()},
+    "O3": {
+        "keep_batchnorm_fp32": False,
+        "cast_model_type": mstype.float16,
+        "loss_scale_manager": None}}
 
 
 def _check_kwargs(key_words):
@@ -117,11 +121,13 @@ 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]. Default: "O0".
+        level (str): Supports [O0, O2, O3]. 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'.
+            O2 is recommended on GPU, O3 is recommemded on Ascend.
 
         cast_model_type (:class:`mindspore.dtype`): Supports `mstype.float16` or `mstype.float32`.
             If set to `mstype.float16`, use `float16` mode to train. If set, overwrite the level setting.
@@ -131,7 +137,7 @@ 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'], Rel.IN, None)
+    validator.check('level', level, "", ['O0', 'O2', 'O3'], Rel.IN, None)
     _check_kwargs(kwargs)
     config = dict(_config_level[level], **kwargs)
     config = edict(config)

mindspore/train/model.py

@@ -54,10 +54,12 @@ class Model:
                              value would be passed to `Loss` metric, predict value and label would be passed to other
                              metric. Default: None.
         amp_level (str): Option for argument `level` in `mindspore.amp.build_train_network`, level for mixed
-            precision training. Supports [O0, O2]. Default: "O0".
+            precision training. Supports [O0, O2, O3]. Default: "O0".
 
             - O0: Do not change.
             - O2: Cast network to float16, keep batchnorm run in float32, using dynamic loss scale.
+            - O3: Cast network to float16, with additional property 'keep_batchnorm_fp32=False'.
+            O2 is recommended on GPU, O3 is recommended on Ascend.
 
         loss_scale_manager (Union[None, LossScaleManager]): If None, not scale the loss, or else
             scale the loss by LossScaleManager. If it is set, overwrite the level setting. It's a eyword argument.