!17491 replace internal interface form model ncf, centerface and mass

From: @zhouneng2
Reviewed-by: @oacjiewen,@c_34
Signed-off-by: @c_34

model_zoo/official/cv/centerface/train.py

@@ -30,7 +30,7 @@ from mindspore.nn.optim.sgd import SGD
 from mindspore import Tensor
 from mindspore.communication.management import init, get_rank, get_group_size
 from mindspore.train.callback import ModelCheckpoint, RunContext
-from mindspore.train.callback import _InternalCallbackParam, CheckpointConfig
+from mindspore.train.callback import CheckpointConfig
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from mindspore.profiler.profiling import Profiler
 from mindspore.common import set_seed
@@ -125,6 +125,16 @@ def convert_training_shape(args_):
     return training_shape
 
 
+class InternalCallbackParam(dict):
+    """Internal callback object's parameters."""
+
+    def __getattr__(self, para_name):
+        return self[para_name]
+
+    def __setattr__(self, para_name, para_value):
+        self[para_name] = para_value
+
+
 if __name__ == "__main__":
     # init distributed
     if args.is_distributed:
@@ -285,7 +295,7 @@ if __name__ == "__main__":
         ckpt_cb = ModelCheckpoint(config=ckpt_config,
                                   directory=args.outputs_dir,
                                   prefix='{}'.format(args.rank))
-        cb_params = _InternalCallbackParam()
+        cb_params = InternalCallbackParam()
         cb_params.train_network = network
         cb_params.epoch_num = ckpt_max_num
         cb_params.cur_epoch_num = 1

model_zoo/official/nlp/mass/src/transformer/transformer_for_train.py

@@ -23,7 +23,6 @@ from mindspore.common.parameter import Parameter
 from mindspore.common import dtype as mstype
 from mindspore.nn.wrap.grad_reducer import DistributedGradReducer
 from mindspore.context import ParallelMode
-from mindspore.parallel._utils import _get_device_num, _get_parallel_mode, _get_gradients_mean
 
 from .transformer import Transformer
 from .grad_clip import GRADIENT_CLIP_TYPE, GRADIENT_CLIP_VALUE, ClipGradients
@@ -245,15 +244,15 @@ class TransformerTrainOneStepWithLossScaleCell(nn.Cell):
         self.reducer_flag = False
         self.all_reduce = P.AllReduce()
 
-        self.parallel_mode = _get_parallel_mode()
+        self.parallel_mode = context.get_auto_parallel_context("parallel_mode")
         if self.parallel_mode not in ParallelMode.MODE_LIST:
             raise ValueError("Parallel mode does not support: ", self.parallel_mode)
         if self.parallel_mode in [ParallelMode.DATA_PARALLEL, ParallelMode.HYBRID_PARALLEL]:
             self.reducer_flag = True
         self.grad_reducer = None
         if self.reducer_flag:
-            mean = _get_gradients_mean()
-            degree = _get_device_num()
+            mean = context.get_auto_parallel_context("gradients_mean")
+            degree = context.get_auto_parallel_context("device_num")
             self.grad_reducer = DistributedGradReducer(optimizer.parameters, mean, degree)
         self.is_distributed = (self.parallel_mode != ParallelMode.STAND_ALONE)
         self.clip_gradients = ClipGradients()

model_zoo/official/recommend/ncf/src/ncf.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 # ============================================================================
 """Neural Collaborative Filtering Model"""
-from mindspore import nn
+from mindspore import nn, context
 from mindspore import Tensor, Parameter, ParameterTuple
 from mindspore._checkparam import Validator as validator
 from mindspore.nn.layer.activation import get_activation
@@ -22,7 +22,6 @@ from mindspore.ops import operations as P
 from mindspore.common.initializer import initializer
 from mindspore.ops import functional as F
 from mindspore.ops import composite as C
-from mindspore.parallel._utils import _get_device_num, _get_parallel_mode, _get_gradients_mean
 from mindspore.context import ParallelMode
 from mindspore.nn.wrap.grad_reducer import DistributedGradReducer
 
@@ -245,12 +244,12 @@ class TrainStepWrap(nn.Cell):
 
         self.reducer_flag = False
         self.grad_reducer = None
-        parallel_mode = _get_parallel_mode()
+        parallel_mode = context.get_auto_parallel_context("parallel_mode")
         if parallel_mode in (ParallelMode.DATA_PARALLEL, ParallelMode.HYBRID_PARALLEL):
             self.reducer_flag = True
         if self.reducer_flag:
-            mean = _get_gradients_mean()
-            degree = _get_device_num()
+            mean = context.get_auto_parallel_context("gradients_mean")
+            degree = context.get_auto_parallel_context("device_num")
             self.grad_reducer = DistributedGradReducer(self.optimizer.parameters, mean, degree)