!5017 remove internal interface in wide&deep

Merge pull request !5017 from yao_yf/wide_and_deep_no_internal_interface

mindspore/parallel/__init__.py

@@ -17,5 +17,7 @@ This interface is ONLY used in Auto-parallel procedure.
 """
 from .algo_parameter_config import get_algo_parameters, reset_algo_parameters, \
     set_algo_parameters
+from ._cost_model_context import set_multi_subgraphs, get_multi_subgraphs
 
-__all__ = ["get_algo_parameters", "reset_algo_parameters", "set_algo_parameters"]
+__all__ = ["set_multi_subgraphs", "get_multi_subgraphs",
+           "get_algo_parameters", "reset_algo_parameters", "set_algo_parameters"]

mindspore/parallel/_cost_model_context.py

@@ -479,7 +479,6 @@ set_cost_model_context_func_map = {
     "costmodel_communi_threshold": cost_model_context().set_costmodel_communi_threshold,
     "costmodel_communi_const": cost_model_context().set_costmodel_communi_const,
     "costmodel_communi_bias": cost_model_context().set_costmodel_communi_bias,
-    "multi_subgraphs": cost_model_context().set_multi_subgraphs,
     "run_phase": cost_model_context().set_run_phase,
     "costmodel_allreduce_fusion_algorithm": cost_model_context().set_costmodel_allreduce_fusion_algorithm,
     "costmodel_allreduce_fusion_times": cost_model_context().set_costmodel_allreduce_fusion_times,
@@ -501,7 +500,6 @@ get_cost_model_context_func_map = {
     "costmodel_communi_threshold": cost_model_context().get_costmodel_communi_threshold,
     "costmodel_communi_const": cost_model_context().get_costmodel_communi_const,
     "costmodel_communi_bias": cost_model_context().get_costmodel_communi_bias,
-    "multi_subgraphs": cost_model_context().get_multi_subgraphs,
     "run_phase": cost_model_context().get_run_phase,
     "costmodel_allreduce_fusion_algorithm": cost_model_context().get_costmodel_allreduce_fusion_algorithm,
     "costmodel_allreduce_fusion_times": cost_model_context().get_costmodel_allreduce_fusion_times,
@@ -538,7 +536,6 @@ def set_cost_model_context(**kwargs):
         costmodel_communi_threshold (float): A parameter used in adjusting communication calculation for practice.
         costmodel_communi_const (float): A parameter used in adjusting communication calculation for practice.
         costmodel_communi_bias (float): A parameter used in adjusting communication calculation for practice.
-        multi_subgraphs (bool): A parameter used in marking the flag of ANF graph containing multiple subgraphs.
         run_phase (int): A parameter indicating which phase is running: training (0) or inference (1). Default: 0.
         costmodel_allreduce_fusion_algorithm (int): The allreduce fusion algorithm.
             0: bypass allreduce fusion;
@@ -591,3 +588,18 @@ def get_cost_model_context(attr_key):
 def reset_cost_model_context():
     """Reset cost model context attributes."""
     cost_model_context().reset_cost_model()
+
+def set_multi_subgraphs(multi_subgraph=True):
+    """
+    Set the flag of ANF graph containing multiple subgraphs.
+
+    Args:
+        multi_subgraph (bool): A parameter used in marking the multi-subgraphs flag.
+    """
+    cost_model_context().set_multi_subgraphs(multi_subgraph)
+
+def get_multi_subgraphs():
+    """
+        Get the flag of ANF graph containing multiple subgraphs.
+    """
+    cost_model_context().get_multi_subgraphs()

model_zoo/official/recommend/wide_and_deep/src/wide_and_deep.py

@@ -14,7 +14,7 @@
 # ============================================================================
 """wide and deep model"""
 import numpy as np
-from mindspore import nn
+from mindspore import nn, context
 from mindspore import Parameter, ParameterTuple
 import mindspore.common.dtype as mstype
 from mindspore.ops import functional as F
@@ -22,10 +22,7 @@ from mindspore.ops import composite as C
 from mindspore.ops import operations as P
 from mindspore.nn import Dropout
 from mindspore.nn.optim import Adam, FTRL, LazyAdam
-# from mindspore.nn.metrics import Metric
 from mindspore.common.initializer import Uniform, initializer
-# from mindspore.train.callback import ModelCheckpoint, CheckpointConfig
-from mindspore.parallel._utils import _get_device_num, _get_parallel_mode, _get_mirror_mean
 from mindspore.train.parallel_utils import ParallelMode
 from mindspore.nn.wrap.grad_reducer import DistributedGradReducer
 from mindspore.communication.management import get_group_size
@@ -142,7 +139,7 @@ class WideDeepModel(nn.Cell):
         self.batch_size = config.batch_size
         host_device_mix = bool(config.host_device_mix)
         parameter_server = bool(config.parameter_server)
-        parallel_mode = _get_parallel_mode()
+        parallel_mode = context.get_auto_parallel_context("parallel_mode")
         is_auto_parallel = parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL)
         if is_auto_parallel:
             self.batch_size = self.batch_size * get_group_size()
@@ -259,7 +256,7 @@ class NetWithLossClass(nn.Cell):
         super(NetWithLossClass, self).__init__(auto_prefix=False)
         host_device_mix = bool(config.host_device_mix)
         parameter_server = bool(config.parameter_server)
-        parallel_mode = _get_parallel_mode()
+        parallel_mode = context.get_auto_parallel_context("parallel_mode")
         is_auto_parallel = parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL)
         self.no_l2loss = (is_auto_parallel if host_device_mix else parameter_server)
         self.network = network
@@ -312,7 +309,7 @@ class TrainStepWrap(nn.Cell):
 
     def __init__(self, network, sens=1024.0, host_device_mix=False, parameter_server=False):
         super(TrainStepWrap, self).__init__()
-        parallel_mode = _get_parallel_mode()
+        parallel_mode = context.get_auto_parallel_context("parallel_mode")
         is_auto_parallel = parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL)
         self.network = network
         self.network.set_train()
@@ -351,12 +348,11 @@ class TrainStepWrap(nn.Cell):
         self.reducer_flag = False
         self.grad_reducer_w = None
         self.grad_reducer_d = None
-        parallel_mode = _get_parallel_mode()
         self.reducer_flag = parallel_mode in (ParallelMode.DATA_PARALLEL,
                                               ParallelMode.HYBRID_PARALLEL)
         if self.reducer_flag:
-            mean = _get_mirror_mean()
-            degree = _get_device_num()
+            mean = context.get_auto_parallel_context("mirror_mean")
+            degree = context.get_auto_parallel_context("device_num")
             self.grad_reducer_w = DistributedGradReducer(self.optimizer_w.parameters, mean, degree)
             self.grad_reducer_d = DistributedGradReducer(self.optimizer_d.parameters, mean, degree)
 

model_zoo/official/recommend/wide_and_deep/train_and_eval_auto_parallel.py

@@ -22,7 +22,7 @@ from mindspore import Model, context
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, TimeMonitor
 from mindspore.train import ParallelMode
 from mindspore.communication.management import get_rank, get_group_size, init
-from mindspore.parallel import _cost_model_context as cost_model_context
+from mindspore.parallel import set_multi_subgraphs
 from mindspore.nn.wrap.cell_wrapper import VirtualDatasetCellTriple
 
 from src.wide_and_deep import PredictWithSigmoid, TrainStepWrap, NetWithLossClass, WideDeepModel
@@ -127,7 +127,7 @@ if __name__ == "__main__":
     context.set_context(mode=context.GRAPH_MODE, device_target=wide_deep_config.device_target, save_graphs=True)
     context.set_context(variable_memory_max_size="24GB")
     context.set_context(enable_sparse=True)
-    cost_model_context.set_cost_model_context(multi_subgraphs=True)
+    set_multi_subgraphs()
     if wide_deep_config.device_target == "Ascend":
         init("hccl")
     elif wide_deep_config.device_target == "GPU":

model_zoo/official/recommend/wide_and_deep_multitable/src/wide_and_deep.py

@@ -16,7 +16,7 @@
 import numpy as np
 
 import mindspore.common.dtype as mstype
-from mindspore import nn
+from mindspore import nn, context
 from mindspore import Tensor, Parameter, ParameterTuple
 from mindspore.ops import functional as F
 from mindspore.ops import composite as C
@@ -24,7 +24,6 @@ from mindspore.ops import operations as P
 from mindspore.nn import Dropout, Flatten
 from mindspore.nn.optim import Adam, FTRL
 from mindspore.common.initializer import Uniform, initializer
-from mindspore.parallel._utils import _get_device_num, _get_parallel_mode, _get_mirror_mean
 from mindspore.train.parallel_utils import ParallelMode
 from mindspore.nn.wrap.grad_reducer import DistributedGradReducer
 
@@ -552,13 +551,13 @@ class TrainStepWrap(nn.Cell):
         self.reducer_flag = False
         self.grad_reducer_w = None
         self.grad_reducer_d = 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_mirror_mean()
-            degree = _get_device_num()
+            mean = context.get_auto_parallel_context("mirror_mean")
+            degree = context.get_auto_parallel_context("device_num")
             self.grad_reducer_w = DistributedGradReducer(
                 self.optimizer_w.parameters, mean, degree)
             self.grad_reducer_d = DistributedGradReducer(

tests/st/model_zoo_tests/wide_and_deep/python_file_for_ci/train_and_test_multinpu_ci.py

@@ -21,7 +21,7 @@ from mindspore import Model, context
 from mindspore.train.callback import TimeMonitor
 from mindspore.train import ParallelMode
 from mindspore.communication.management import get_rank, get_group_size, init
-from mindspore.parallel import _cost_model_context as cost_model_context
+from mindspore.parallel import set_multi_subgraphs
 from mindspore.nn.wrap.cell_wrapper import VirtualDatasetCellTriple
 
 from src.wide_and_deep import PredictWithSigmoid, TrainStepWrap, NetWithLossClass, WideDeepModel
@@ -33,7 +33,7 @@ from src.config import WideDeepConfig
 sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=True)
 context.set_auto_parallel_context(parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL, mirror_mean=True)
-cost_model_context.set_cost_model_context(multi_subgraphs=True)
+set_multi_subgraphs()
 init()
 
 

tests/ut/python/parallel/test_auto_parallel_double_subgraphs.py

@@ -23,7 +23,7 @@ from mindspore.nn.optim import Adam, FTRL
 from mindspore.ops import composite as C
 from mindspore.ops import functional as F
 from mindspore.ops import operations as P
-from mindspore.parallel import _cost_model_context as cost_model_context
+from mindspore.parallel import set_multi_subgraphs
 from mindspore.parallel._utils import _reset_op_id as reset_op_id
 
 
@@ -103,7 +103,7 @@ class TrainStepWarp(nn.Cell):
 
 
 def test_double_subgraphs():
-    cost_model_context.set_cost_model_context(multi_subgraphs=True)
+    set_multi_subgraphs()
     context.set_context(save_graphs=True)
     context.set_auto_parallel_context(device_num=8, global_rank=0)
     context.set_auto_parallel_context(parallel_mode="auto_parallel")