[PARALLEL LOGING]Less the logging cout

mindspore/ccsrc/frontend/parallel/auto_parallel/edge_costmodel.h

@@ -14,8 +14,8 @@
  * limitations under the License.
  */
 
-#ifndef PARALLEL_AUTO_PARALLEL_EDGE_COSTMODEL_H_
-#define PARALLEL_AUTO_PARALLEL_EDGE_COSTMODEL_H_
+#ifndef MINDSPORE_CCSRC_FRONTEND_PARALLEL_AUTO_PARALLEL_EDGE_COSTMODEL_H_
+#define MINDSPORE_CCSRC_FRONTEND_PARALLEL_AUTO_PARALLEL_EDGE_COSTMODEL_H_
 
 #include <map>
 #include <memory>
@@ -189,4 +189,4 @@ class Edge {
 };
 }  // namespace parallel
 }  // namespace mindspore
-#endif  // PARALLEL_AUTO_PARALLEL_EDGE_COSTMODEL_H_
+#endif  // MINDSPORE_CCSRC_FRONTEND_PARALLEL_AUTO_PARALLEL_EDGE_COSTMODEL_H_

mindspore/ccsrc/frontend/parallel/step_auto_parallel.h

@@ -14,8 +14,8 @@
  * limitations under the License.
  */
 
-#ifndef PARALLEL_STEP_AUTO_PARALLEL_H_
-#define PARALLEL_STEP_AUTO_PARALLEL_H_
+#ifndef MINDSPORE_CCSRC_FRONTEND_PARALLEL_STEP_AUTO_PARALLEL_H_
+#define MINDSPORE_CCSRC_FRONTEND_PARALLEL_STEP_AUTO_PARALLEL_H_
 
 #include <map>
 #include <memory>
@@ -72,4 +72,4 @@ std::vector<std::vector<size_t>> GetSharedTensorsOps(
   const std::vector<std::vector<std::string>> &input_tensor_names);
 }  // namespace parallel
 }  // namespace mindspore
-#endif  // PARALLEL_STEP_AUTO_PARALLEL_H_
+#endif  // MINDSPORE_CCSRC_FRONTEND_PARALLEL_STEP_AUTO_PARALLEL_H_

mindspore/python/mindspore/log.py

@@ -601,3 +601,34 @@ def _setup_logger(kwargs):
     finally:
         _setup_logger_lock.release()
     return _global_logger
+
+
+class _LogActionOnce:
+    """
+    A wrapper for modify the warning logging to an empty function. This is used when we want to only log
+    once to avoid the repeated logging.
+
+    Args:
+        logger (logging): The logger object.
+
+    """
+    __is_logged__ = False
+
+    def __init__(self, logger):
+        self.logger = logger
+
+    def __call__(self, func):
+        def wrapper(*args, **kwargs):
+            if not hasattr(self.logger, 'warning'):
+                return func(*args, **kwargs)
+
+            _old_ = self.logger.warning
+            if _LogActionOnce.__is_logged__:
+                self.logger.warning = lambda x: x
+            else:
+                _LogActionOnce.__is_logged__ = True
+            res = func(*args, **kwargs)
+            if hasattr(self.logger, 'warning'):
+                self.logger.warning = _old_
+            return res
+        return wrapper

mindspore/python/mindspore/ops/primitive.py

@@ -18,6 +18,7 @@ import functools
 import inspect
 import copy
 from mindspore.common.api import _wrap_func
+from mindspore.log import _LogActionOnce
 from mindspore import context, log as logger
 from mindspore.parallel._utils import _is_in_auto_parallel_mode
 from .._c_expression import Primitive_, real_run_op, prim_type
@@ -153,6 +154,7 @@ class Primitive(Primitive_):
         self.add_prim_attr("stage", stage)
         return self
 
+    @_LogActionOnce(logger=logger)
     def shard(self, in_strategy=None, out_strategy=None):
         """
         Add strategies to primitive attribute.
@@ -200,11 +202,17 @@ class Primitive(Primitive_):
 
         if not _is_in_auto_parallel_mode():
             if in_strategy is not None:
-                logger.warning(f"The in_strategy: {in_strategy} of {self.name} is not valid in {mode}. "
-                               f"Please use semi auto or auto parallel mode.")
+                logger.warning(f"The in_strategy of the operator in your network will not take effect in {mode} mode. "
+                               f"This means the the shard function called in the network is ignored. "
+                               f"If you want to enable it, please use semi auto or auto parallel mode by "
+                               f"context.set_auto_parallel_context(parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL "
+                               f"or context.set_auto_parallel_context(parallel_mode=ParallelMode.AUTO_PARALLEL")
             if out_strategy is not None:
-                logger.warning(f"The out_strategy: {out_strategy} of {self.name} is not valid in {mode}. "
-                               f"Please use semi auto or auto parallel mode.")
+                logger.warning(f"The out_strategy of the operator in your network will not take effect in {mode} mode."
+                               f" This means the the shard function called in the network is ignored. "
+                               f"If you want to enable it, please use semi auto or auto parallel mode by "
+                               f"context.set_auto_parallel_context(parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL "
+                               f"or context.set_auto_parallel_context(parallel_mode=ParallelMode.AUTO_PARALLEL")
 
         self.add_prim_attr("in_strategy", in_strategy)
         self.add_prim_attr("out_strategy", out_strategy)