add shard strategy validation

mindspore/ops/primitive.py

@@ -17,7 +17,7 @@
 import inspect
 import copy
 from mindspore.common.api import _wrap_func
-from mindspore import context
+from mindspore import context, log as logger
 from .._c_expression import Primitive_, real_run_op, prim_type
 from .._checkparam import Validator
 from . import signature as sig
@@ -141,6 +141,10 @@ class Primitive(Primitive_):
         Args:
             strategy (tuple): Strategy describes the distributed parallel mode of the current primitive.
         """
+        if context.get_auto_parallel_context("parallel_mode") not in [context.ParallelMode.AUTO_PARALLEL,
+                                                                      context.ParallelMode.SEMI_AUTO_PARALLEL]:
+            logger.warning("Shard strategy is not valid in ", context.get_auto_parallel_context("parallel_mode"),
+                           " mode. Please use semi auto or auto parallel mode.")
         self.add_prim_attr("strategy", strategy)
         return self
 

mindspore/train/model.py

@@ -809,12 +809,15 @@ class Model:
             Dict, Parameter layout dictionary used for load distributed checkpoint
 
         Examples:
+            >>> # This example should be run with multiple devices. Refer to the tutorial > Distributed Training on
+            >>> # mindspore.cn.
             >>> import numpy as np
             >>> import mindspore as ms
             >>> from mindspore import Model, context, Tensor
             >>> from mindspore.context import ParallelMode
             >>>
             >>> context.set_context(mode=context.GRAPH_MODE)
+            >>> init()
             >>> context.set_auto_parallel_context(full_batch=True, parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL)
             >>> input_data = Tensor(np.random.randint(0, 255, [1, 3, 224, 224]), ms.float32)
             >>> model = Model(Net())