!7608 [AutoParallel] add Dropout operator

Merge pull request !7608 from Chong/CheckParserInfo

mindspore/ccsrc/frontend/parallel/auto_parallel/rec_core/rec_generate_strategy.cc

@@ -416,7 +416,7 @@ Strategys PrepareStrategy(const std::shared_ptr<Graph> &graph, const std::vector
     return PrepareMatMul(graph, ops, iter_graph, iter_ops);
   } else if (type == ONEHOT) {
     return PrepareOneHot(graph, ops, iter_graph, iter_ops);
-  } else if ((type == SPARSE_SOFTMAX_CROSS_ENTROPY_WITH_LOGITS) || (type == "_VirtualDataset")) {
+  } else if ((type == SPARSE_SOFTMAX_CROSS_ENTROPY_WITH_LOGITS) || (type == "_VirtualDataset") || (type == "Dropout")) {
     return MakeDataParallelStrategy(graph, ops, iter_graph, iter_ops);
   } else {
     return MakeRecSearchStrategy(graph, ops, iter_graph, iter_ops);

mindspore/ccsrc/frontend/parallel/auto_parallel/rec_core/rec_parse_graph.cc

@@ -53,6 +53,10 @@ Graph::NodeType MakeNewOperator(const std::vector<std::shared_ptr<OperatorInfo>>
     NewOp.apply.op_type = DictOpType.at(op_type);
   }
 
+  if (ops[iter_ops]->outputs_tensor_info().size() == 0) {
+    MS_LOG(EXCEPTION) << ops[iter_ops]->name() << " output tensor info is empty.";
+  }
+
   if (ops[iter_ops]->outputs_tensor_info()[0].shape().size() == 4) {
     NewOp.tensor_parm = MakeTensor(
       ops[iter_ops]->outputs_tensor_info()[0].shape()[0], ops[iter_ops]->outputs_tensor_info()[0].shape()[1],
@@ -74,6 +78,10 @@ Graph::NodeType MakeNewOperator(const std::vector<std::shared_ptr<OperatorInfo>>
 
 OperatorRec CompleteOperatorInputs(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const size_t iter_ops,
                                    Graph::NodeType NewTensor) {
+  if (ops[iter_ops]->inputs_tensor_info().size() > MAX_INPUT_NUM) {
+    MS_LOG(EXCEPTION) << ops[iter_ops]->name() << " input tensor num exceeds limit.";
+  }
+
   for (size_t iter_input_tensors = 0; iter_input_tensors < ops[iter_ops]->inputs_tensor_info().size();
        iter_input_tensors++) {
     if (ops[iter_ops]->inputs_tensor_info()[iter_input_tensors].shape().size() == 4) {