diff --git a/mindspore/ccsrc/frontend/parallel/ops_info/gather_v2_p_info.h b/mindspore/ccsrc/frontend/parallel/ops_info/gather_v2_p_info.h
index c77f35b1e7d..f4b3aaf3e52 100644
--- a/mindspore/ccsrc/frontend/parallel/ops_info/gather_v2_p_info.h
+++ b/mindspore/ccsrc/frontend/parallel/ops_info/gather_v2_p_info.h
@@ -79,7 +79,6 @@ class GatherPInfo : public OperatorInfo {
   int64_t index_offset_;
   int64_t slice_size_;
   std::string replace_op_name_ = GATHERV2;
-  Shape out_dev_matrix_shape_;
   Group group_;
   bool manual_split_ = false;
   bool dynamic_shape_indices_ = false;
diff --git a/mindspore/ccsrc/frontend/parallel/ops_info/operator_info.cc b/mindspore/ccsrc/frontend/parallel/ops_info/operator_info.cc
index 052a5a808be..8e06415863e 100644
--- a/mindspore/ccsrc/frontend/parallel/ops_info/operator_info.cc
+++ b/mindspore/ccsrc/frontend/parallel/ops_info/operator_info.cc
@@ -1581,8 +1581,11 @@ Status OperatorInfo::InferAsLossDivisor() {
     return SUCCESS;
   }
 
-  as_loss_divisor_ = ComputeRepeatDeviceNumByTensorMap(dev_matrix_shape_, outputs_tensor_map_[0]);
-  MS_LOG(INFO) << name_ << ": the dev matrix shape is " << ShapeToString(dev_matrix_shape_)
+  if (out_dev_matrix_shape_.empty()) {
+    out_dev_matrix_shape_ = dev_matrix_shape_;
+  }
+  as_loss_divisor_ = ComputeRepeatDeviceNumByTensorMap(out_dev_matrix_shape_, outputs_tensor_map_[0]);
+  MS_LOG(INFO) << name_ << ": the dev matrix shape is " << ShapeToString(out_dev_matrix_shape_)
                << ", the output tensor map is " << ShapeToString(outputs_tensor_map_[0]) << ", loss divisor is "
                << as_loss_divisor_;
   return SUCCESS;
diff --git a/mindspore/ccsrc/frontend/parallel/ops_info/operator_info.h b/mindspore/ccsrc/frontend/parallel/ops_info/operator_info.h
index 4583de949a8..e1beef52045 100644
--- a/mindspore/ccsrc/frontend/parallel/ops_info/operator_info.h
+++ b/mindspore/ccsrc/frontend/parallel/ops_info/operator_info.h
@@ -232,6 +232,7 @@ class OperatorInfo {
   std::vector<TensorInfo> inputs_tensor_info_;
   std::vector<TensorInfo> outputs_tensor_info_;
   Shape dev_matrix_shape_;  // if repeated calculation, it contains the repeated_calc_num_
+  Shape out_dev_matrix_shape_;
   int64_t repeated_calc_num_ = 1;
   int64_t as_loss_divisor_ = 1;
   TensorMaps inputs_tensor_map_;
diff --git a/mindspore/ops/primitive.py b/mindspore/ops/primitive.py
index 7e633b27890..21882254340 100644
--- a/mindspore/ops/primitive.py
+++ b/mindspore/ops/primitive.py
@@ -146,10 +146,10 @@ class Primitive(Primitive_):
         mode = context.get_auto_parallel_context("parallel_mode")
         if strategy is not None:
             if not isinstance(strategy, tuple):
-                raise TypeError('strategy must be tuple type.')
+                raise TypeError(f'strategy must be tuple type, but got:{type(strategy)}')
             for ele in strategy:
                 if not isinstance(ele, tuple):
-                    raise TypeError('The element of strategy must be tuple type.')
+                    raise TypeError(f'The element of strategy must be tuple type, but got:{type(ele)}')
         if not _is_in_auto_parallel_mode() and strategy:
             logger.warning(f"The shard strategy {strategy} of {self.name} is not valid in {mode}. "
                            f"Please use semi auto or auto parallel mode.")