回退 'Pull Request !3103 : change type of Shape from int32 to int64'

mindspore/ccsrc/frontend/operator/prim_nn.cc

@@ -402,36 +402,31 @@ AbstractBasePtr InferImplDropoutGenMask(const AnalysisEnginePtr &, const Primiti
   for (std::size_t i = 0; i < x_shape->size(); ++i) {
     auto value_track = x_shape_data[i]->GetValueTrack();
     MS_EXCEPTION_IF_NULL(value_track);
-    int64_t e_value = 0;
-    if (value_track->isa<Int64Imm>()) {
-      e_value = GetValue<int64_t>(value_track);
-    } else if (value_track->isa<Int32Imm>()) {
-      e_value = static_cast<int64_t>(GetValue<int>(value_track));
-    } else {
-      MS_LOG(EXCEPTION) << "DropOutGenMask input x_shape elements is not int64 or int32, but "
-                        << value_track->ToString() << ".";
+    if (!value_track->isa<Int32Imm>()) {
+      MS_LOG(EXCEPTION) << "DropOutGenMask input x_shape elements is not int32, but " << value_track->ToString() << ".";
     }
 
+    int e_value = GetValue<int>(value_track);
     if (e_value <= 0) {
       MS_LOG(EXCEPTION) << "DropOutGenMask product of x_shape should be > 0";
     }
-    if (std::numeric_limits<int64_t>::max() / count / e_value < 1) {
+    if (std::numeric_limits<int>::max() / count / e_value < 1) {
       MS_LOG(EXCEPTION) << "integer multiply integer overflow";
     }
     count = count * e_value;
   }
 
   // convert to bytes(8 bits) mask, using round up
-  int64_t n128s = count / 128;
+  int n128s = count / 128;
   if ((count % 128) != 0) {
     n128s++;
   }
-  int64_t bytes_count = n128s * 16;
-  std::vector<int64_t> shape_y{bytes_count};
+  int bytes_count = n128s * 16;
+  std::vector<int> shape_y{bytes_count};
 
   primitive->set_attr("T", kInt32);
   return std::make_shared<AbstractTensor>(std::make_shared<AbstractScalar>(kAnyValue, kUInt8),
-                                          std::make_shared<Shape>(std::vector<int64_t>{shape_y}));
+                                          std::make_shared<Shape>(std::vector<int>{shape_y}));
 }
 }  // namespace abstract
 }  // namespace mindspore

mindspore/ccsrc/frontend/parallel/auto_parallel/graph_costmodel.cc

@@ -1580,7 +1580,7 @@ Status CostGraph::InitSelectedStrategy() {
         if (stra.empty()) {
           MS_LOG(EXCEPTION) << "Infer strategy by tensor_info failed";
         }
-        Strategys stra_inputs = {stra};
+        std::vector<Dimensions> stra_inputs = {stra};
         StrategyPtr reshape_stra =
           std::make_shared<Strategy>((*pre_iter)->prev_operator()->strategy()->GetInputStage(), stra_inputs);
         reshape_info->set_strategy(reshape_stra);

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

@@ -45,9 +45,10 @@ void GenerateStrategy(const std::shared_ptr<Graph> &graph, const std::vector<std
   GenerateRemainingOperatorStrategy(graph, ops, input_tensor_names, index_list, no_stra_op_list);
 }
 
-Strategys PrepareMatMul(const std::shared_ptr<Graph> &graph, const std::vector<std::shared_ptr<OperatorInfo>> &ops,
-                        const size_t iter_graph, const size_t iter_ops) {
-  Strategys strategies;
+std::vector<std::vector<int32_t>> PrepareMatMul(const std::shared_ptr<Graph> &graph,
+                                                const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                const size_t iter_graph, const size_t iter_ops) {
+  std::vector<std::vector<int32_t>> strategies;
   auto attrs = ops[iter_ops]->attrs();
   bool transpose_a = attrs[TRANSPOSE_A]->cast<BoolImmPtr>()->value();
   bool transpose_b = attrs[TRANSPOSE_B]->cast<BoolImmPtr>()->value();
@@ -104,40 +105,41 @@ Strategys PrepareMatMul(const std::shared_ptr<Graph> &graph, const std::vector<s
   }
 
   for (size_t iter_op_inputs = 0; iter_op_inputs < ops[iter_ops]->inputs_tensor_info().size(); iter_op_inputs++) {
-    Dimensions s;
+    std::vector<int32_t> s;
     if (transpose_a && (iter_op_inputs == 0)) {
       s.push_back(
-        static_cast<int64_t>(1.0 / graph->nodes[iter_graph].apply.arguments[iter_op_inputs].tensor_str.str_w));
+        static_cast<int32_t>(1.0 / graph->nodes[iter_graph].apply.arguments[iter_op_inputs].tensor_str.str_w));
       s.push_back(
-        static_cast<int64_t>(1.0 / graph->nodes[iter_graph].apply.arguments[iter_op_inputs].tensor_str.str_h));
+        static_cast<int32_t>(1.0 / graph->nodes[iter_graph].apply.arguments[iter_op_inputs].tensor_str.str_h));
     } else if (transpose_b && (iter_op_inputs == 1)) {
       s.push_back(
-        static_cast<int64_t>(1.0 / graph->nodes[iter_graph].apply.arguments[iter_op_inputs].tensor_str.str_w));
+        static_cast<int32_t>(1.0 / graph->nodes[iter_graph].apply.arguments[iter_op_inputs].tensor_str.str_w));
       s.push_back(
-        static_cast<int64_t>(1.0 / graph->nodes[iter_graph].apply.arguments[iter_op_inputs].tensor_str.str_h));
+        static_cast<int32_t>(1.0 / graph->nodes[iter_graph].apply.arguments[iter_op_inputs].tensor_str.str_h));
     } else {
       s.push_back(
-        static_cast<int64_t>(1.0 / graph->nodes[iter_graph].apply.arguments[iter_op_inputs].tensor_str.str_h));
+        static_cast<int32_t>(1.0 / graph->nodes[iter_graph].apply.arguments[iter_op_inputs].tensor_str.str_h));
       s.push_back(
-        static_cast<int64_t>(1.0 / graph->nodes[iter_graph].apply.arguments[iter_op_inputs].tensor_str.str_w));
+        static_cast<int32_t>(1.0 / graph->nodes[iter_graph].apply.arguments[iter_op_inputs].tensor_str.str_w));
     }
     strategies.push_back(s);
   }
   return strategies;
 }
 
-Strategys PrepareBiasAdd(const std::shared_ptr<Dimensions> &s) {
-  Strategys strategies;
+std::vector<std::vector<int32_t>> PrepareBiasAdd(const std::shared_ptr<std::vector<int32_t>> &s) {
+  std::vector<std::vector<int32_t>> strategies;
   strategies.push_back(*s);
-  Dimensions s_biasadd;
+  std::vector<int32_t> s_biasadd;
   s_biasadd.push_back(s->at(1));
   strategies.push_back(s_biasadd);
   return strategies;
 }
 
-Strategys PrepareOneHot(const std::shared_ptr<Graph> &graph, const std::vector<std::shared_ptr<OperatorInfo>> &ops,
-                        const size_t iter_graph, const size_t iter_ops) {
-  Strategys strategies = MakeRecSearchStrategy(graph, ops, iter_graph, iter_ops);
+std::vector<std::vector<int32_t>> PrepareOneHot(const std::shared_ptr<Graph> &graph,
+                                                const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                const size_t iter_graph, const size_t iter_ops) {
+  std::vector<std::vector<int32_t>> strategies = MakeRecSearchStrategy(graph, ops, iter_graph, iter_ops);
 
   int32_t axis = -1;
   auto iter = ops[iter_ops]->attrs().find(AXIS);
@@ -156,14 +158,15 @@ Strategys PrepareOneHot(const std::shared_ptr<Graph> &graph, const std::vector<s
     graph->nodes[iter_graph].tensor_parm.tensor_str.str_w = 1.0;
   }
 
-  Dimensions s_empty = {};
+  std::vector<int32_t> s_empty = {};
   strategies.push_back(s_empty);
   strategies.push_back(s_empty);
   return strategies;
 }
 
-Strategys PrepareGatherV2(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const size_t iter_ops, Dimensions s) {
-  Strategys strategies;
+std::vector<std::vector<int32_t>> PrepareGatherV2(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                  const size_t iter_ops, std::vector<int32_t> s) {
+  std::vector<std::vector<int32_t>> strategies;
 
   auto axis_input = GetValue<int>(ops[iter_ops]->input_value().at(2));
   if (axis_input < 0) {
@@ -182,7 +185,7 @@ Strategys PrepareGatherV2(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
     return strategies;
   }
 
-  Dimensions s_indices;
+  std::vector<int32_t> s_indices;
   for (size_t i = 0; i < ops[iter_ops]->inputs_tensor_info()[1].shape().size(); i++) {
     s_indices.push_back(1);
   }
@@ -191,8 +194,8 @@ Strategys PrepareGatherV2(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
   return strategies;
 }
 
-Strategys PrepareL2Normalize(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const size_t iter_ops,
-                             Dimensions s) {
+std::vector<std::vector<int32_t>> PrepareL2Normalize(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                     const size_t iter_ops, std::vector<int32_t> s) {
   int32_t axis = 0;
   auto iter = ops[iter_ops]->attrs().find(AXIS);
   if (iter != ops[iter_ops]->attrs().end()) {
@@ -212,14 +215,14 @@ Strategys PrepareL2Normalize(const std::vector<std::shared_ptr<OperatorInfo>> &o
 
   s[IntToSize(axis_index)] = 1;
 
-  Strategys strategies;
+  std::vector<std::vector<int32_t>> strategies;
   strategies.push_back(s);
   return strategies;
 }
 
-Strategys MakeRecSearchStrategy(const std::shared_ptr<Graph> &graph,
-                                const std::vector<std::shared_ptr<OperatorInfo>> &ops, const size_t iter_graph,
-                                const size_t iter_ops) {
+std::vector<std::vector<int32_t>> MakeRecSearchStrategy(const std::shared_ptr<Graph> &graph,
+                                                        const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                        const size_t iter_graph, const size_t iter_ops) {
   if (ops.empty()) {
     MS_LOG(EXCEPTION) << "Failure: Operators is empty.";
   }
@@ -228,31 +231,31 @@ Strategys MakeRecSearchStrategy(const std::shared_ptr<Graph> &graph,
   }
 
   StrategyPtr origin_strategy = ops[iter_ops]->strategy();
-  Strategys strategies;
+  std::vector<std::vector<int32_t>> strategies;
   for (size_t iter_op_inputs = 0; iter_op_inputs < ops[iter_ops]->inputs_tensor_info().size(); iter_op_inputs++) {
     if (iter_op_inputs >= origin_strategy->GetInputDim().size()) {
       MS_LOG(EXCEPTION) << "Failure: Strategy's InputDim out of range.";
     }
 
     size_t output_size = origin_strategy->GetInputDim()[iter_op_inputs].size();
-    Dimensions s;
+    std::vector<int32_t> s;
     if (output_size == 4) {
       s.push_back(
-        static_cast<int64_t>(1.0 / graph->nodes[iter_graph].apply.arguments[iter_op_inputs].tensor_str.str_n));
+        static_cast<int32_t>(1.0 / graph->nodes[iter_graph].apply.arguments[iter_op_inputs].tensor_str.str_n));
       s.push_back(
-        static_cast<int64_t>(1.0 / graph->nodes[iter_graph].apply.arguments[iter_op_inputs].tensor_str.str_c));
+        static_cast<int32_t>(1.0 / graph->nodes[iter_graph].apply.arguments[iter_op_inputs].tensor_str.str_c));
       s.push_back(
-        static_cast<int64_t>(1.0 / graph->nodes[iter_graph].apply.arguments[iter_op_inputs].tensor_str.str_h));
+        static_cast<int32_t>(1.0 / graph->nodes[iter_graph].apply.arguments[iter_op_inputs].tensor_str.str_h));
       s.push_back(
-        static_cast<int64_t>(1.0 / graph->nodes[iter_graph].apply.arguments[iter_op_inputs].tensor_str.str_w));
+        static_cast<int32_t>(1.0 / graph->nodes[iter_graph].apply.arguments[iter_op_inputs].tensor_str.str_w));
     } else if (output_size == 2) {
       s.push_back(
-        static_cast<int64_t>(1.0 / graph->nodes[iter_graph].apply.arguments[iter_op_inputs].tensor_str.str_h));
+        static_cast<int32_t>(1.0 / graph->nodes[iter_graph].apply.arguments[iter_op_inputs].tensor_str.str_h));
       s.push_back(
-        static_cast<int64_t>(1.0 / graph->nodes[iter_graph].apply.arguments[iter_op_inputs].tensor_str.str_w));
+        static_cast<int32_t>(1.0 / graph->nodes[iter_graph].apply.arguments[iter_op_inputs].tensor_str.str_w));
     } else if (output_size == 1) {
       s.push_back(
-        static_cast<int64_t>(1.0 / graph->nodes[iter_graph].apply.arguments[iter_op_inputs].tensor_str.str_w));
+        static_cast<int32_t>(1.0 / graph->nodes[iter_graph].apply.arguments[iter_op_inputs].tensor_str.str_w));
     } else if (output_size == 0) {
       s = {};
     } else {
@@ -263,9 +266,9 @@ Strategys MakeRecSearchStrategy(const std::shared_ptr<Graph> &graph,
   return strategies;
 }
 
-Strategys MakeDataParallelStrategy(const std::shared_ptr<Graph> &graph,
-                                   const std::vector<std::shared_ptr<OperatorInfo>> &ops, const size_t iter_graph,
-                                   const size_t iter_ops) {
+std::vector<std::vector<int32_t>> MakeDataParallelStrategy(const std::shared_ptr<Graph> &graph,
+                                                           const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                           const size_t iter_graph, const size_t iter_ops) {
   if (ops.empty()) {
     MS_LOG(EXCEPTION) << "Failure: Operators is empty.";
   }
@@ -274,7 +277,7 @@ Strategys MakeDataParallelStrategy(const std::shared_ptr<Graph> &graph,
   }
 
   StrategyPtr origin_strategy = ops[iter_ops]->strategy();
-  Strategys strategies;
+  std::vector<std::vector<int32_t>> strategies;
   size_t max_device_num = g_device_manager->DeviceNum();
   size_t target_tensor_batch = ops[iter_ops]->inputs_tensor_info()[0].shape()[0];
   for (size_t iter_op_inputs = 0; iter_op_inputs < ops[iter_ops]->inputs_tensor_info().size(); iter_op_inputs++) {
@@ -282,7 +285,7 @@ Strategys MakeDataParallelStrategy(const std::shared_ptr<Graph> &graph,
       MS_LOG(EXCEPTION) << "Failure: Strategy's InputDim out of range.";
     }
 
-    Dimensions s;
+    std::vector<int32_t> s;
     size_t input_size = origin_strategy->GetInputDim()[iter_op_inputs].size();
     for (size_t dim = 0; dim < input_size; dim++) {
       if (input_size == 1 || input_size == 2 || input_size == 4) {
@@ -315,8 +318,9 @@ Strategys MakeDataParallelStrategy(const std::shared_ptr<Graph> &graph,
   return strategies;
 }
 
-Strategys PrepareStrategy(const std::shared_ptr<Graph> &graph, const std::vector<std::shared_ptr<OperatorInfo>> &ops,
-                          const size_t iter_graph, const size_t iter_ops) {
+std::vector<std::vector<int32_t>> PrepareStrategy(const std::shared_ptr<Graph> &graph,
+                                                  const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                  const size_t iter_graph, const size_t iter_ops) {
   if (ops.empty()) {
     MS_LOG(EXCEPTION) << "Failure: Operators is empty.";
   }
@@ -344,7 +348,7 @@ void GeneratePartitionedOperatorStrategy(const std::shared_ptr<Graph> &graph,
                                          const std::vector<std::shared_ptr<OperatorInfo>> &ops,
                                          const std::shared_ptr<std::vector<size_t>> &index_list) {
   for (size_t iter_ops = 0; iter_ops < (size_t)index_list->size(); iter_ops++) {
-    Strategys strategies;
+    std::vector<std::vector<int32_t>> strategies;
     size_t iter_graph = index_list->at(iter_ops);
     if (iter_graph != SIZE_MAX && ops[iter_ops]->type() != GET_NEXT) {
       strategies = PrepareStrategy(graph, ops, iter_graph, iter_ops);
@@ -371,10 +375,10 @@ size_t FindIndexOfOperatorIncoming(const std::vector<std::vector<std::string>> &
   return incoming_op_index;
 }
 
-Dimensions CopyIncomingOperatorOutputStrategy(const std::shared_ptr<Graph> &graph,
-                                              const std::vector<std::shared_ptr<OperatorInfo>> &ops,
-                                              const size_t iter_ops, const size_t iter_graph) {
-  Dimensions s;
+std::vector<int32_t> CopyIncomingOperatorOutputStrategy(const std::shared_ptr<Graph> &graph,
+                                                        const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                        const size_t iter_ops, const size_t iter_graph) {
+  std::vector<int32_t> s;
   for (auto input : ops[iter_ops]->inputs_tensor_info()) {
     auto input_stra_dim = input.shape().size();
     if (input_stra_dim == 0) {
@@ -398,9 +402,9 @@ Dimensions CopyIncomingOperatorOutputStrategy(const std::shared_ptr<Graph> &grap
   return s;
 }
 
-Dimensions PrepareIncomingOperatorInputStrategy(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
-                                                const size_t incoming_op_index) {
-  Dimensions s;
+std::vector<int32_t> PrepareIncomingOperatorInputStrategy(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                          const size_t incoming_op_index) {
+  std::vector<int32_t> s;
   if (ops[incoming_op_index]->type() == RESHAPE || ops[incoming_op_index]->type() == GATHERV2 ||
       ops[incoming_op_index]->type() == TRANSPOSE) {
     return s;
@@ -422,8 +426,8 @@ Dimensions PrepareIncomingOperatorInputStrategy(const std::vector<std::shared_pt
   return s;
 }
 
-Dimensions GetAxisList(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const int iter_ops) {
-  Dimensions axis_list;
+std::vector<int32_t> GetAxisList(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const int iter_ops) {
+  std::vector<int32_t> axis_list;
   auto axis_param = ops[iter_ops]->attrs().find(AXIS)->second;
   std::vector<ValuePtr> elements;
   if (axis_param->isa<ValueTuple>()) {
@@ -444,10 +448,10 @@ Dimensions GetAxisList(const std::vector<std::shared_ptr<OperatorInfo>> &ops, co
   return axis_list;
 }
 
-Dimensions ModifyStrategyIfSqueezeIncoming(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
-                                           const size_t incoming_op_index, Dimensions s) {
-  Dimensions s_Squeeze;
-  Dimensions stra_dim_list;
+std::vector<int32_t> ModifyStrategyIfSqueezeIncoming(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                     const size_t incoming_op_index, std::vector<int32_t> s) {
+  std::vector<int32_t> s_Squeeze;
+  std::vector<int32_t> stra_dim_list;
   for (size_t i = 0; i < s.size(); i++) {
     stra_dim_list.push_back(i);
   }
@@ -484,8 +488,8 @@ bool GetKeepDims(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const si
   return keepdims;
 }
 
-Dimensions GetDimList(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const size_t iter_ops) {
-  Dimensions dim_list;
+std::vector<int32_t> GetDimList(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const size_t iter_ops) {
+  std::vector<int32_t> dim_list;
   bool keep_dims = GetKeepDims(ops, iter_ops);
   if (keep_dims != false) {
     return dim_list;
@@ -509,10 +513,10 @@ Dimensions GetDimList(const std::vector<std::shared_ptr<OperatorInfo>> &ops, con
   return dim_list;
 }
 
-Dimensions ModifyStrategyIfReduceIncoming(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
-                                          const size_t incoming_op_index, Dimensions s) {
-  Dimensions s_Reduce;
-  Dimensions axis_list;
+std::vector<int32_t> ModifyStrategyIfReduceIncoming(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                    const size_t incoming_op_index, std::vector<int32_t> s) {
+  std::vector<int32_t> s_Reduce;
+  std::vector<int32_t> axis_list;
   for (size_t i = 0; i < s.size(); i++) {
     axis_list.push_back(i);
   }
@@ -532,8 +536,8 @@ Dimensions ModifyStrategyIfReduceIncoming(const std::vector<std::shared_ptr<Oper
   return s_Reduce;
 }
 
-Dimensions GetDimListFromAttrs(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const size_t iter_ops) {
-  Dimensions dim_list;
+std::vector<int32_t> GetDimListFromAttrs(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const size_t iter_ops) {
+  std::vector<int32_t> dim_list;
   auto iter = ops[iter_ops]->attrs().find(AXIS);
   if (iter == ops[iter_ops]->attrs().end()) {
     MS_LOG(EXCEPTION) << ops[iter_ops]->name() << ": Don't have attr axis.";
@@ -560,15 +564,15 @@ Dimensions GetDimListFromAttrs(const std::vector<std::shared_ptr<OperatorInfo>>
   return dim_list;
 }
 
-Dimensions ModifyStrategyIfArgIncoming(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
-                                       const size_t incoming_op_index, Dimensions s) {
+std::vector<int32_t> ModifyStrategyIfArgIncoming(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                 const size_t incoming_op_index, std::vector<int32_t> s) {
   bool keepdims = GetKeepDims(ops, incoming_op_index);
   if (keepdims) {
     return s;
   }
 
-  Dimensions s_Arg;
-  Dimensions axis_list;
+  std::vector<int32_t> s_Arg;
+  std::vector<int32_t> axis_list;
   for (size_t i = 0; i < s.size(); i++) {
     axis_list.push_back(i);
   }
@@ -588,9 +592,9 @@ Dimensions ModifyStrategyIfArgIncoming(const std::vector<std::shared_ptr<Operato
   return s_Arg;
 }
 
-Dimensions CopyIncomingOperatorInputStrategy(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
-                                             const size_t iter_ops, const size_t incoming_op_index) {
-  Dimensions s;
+std::vector<int32_t> CopyIncomingOperatorInputStrategy(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                       const size_t iter_ops, const size_t incoming_op_index) {
+  std::vector<int32_t> s;
   s = PrepareIncomingOperatorInputStrategy(ops, incoming_op_index);
   if (s.size() != 0) {
     if (ops[incoming_op_index]->type() == SQUEEZE) {
@@ -607,9 +611,10 @@ Dimensions CopyIncomingOperatorInputStrategy(const std::vector<std::shared_ptr<O
   return s;
 }
 
-Strategys GenerateStrategiesFromStrategy(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const size_t iter_ops,
-                                         Dimensions basic_stra) {
-  Strategys stra;
+std::vector<std::vector<int32_t>> GenerateStrategiesFromStrategy(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                                 const size_t iter_ops,
+                                                                 std::vector<int32_t> basic_stra) {
+  std::vector<std::vector<int32_t>> stra;
   MS_EXCEPTION_IF_NULL(ops[iter_ops]);
 
   if (basic_stra.size() == 0) {
@@ -620,7 +625,7 @@ Strategys GenerateStrategiesFromStrategy(const std::vector<std::shared_ptr<Opera
     return stra;
   }
 
-  auto s_ptr = std::make_shared<Dimensions>(basic_stra);
+  auto s_ptr = std::make_shared<std::vector<int32_t>>(basic_stra);
   if (ops[iter_ops]->type() == BIAS_ADD) {
     return PrepareBiasAdd(s_ptr);
   }
@@ -639,8 +644,9 @@ Strategys GenerateStrategiesFromStrategy(const std::vector<std::shared_ptr<Opera
 }
 
 // Function to deal with ops with broadcasting, like TensorAdd/Sub/Mul/Div etc.
-Strategys CheckBroadcast(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const size_t iter_ops, Dimensions s) {
-  Strategys stra;
+std::vector<std::vector<int32_t>> CheckBroadcast(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                 const size_t iter_ops, std::vector<int32_t> s) {
+  std::vector<std::vector<int32_t>> stra;
 
   size_t first_tensor_dim = ops[iter_ops]->inputs_tensor_info()[0].shape().size();
   size_t second_tensor_dim = ops[iter_ops]->inputs_tensor_info()[1].shape().size();
@@ -665,10 +671,11 @@ Strategys CheckBroadcast(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
   return stra;
 }
 
-Dimensions ApplyBroadcast(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const size_t iter_ops, Dimensions s,
-                          size_t target_tensor_dim, size_t refer_tensor_dim, bool braoadcast_first_tensor) {
-  Dimensions s_empty = {};
-  Dimensions s_broadcast;
+std::vector<int32_t> ApplyBroadcast(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const size_t iter_ops,
+                                    std::vector<int32_t> s, size_t target_tensor_dim, size_t refer_tensor_dim,
+                                    bool braoadcast_first_tensor) {
+  std::vector<int32_t> s_empty = {};
+  std::vector<int32_t> s_broadcast;
   int target_tensor_index = 0;
   int refer_tensor_index = 0;
 
@@ -712,10 +719,10 @@ Dimensions ApplyBroadcast(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
 }
 
 // Check whether the operator can be divided by the current strategy.
-Strategys CheckDivisible(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const size_t iter_ops,
-                         Dimensions basic_stra) {
-  Dimensions s_empty = {};
-  Strategys stra;
+std::vector<std::vector<int32_t>> CheckDivisible(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                 const size_t iter_ops, std::vector<int32_t> basic_stra) {
+  std::vector<int32_t> s_empty = {};
+  std::vector<std::vector<int32_t>> stra;
 
   // For all the input tensors.
   for (size_t iter_op_inputs = 0; iter_op_inputs < (size_t)ops[iter_ops]->inputs_tensor_info().size();
@@ -726,7 +733,7 @@ Strategys CheckDivisible(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
       continue;
     }
 
-    Dimensions tmp_stra = basic_stra;
+    std::vector<int32_t> tmp_stra = basic_stra;
     bool modified = false;
 
     // Make sure each tensor's dim shape is greater than 1. If not, push back strategy as 1 instead.
@@ -758,8 +765,8 @@ void GenerateEliminatedOperatorStrategyForward(const std::shared_ptr<Graph> &gra
 
   for (size_t iter_list = no_stra_op_list->size(); iter_list > 0; iter_list--) {
     size_t iter_ops = no_stra_op_list->at(iter_list - 1);
-    Strategys stra;
-    Dimensions s;
+    std::vector<std::vector<int32_t>> stra;
+    std::vector<int32_t> s;
     size_t incoming_op_index = FindIndexOfOperatorIncoming(input_tensor_names, iter_ops);
     if (incoming_op_index != SIZE_MAX) {
       auto iter_graph = index_list->at(incoming_op_index);
@@ -786,9 +793,9 @@ void GenerateEliminatedOperatorStrategyForward(const std::shared_ptr<Graph> &gra
   }
 }
 
-Dimensions ModifyStrategyIfSqueezeOutgoing(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const size_t iter_ops,
-                                           Dimensions s) {
-  Dimensions s_Squeeze;
+std::vector<int32_t> ModifyStrategyIfSqueezeOutgoing(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                     const size_t iter_ops, std::vector<int32_t> s) {
+  std::vector<int32_t> s_Squeeze;
   auto axis_list = GetAxisList(ops, iter_ops);
   size_t s_index = 0;
   size_t axis_list_index = 0;
@@ -813,10 +820,10 @@ Dimensions ModifyStrategyIfSqueezeOutgoing(const std::vector<std::shared_ptr<Ope
   return s_Squeeze;
 }
 
-Dimensions CopyOutgoingOperatorInputStrategy(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
-                                             const std::vector<std::vector<std::string>> &input_tensor_names,
-                                             const size_t iter_ops) {
-  Dimensions s;
+std::vector<int32_t> CopyOutgoingOperatorInputStrategy(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                       const std::vector<std::vector<std::string>> &input_tensor_names,
+                                                       const size_t iter_ops) {
+  std::vector<int32_t> s;
   if (ops[iter_ops]->type() == REDUCE_MAX || ops[iter_ops]->type() == REDUCE_MIN ||
       ops[iter_ops]->type() == REDUCE_SUM || ops[iter_ops]->type() == REDUCE_MEAN || ops[iter_ops]->type() == RESHAPE ||
       ops[iter_ops]->type() == GATHERV2 || ops[iter_ops]->type() == TRANSPOSE ||
@@ -860,8 +867,8 @@ void GenerateEliminatedOperatorStrategyBackward(const std::vector<std::shared_pt
 
   for (size_t iter_list = no_stra_op_list->size(); iter_list > 0; iter_list--) {
     auto iter_ops = no_stra_op_list->at(iter_list - 1);
-    Strategys stra;
-    Dimensions s = CopyOutgoingOperatorInputStrategy(ops, input_tensor_names, iter_ops);
+    std::vector<std::vector<int32_t>> stra;
+    std::vector<int32_t> s = CopyOutgoingOperatorInputStrategy(ops, input_tensor_names, iter_ops);
 
     if (s.size() != 0 && ops[iter_ops]->type() == SQUEEZE) {
       s = ModifyStrategyIfSqueezeOutgoing(ops, iter_ops, s);
@@ -900,8 +907,8 @@ void GenerateRemainingOperatorStrategy(const std::shared_ptr<Graph> &graph,
 
   for (size_t iter_list = 0; iter_list < no_stra_op_list->size(); iter_list++) {
     auto iter_ops = no_stra_op_list->at(iter_list);
-    Strategys stra;
-    Dimensions s;
+    std::vector<std::vector<int32_t>> stra;
+    std::vector<int32_t> s;
 
     size_t max_dim_num = 0;
     for (size_t iter_op_inputs = 0; iter_op_inputs < ops[iter_ops]->inputs_tensor_info().size(); iter_op_inputs++) {

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

@@ -31,60 +31,68 @@ void GenerateStrategy(const std::shared_ptr<Graph> &graph, const std::vector<std
                       const std::shared_ptr<std::vector<std::vector<size_t>>> &eli_list,
                       const std::vector<std::vector<std::string>> &input_tensor_names,
                       const std::shared_ptr<std::vector<size_t>> &index_list);
-Strategys PrepareMatMul(const std::shared_ptr<Graph> &graph, const std::vector<std::shared_ptr<OperatorInfo>> &ops,
-                        const size_t iter_graph, const size_t iter_ops);
-Strategys PrepareBiasAdd(const std::shared_ptr<Dimensions> &s);
-Strategys PrepareOneHot(const std::shared_ptr<Graph> &graph, const std::vector<std::shared_ptr<OperatorInfo>> &ops,
-                        const size_t iter_graph, const size_t iter_ops);
-Strategys PrepareGatherV2(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const size_t iter_ops, Dimensions s);
-Strategys PrepareL2Normalize(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const size_t iter_ops,
-                             Dimensions s);
-Strategys MakeRecSearchStrategy(const std::shared_ptr<Graph> &graph,
-                                const std::vector<std::shared_ptr<OperatorInfo>> &ops, const size_t iter_graph,
-                                const size_t iter_ops);
-Strategys CheckBroadcast(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const size_t iter_ops, Dimensions s);
-Dimensions ApplyBroadcast(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const size_t iter_ops, Dimensions s,
-                          size_t target_tensor_dim, size_t refer_tensor_dim, bool braoadcast_first_tensor);
-Strategys CheckDivisible(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const size_t iter_ops, Dimensions s);
-Strategys MakeDataParallelStrategy(const std::shared_ptr<Graph> &graph,
-                                   const std::vector<std::shared_ptr<OperatorInfo>> &ops, const size_t iter_graph,
-                                   const size_t iter_ops);
-Strategys PrepareStrategy(const std::shared_ptr<Graph> &graph, const std::vector<std::shared_ptr<OperatorInfo>> &ops,
-                          const size_t iter_graph, const size_t iter_ops);
+std::vector<std::vector<int32_t>> PrepareMatMul(const std::shared_ptr<Graph> &graph,
+                                                const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                const size_t iter_graph, const size_t iter_ops);
+std::vector<std::vector<int32_t>> PrepareBiasAdd(const std::shared_ptr<std::vector<int32_t>> &s);
+std::vector<std::vector<int32_t>> PrepareOneHot(const std::shared_ptr<Graph> &graph,
+                                                const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                const size_t iter_graph, const size_t iter_ops);
+std::vector<std::vector<int32_t>> PrepareGatherV2(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                  const size_t iter_ops, std::vector<int32_t> s);
+std::vector<std::vector<int32_t>> PrepareL2Normalize(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                     const size_t iter_ops, std::vector<int32_t> s);
+std::vector<std::vector<int32_t>> CheckBroadcast(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                 const size_t iter_ops, std::vector<int32_t> s);
+std::vector<int32_t> ApplyBroadcast(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const size_t iter_ops,
+                                    std::vector<int32_t> s, size_t target_tensor_dim, size_t refer_tensor_dim,
+                                    bool braoadcast_first_tensor);
+std::vector<std::vector<int32_t>> CheckDivisible(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                 const size_t iter_ops, std::vector<int32_t> s);
+std::vector<std::vector<int32_t>> MakeRecSearchStrategy(const std::shared_ptr<Graph> &graph,
+                                                        const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                        const size_t iter_graph, const size_t iter_ops);
+std::vector<std::vector<int32_t>> MakeDataParallelStrategy(const std::shared_ptr<Graph> &graph,
+                                                           const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                           const size_t iter_graph, const size_t iter_ops);
+std::vector<std::vector<int32_t>> PrepareStrategy(const std::shared_ptr<Graph> &graph,
+                                                  const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                  const size_t iter_graph, const size_t iter_ops);
 void GeneratePartitionedOperatorStrategy(const std::shared_ptr<Graph> &graph,
                                          const std::vector<std::shared_ptr<OperatorInfo>> &ops,
                                          const std::shared_ptr<std::vector<size_t>> &index_list);
 size_t FindIndexOfOperatorIncoming(const std::vector<std::vector<std::string>> &input_tensor_names,
                                    const size_t iter_ops);
-Dimensions CopyIncomingOperatorOutputStrategy(const std::shared_ptr<Graph> &graph,
-                                              const std::vector<std::shared_ptr<OperatorInfo>> &ops,
-                                              const size_t iter_ops, const size_t iter_graph);
-Dimensions PrepareIncomingOperatorInputStrategy(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
-                                                const size_t incoming_op_index);
-Dimensions GetAxisList(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const int iter_ops);
-Dimensions ModifyStrategyIfSqueezeIncoming(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
-                                           const size_t incoming_op_index, Dimensions s);
+std::vector<int32_t> CopyIncomingOperatorOutputStrategy(const std::shared_ptr<Graph> &graph,
+                                                        const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                        const size_t iter_ops, const size_t iter_graph);
+std::vector<int32_t> PrepareIncomingOperatorInputStrategy(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                          const size_t incoming_op_index);
+std::vector<int32_t> GetAxisList(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const int iter_ops);
+std::vector<int32_t> ModifyStrategyIfSqueezeIncoming(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                     const size_t incoming_op_index, std::vector<int32_t> s);
 bool GetKeepDims(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const size_t iter_ops);
-Dimensions GetDimList(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const size_t iter_ops);
-Dimensions ModifyStrategyIfReduceIncoming(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
-                                          const size_t incoming_op_index, Dimensions s);
-Dimensions GetDimListFromAttrs(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const size_t iter_ops);
-Dimensions ModifyStrategyIfArgIncoming(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
-                                       const size_t incoming_op_index, Dimensions s);
-Dimensions CopyIncomingOperatorInputStrategy(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
-                                             const size_t iter_ops, const size_t incoming_op_index);
-Strategys GenerateStrategiesFromStrategy(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const size_t iter_ops,
-                                         Dimensions basic_stra);
+std::vector<int32_t> GetDimList(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const size_t iter_ops);
+std::vector<int32_t> ModifyStrategyIfReduceIncoming(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                    const size_t incoming_op_index, std::vector<int32_t> s);
+std::vector<int32_t> GetDimListFromAttrs(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const size_t iter_ops);
+std::vector<int32_t> ModifyStrategyIfArgIncoming(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                 const size_t incoming_op_index, std::vector<int32_t> s);
+std::vector<int32_t> CopyIncomingOperatorInputStrategy(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                       const size_t iter_ops, const size_t incoming_op_index);
+std::vector<std::vector<int32_t>> GenerateStrategiesFromStrategy(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                                 const size_t iter_ops,
+                                                                 std::vector<int32_t> basic_stra);
 void GenerateEliminatedOperatorStrategyForward(const std::shared_ptr<Graph> &graph,
                                                const std::vector<std::shared_ptr<OperatorInfo>> &ops,
                                                const std::vector<std::vector<std::string>> &input_tensor_names,
                                                const std::shared_ptr<std::vector<size_t>> &index_list,
                                                const std::shared_ptr<std::vector<size_t>> &no_stra_op_list);
-Dimensions ModifyStrategyIfSqueezeOutgoing(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const size_t iter_ops,
-                                           Dimensions s);
-Dimensions CopyOutgoingOperatorInputStrategy(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
-                                             const std::vector<std::vector<std::string>> &input_tensor_names,
-                                             const size_t iter_ops);
+std::vector<int32_t> ModifyStrategyIfSqueezeOutgoing(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                     const size_t iter_ops, std::vector<int32_t> s);
+std::vector<int32_t> CopyOutgoingOperatorInputStrategy(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                       const std::vector<std::vector<std::string>> &input_tensor_names,
+                                                       const size_t iter_ops);
 void GenerateEliminatedOperatorStrategyBackward(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
                                                 const std::vector<std::vector<std::string>> &input_tensor_names,
                                                 const std::shared_ptr<std::vector<size_t>> &no_stra_op_list);

mindspore/ccsrc/frontend/parallel/context.cc

@@ -29,7 +29,7 @@
 
 namespace mindspore {
 namespace parallel {
-static std::map<std::string, Shape> param_shapes;
+static std::map<std::string, std::vector<int>> param_shapes;
 
 std::vector<std::string> PARALLEL_MODE_LIST = {STAND_ALONE, DATA_PARALLEL, HYBRID_PARALLEL, SEMI_AUTO_PARALLEL,
                                                AUTO_PARALLEL};
@@ -173,7 +173,7 @@ void ParallelParameterContextRestoreInNoTraining(const FuncGraphPtr &func_graph,
     MS_LOG(WARNING) << "Can not found the shape for parameter " << param_node->name();
     return;
   }
-  Shape shape = iter->second;
+  std::vector<int> shape = iter->second;
   std::shared_ptr<abstract::BaseShape> base_shape = std::make_shared<abstract::Shape>(shape);
   ptr->set_shape(base_shape);
   MS_LOG(DEBUG) << "The parameter name is " << param_node->name() << ", the shape is " << shape;
@@ -189,10 +189,7 @@ void ParallelParameterContextCkptInTraining(const FuncGraphPtr &func_graph, cons
     return;
   }
 
-  std::vector<int> shape_int = dyn_cast<abstract::Shape>(ptr->GetShapeTrack())->shape();
-  Shape shape;
-  (void)std::transform(shape_int.begin(), shape_int.end(), std::back_inserter(shape),
-                       [](const int &value) { return static_cast<int64_t>(value); });
+  std::vector<int> shape = dyn_cast<abstract::Shape>(ptr->GetShapeTrack())->shape();
   auto ret = param_shapes.try_emplace(param_node->name(), shape);
   if (!ret.second) {
     MS_LOG(EXCEPTION) << "The shape for parameter name " << param_node->name() << " is existed";

mindspore/ccsrc/frontend/parallel/device_matrix.cc

@@ -159,7 +159,7 @@ std::string ShapeToString(const Shape &shape) {
   return str + "]";
 }
 
-std::string ListToString(const RankList &list) {
+std::string ListToString(const std::vector<int32_t> &list) {
   std::string str = "[";
   for (auto &element : list) {
     str += std::to_string(element) + ", ";

mindspore/ccsrc/frontend/parallel/device_matrix.h

@@ -27,7 +27,7 @@
 namespace mindspore {
 namespace parallel {
 using RankList = std::vector<int32_t>;
-using Shape = std::vector<int64_t>;
+using Shape = std::vector<int32_t>;
 
 class DeviceMatrix {
  public:
@@ -48,7 +48,7 @@ class DeviceMatrix {
 };
 
 std::string ShapeToString(const Shape &shape);
-std::string ListToString(const RankList &list);
+std::string ListToString(const std::vector<int32_t> &list);
 }  // namespace parallel
 }  // namespace mindspore
 

mindspore/ccsrc/frontend/parallel/graph_util/get_parallel_info.cc

@@ -45,13 +45,13 @@ py::dict GetParameterLayout(const FuncGraphPtr &graph) {
       auto tensor_map = tensor_layout->tensor_map().array();
       auto slice_shape = tensor_layout->slice_shape().array();
       int32_t _field_size = tensor_layout->get_field_size();
-      Shape field_size;
+      std::vector<int32_t> field_size;
       if (_field_size != 0) {
         field_size.push_back(_field_size);
       } else {
         field_size = {0};
       }
-      std::vector<Shape> layout = {device_arrangement, tensor_map, slice_shape, field_size};
+      std::vector<std::vector<int32_t>> layout = {device_arrangement, tensor_map, slice_shape, field_size};
       dict[py::str(name)] = layout;
       MS_LOG(INFO) << "GetParameterLayout name = " << name << ", layout " << tensor_layout->ToString();
     }

mindspore/ccsrc/frontend/parallel/ops_info/activation_info.cc

@@ -130,7 +130,7 @@ Status Softmax::CheckStrategy(const StrategyPtr &strategy) {
     return FAILED;
   }
 
-  Strategys stra = strategy->GetInputDim();
+  std::vector<Dimensions> stra = strategy->GetInputDim();
   Dimensions input_strategy = stra.at(0);
 
   for (auto &element : axis_) {
@@ -181,7 +181,7 @@ Status Softmax::GetAttrs() {
         MS_LOG(ERROR) << name_ << " : The axis tuple is empty.";
         return FAILED;
       }
-      MS_LOG(INFO) << name_ << " : The axis is tuple, value is " << ListToString(axis_);
+      MS_LOG(INFO) << name_ << " : The axis is tuple, value is " << ShapeToString(axis_);
     } else {
       MS_LOG(ERROR) << name_ << " : The value of axis is not int or tuple int.";
       return FAILED;
@@ -258,7 +258,7 @@ Status Softmax::GenerateStrategies(int32_t stage_id) {
 }
 
 Status ActivationBase::InferDevMatrixShape() {
-  Strategys stra = strategy_->GetInputDim();
+  std::vector<Dimensions> stra = strategy_->GetInputDim();
   Dimensions input_strategy = stra.at(0);
 
   dev_matrix_shape_ = input_strategy;
@@ -296,11 +296,11 @@ Status ActivationBase::InferForwardCommunication() {
 }
 
 Status ActivationBase::InferTensorMap() {
-  Shape tensor_map_index;
+  std::vector<int32_t> tensor_map_index;
   size_t size = inputs_shape_.at(0).size();
   // such as 4: tensor_map_index [3,2,1,0]
   for (size_t i = 0; i < size; ++i) {
-    tensor_map_index.push_back((int64_t)(size - i - 1));
+    tensor_map_index.push_back((int32_t)(size - i - 1));
   }
 
   inputs_tensor_map_.push_back(tensor_map_index);
@@ -425,7 +425,7 @@ Status ExpandDimsInfo::InferTensorMap() {
 
   // for example: if the dimension of input is 3, and the axis is 2,
   // then the input_tensor_map is [2, 1, 0], the output_tensor_map is [2, 1, -1, 0]
-  Shape input_tensor_map, output_tensor_map;
+  std::vector<int32_t> input_tensor_map, output_tensor_map;
   size_t size = inputs_shape_[0].size();
   for (size_t i = 0; i < size; ++i) {
     input_tensor_map.push_back(SizeToInt(size - i - 1));
@@ -607,7 +607,7 @@ Status SqueezeInfo::InferReplaceOps(const StrategyPtr &strategy) {
 Status SqueezeInfo::InferTensorMap() {
   // for example: if the shape of input is [32, 32, 1], and the axis is (2, ),
   // then the input_tensor_map is [2, 1, 0], the output_tensor_map is [2, 1]
-  Shape input_tensor_map, output_tensor_map;
+  std::vector<int32_t> input_tensor_map, output_tensor_map;
   if (inputs_shape_.empty()) {
     MS_LOG(ERROR) << name_ << ": The inputs shape is empty";
     return FAILED;

mindspore/ccsrc/frontend/parallel/ops_info/arithmetic_info.cc

@@ -54,9 +54,9 @@ Shapes ArithmeticBase::InferExpendShape() {
   return input_shapes;
 }
 
-Strategys ExpendStrategy(const StrategyPtr &strategy) {
-  Strategys expend_strategy;
-  Strategys stra = strategy->GetInputDim();
+std::vector<Dimensions> ExpendStrategy(const StrategyPtr &strategy) {
+  std::vector<Dimensions> expend_strategy;
+  std::vector<Dimensions> stra = strategy->GetInputDim();
   Dimensions sub_a_strategy = stra.at(0);
   Dimensions sub_b_strategy = stra.at(1);
   size_t input_a_size = sub_a_strategy.size();
@@ -83,7 +83,7 @@ Status ArithmeticBase::CheckStrategy(const StrategyPtr &strategy) {
     return FAILED;
   }
   Shapes input_shapes = InferExpendShape();
-  Strategys expend_strategy = ExpendStrategy(strategy);
+  std::vector<Dimensions> expend_strategy = ExpendStrategy(strategy);
   Dimensions sub_a_strategy = expend_strategy.at(0);
   Dimensions sub_b_strategy = expend_strategy.at(1);
   Shape input_a_shape = input_shapes.at(0);
@@ -103,7 +103,7 @@ Status ArithmeticBase::CheckStrategy(const StrategyPtr &strategy) {
 }
 
 Status ArithmeticBase::InferDevMatrixShape() {
-  Strategys expend_strategy = ExpendStrategy(strategy_);
+  std::vector<Dimensions> expend_strategy = ExpendStrategy(strategy_);
   Dimensions sub_a_strategy = expend_strategy.at(0);
   Dimensions sub_b_strategy = expend_strategy.at(1);
   Shape dev_shape;
@@ -123,7 +123,7 @@ TensorMap SetExpendTensorMap(const Shape &strategy, const Shape &dev_matrix_shap
   TensorMap tensor_map_index;
   for (size_t i = 0; i < strategy.size(); ++i) {
     if (strategy[i] == dev_matrix_shape[i]) {
-      tensor_map_index.push_back((int64_t)(LAST_INDEX(strategy.size()) - i));
+      tensor_map_index.push_back((int32_t)(LAST_INDEX(SizeToUint(strategy.size())) - i));
     } else {
       tensor_map_index.push_back(-1);
     }
@@ -159,15 +159,15 @@ void ArithmeticBase::ReComputeBatchSplitFlagList() {
 }
 
 Status ArithmeticBase::InferTensorMap() {
-  Shape tensor_map_index;
-  Strategys expend_strategy = ExpendStrategy(strategy_);
+  std::vector<int32_t> tensor_map_index;
+  std::vector<Dimensions> expend_strategy = ExpendStrategy(strategy_);
   Dimensions sub_a_expend_strategy = expend_strategy.at(0);
   Dimensions sub_b_expend_strategy = expend_strategy.at(1);
   Strategys stra = strategy_->GetInputDim();
   Dimensions sub_a_strategy = stra.at(0);
   Dimensions sub_b_strategy = stra.at(1);
   for (size_t i = 0; i < sub_a_expend_strategy.size(); ++i) {
-    tensor_map_index.push_back((int64_t)(LAST_INDEX(sub_a_expend_strategy.size()) - i));
+    tensor_map_index.push_back((int32_t)(LAST_INDEX(SizeToUint(sub_a_expend_strategy.size())) - i));
   }
 
   Shape dev_shape;
@@ -261,7 +261,7 @@ Status ArithmeticBase::InferTensorInfo() {
 
   // infer slice shape
   Shapes inputs_slice_shape, outputs_slice_shape;
-  Strategys expend_strategy = ExpendStrategy(strategy_);
+  std::vector<Dimensions> expend_strategy = ExpendStrategy(strategy_);
   Dimensions sub_a_expend_strategy = expend_strategy.at(0);
   Dimensions sub_b_expend_strategy = expend_strategy.at(1);
   Strategys inputs_strategy = strategy_->GetInputDim();

mindspore/ccsrc/frontend/parallel/ops_info/batch_parallel_info.cc

@@ -43,13 +43,13 @@ Status BatchParallelInfo::CheckStrategy(const StrategyPtr &strategy) {
   dev_num_ = dev_num;
 
   size_t strategy_size = strategy->GetInputNumber();
-  Strategys stra = strategy->GetInputDim();
+  std::vector<Dimensions> stra = strategy->GetInputDim();
   for (size_t i = 0; i < strategy_size; ++i) {
     Shape sub_strategy = stra.at(i);
     size_t strategy_len = sub_strategy.size();
     bool flag = false;
     for (size_t j = 0; j < strategy_len; ++j) {
-      int64_t strategy_value = sub_strategy.at(j);
+      int32_t strategy_value = sub_strategy.at(j);
       if (strategy_value > 1) {
         if (flag || strategy_value != dev_num_) {
           if (is_auto_parallel_) {
@@ -95,7 +95,7 @@ Status BatchParallelInfo::InferTensorMap() {
     return FAILED;
   }
   for (size_t i = 0; i < inputs_shape_.size(); i++) {
-    Shape tensor_map_index;
+    std::vector<int32_t> tensor_map_index;
     for (size_t j = 0; j < inputs_shape_[i].size(); ++j) {
       if (strategy_->GetInputDim()[i][j] == dev_num_ && j == 0) {
         tensor_map_index.push_back(0);
@@ -106,7 +106,7 @@ Status BatchParallelInfo::InferTensorMap() {
     inputs_tensor_map_.push_back(tensor_map_index);
   }
   for (size_t i = 0; i < outputs_shape_.size(); i++) {
-    Shape tensor_map_index;
+    std::vector<int32_t> tensor_map_index;
     for (size_t j = 0; j < outputs_shape_[i].size(); ++j) {
       if (i == 0 && j == 0) {
         tensor_map_index.push_back(0);
@@ -123,7 +123,7 @@ Strategys BatchParallelInfo::GetOutputsStrategy() {
   Strategys outputs_strategy;
 
   for (size_t i = 0; i < outputs_shape_.size(); ++i) {
-    Dimensions strategy;
+    std::vector<int32_t> strategy;
     for (size_t j = 0; j < outputs_shape_[i].size(); ++j) {
       if (i == 0 && j == 0) {
         strategy.push_back(dev_num_);
@@ -201,7 +201,7 @@ Status BatchParallelInfo::GenerateStrategies(int32_t stage_id) {
   is_auto_parallel_ = true;
   size_t total_dev_num = g_device_manager->GetDeviceListByStageId(stage_id).size();
   StrategyPtr sp;
-  Strategys strategy;
+  std::vector<Dimensions> strategy;
   for (size_t i = 0; i < inputs_shape_.size(); i++) {
     Shape temp(inputs_shape_[i].size(), 1);
     if (split_flag_list_[i]) {

mindspore/ccsrc/frontend/parallel/ops_info/bias_add_info.cc

@@ -36,11 +36,11 @@ Status BiasAddInfo::CheckStrategy(const StrategyPtr &strategy) {
     }
     return FAILED;
   }
-  Strategys stra = strategy->GetInputDim();
+  std::vector<Dimensions> stra = strategy->GetInputDim();
   Dimensions sub_a_strategy = stra.at(0);
   Dimensions sub_b_strategy = stra.at(1);
-  int64_t channel_a_strategy = sub_a_strategy.at(1);
-  int64_t channel_b_strategy = sub_b_strategy.at(0);
+  int32_t channel_a_strategy = sub_a_strategy.at(1);
+  int32_t channel_b_strategy = sub_b_strategy.at(0);
   if (channel_a_strategy != channel_b_strategy) {
     if (is_auto_parallel_) {
       MS_LOG(DEBUG) << name_ << " : Invalid strategy.";
@@ -53,7 +53,7 @@ Status BiasAddInfo::CheckStrategy(const StrategyPtr &strategy) {
 }
 
 Status BiasAddInfo::InferDevMatrixShape() {
-  Strategys stra = strategy_->GetInputDim();
+  std::vector<Dimensions> stra = strategy_->GetInputDim();
   Dimensions sub_a_strategy = stra.at(0);
   dev_matrix_shape_ = sub_a_strategy;
   return SUCCESS;
@@ -67,13 +67,13 @@ void BiasAddInfo::ReComputeBatchSplitFlagList() {
 Status BiasAddInfo::InferTensorMap() {
   TensorMap sub_a_tensor_map;
   TensorMap sub_b_tensor_map;
-  Strategys stra = strategy_->GetInputDim();
+  std::vector<Dimensions> stra = strategy_->GetInputDim();
   Dimensions sub_a_strategy = stra.at(0);
   size_t sub_a_strategy_size = sub_a_strategy.size();
   for (size_t i = 0; i < sub_a_strategy_size; ++i) {
-    sub_a_tensor_map.push_back((int32_t)(LAST_INDEX(sub_a_strategy_size) - i));
+    sub_a_tensor_map.push_back((int32_t)(LAST_INDEX(SizeToUint(sub_a_strategy_size)) - i));
   }
-  sub_b_tensor_map.push_back((int32_t)(LAST_INDEX(sub_a_strategy_size) - 1));
+  sub_b_tensor_map.push_back((int32_t)(LAST_INDEX(SizeToUint(sub_a_strategy_size)) - 1));
 
   inputs_tensor_map_.push_back(sub_a_tensor_map);
   inputs_tensor_map_.push_back(sub_b_tensor_map);
@@ -213,7 +213,7 @@ Status BiasAddInfo::GenerateStrategies(int32_t stage_id) {
   MS_LOG(INFO) << name_ << " : Generate strategies with broadcast success.";
 
   for (auto &sp : sp_vector) {
-    Strategys tmp_strategy;
+    std::vector<Dimensions> tmp_strategy;
     Dimensions input0_strategy = sp->GetInputDim()[0];
     tmp_strategy.push_back(input0_strategy);  // input0
 

mindspore/ccsrc/frontend/parallel/ops_info/dropout_do_mask_info.cc

@@ -38,7 +38,7 @@ Status DropoutDoMaskInfo::CheckStrategy(const StrategyPtr &strategy) {
     return FAILED;
   }
 
-  Strategys stra = strategy->GetInputDim();
+  std::vector<Dimensions> stra = strategy->GetInputDim();
   if (stra.size() != 1) {
     MS_LOG(ERROR) << name_ << ": Invalid strategy size " << stra.size() << ", it must be 1";
     return FAILED;
@@ -68,7 +68,7 @@ Status DropoutDoMaskInfo::InferDevMatrixShape() {
     return FAILED;
   }
 
-  Strategys strategy = strategy_->GetInputDim();
+  std::vector<Dimensions> strategy = strategy_->GetInputDim();
   if (strategy.empty()) {
     MS_LOG(ERROR) << name_ << ": The strategy is empty";
     return FAILED;
@@ -84,7 +84,7 @@ Status DropoutDoMaskInfo::InferTensorMap() {
     return FAILED;
   }
 
-  Shape tensor_map_index;
+  std::vector<int32_t> tensor_map_index;
   size_t size = inputs_shape_[0].size();
   // if the dimension of input is 4, and tensor_map_index is [3, 2, 1, 0]
   for (size_t i = 0; i < size; ++i) {
@@ -169,13 +169,13 @@ Status DropoutDoMaskInfo::GenerateStrategies(int32_t stage_id) {
   return SUCCESS;
 }
 
-std::shared_ptr<Strategys> DropoutDoMaskInfo::GenerateBatchStrategies() {
+std::shared_ptr<std::vector<std::vector<int32_t>>> DropoutDoMaskInfo::GenerateBatchStrategies() {
   CheckGlobalDeviceManager();
   size_t dev_num = g_device_manager->GetDeviceListByStageId(0).size();
   Dimensions strategy(inputs_shape_[0].size() - 1, 1);
   (void)strategy.insert(strategy.begin(), SizeToInt(dev_num));
-  Strategys strategy_v = {strategy};
-  return std::make_shared<Strategys>(strategy_v);
+  std::vector<Dimensions> strategy_v = {strategy};
+  return std::make_shared<std::vector<std::vector<int32_t>>>(strategy_v);
 }
 
 Status DropoutDoMaskInfo::Init(const StrategyPtr &strategy) {

mindspore/ccsrc/frontend/parallel/ops_info/dropout_do_mask_info.h

@@ -40,7 +40,7 @@ class DropoutDoMaskInfo : public OperatorInfo {
   Status GenerateStrategies(int32_t stage_id) override;
   Status SetCostUnderStrategy(const StrategyPtr &strategy) override;
   Status InitForCostModel(const StrategyPtr &strategy) override;
-  std::shared_ptr<Strategys> GenerateBatchStrategies() override;
+  std::shared_ptr<std::vector<std::vector<int32_t>>> GenerateBatchStrategies() override;
   std::vector<Operator> GetDropoutGenMaskReplaceOp(const CNodePtr &cnode);
 
  protected:

mindspore/ccsrc/frontend/parallel/ops_info/gather_v2_info.cc

@@ -109,7 +109,7 @@ Status GatherV2Info::CheckStrategy(const StrategyPtr &strategy) {
 }
 
 Status GatherV2Info::InferDevMatrixShape() {
-  Strategys stra = strategy_->GetInputDim();
+  std::vector<Dimensions> stra = strategy_->GetInputDim();
   dev_matrix_shape_ = stra.at(0);
   return SUCCESS;
 }
@@ -129,8 +129,8 @@ Status GatherV2Info::InferTensorMap() {
                   << outputs_shape_.size();
     return FAILED;
   }
-  Shape tensor_map_in;
-  Shape tensor_map_out;
+  std::vector<int32_t> tensor_map_in;
+  std::vector<int32_t> tensor_map_out;
   size_t size = inputs_shape_.at(0).size();
   // such as 4: tensor_map_index [3,2,1,0]
   for (size_t i = 0; i < size; ++i) {
@@ -149,7 +149,7 @@ Status GatherV2Info::InferTensorMap() {
     return FAILED;
   }
 
-  Shape tensor_map_in_index;
+  std::vector<int32_t> tensor_map_in_index;
   if (index_size_ >= 1) {
     tensor_map_in_index.push_back(SizeToInt(size - axis_ - 1));
   }
@@ -323,7 +323,7 @@ Status GatherV2Info::SetCostUnderStrategy(const StrategyPtr &strategy) {
   return SUCCESS;
 }
 
-std::shared_ptr<Strategys> GatherV2Info::GenerateBatchStrategies() {
+std::shared_ptr<std::vector<std::vector<int32_t>>> GatherV2Info::GenerateBatchStrategies() {
   if (inputs_shape_.size() != GATHER_V2_INPUTS_SIZE) {
     MS_LOG(EXCEPTION) << name_ << ": inputs shape size must be " << GATHER_V2_INPUTS_SIZE << ", but is "
                       << inputs_shape_.size();
@@ -343,8 +343,8 @@ std::shared_ptr<Strategys> GatherV2Info::GenerateBatchStrategies() {
   for (size_t i = 1; i < inputs_shape_[0].size(); i++) {
     strategy.push_back(1);
   }
-  Strategys strategy_v = {strategy};
-  return std::make_shared<Strategys>(strategy_v);
+  std::vector<Dimensions> strategy_v = {strategy};
+  return std::make_shared<std::vector<std::vector<int32_t>>>(strategy_v);
 }
 }  // namespace parallel
 }  // namespace mindspore

mindspore/ccsrc/frontend/parallel/ops_info/gather_v2_info.h

@@ -50,7 +50,7 @@ class GatherV2Info : public OperatorInfo {
 
   Status GenerateStrategies(int32_t stage_id) override;
   Status SetCostUnderStrategy(const StrategyPtr &strategy) override;
-  std::shared_ptr<Strategys> GenerateBatchStrategies() override;
+  std::shared_ptr<std::vector<std::vector<int32_t>>> GenerateBatchStrategies() override;
 
  protected:
   Status CheckStrategy(const StrategyPtr &strategy) override;

mindspore/ccsrc/frontend/parallel/ops_info/gather_v2_p_info.cc

@@ -73,8 +73,8 @@ Status GatherV2PInfo::GetAttrs() {
             MS_LOG(ERROR) << "Failure: Size of manual_split element must be 2.";
             return FAILED;
           }
-          param_split_shapes_.push_back(static_cast<int64_t>(GetValue<int>(value_vector[0])));
-          index_offsets_.push_back(static_cast<int64_t>(GetValue<int>(value_vector[1])));
+          param_split_shapes_.push_back(static_cast<int32_t>(GetValue<int>(value_vector[0])));
+          index_offsets_.push_back(static_cast<int32_t>(GetValue<int>(value_vector[1])));
         } else {
           MS_LOG(ERROR) << "Failure: Manual split strategy's format is wrong! Need ValueSequeue";
           return FAILED;
@@ -93,14 +93,14 @@ Status GatherV2PInfo::GetAttrs() {
 
 Status GatherV2PInfo::CheckManualSplit() {
   auto param_shape = inputs_shape_.at(0);
-  int64_t split_shape_sum = std::accumulate(param_split_shapes_.begin(), param_split_shapes_.end(), 0,
-                                            [](int64_t s, int64_t shape) { return s + shape; });
+  int32_t split_shape_sum = std::accumulate(param_split_shapes_.begin(), param_split_shapes_.end(), 0,
+                                            [](int32_t s, int32_t shape) { return s + shape; });
   if (split_shape_sum < param_shape.at(0)) {
     MS_LOG(ERROR) << "Failure: Sum of splited shapes should not be smaller than param_shape.";
     return FAILED;
   }
 
-  if (std::any_of(index_offsets_.begin(), index_offsets_.end(), [](const int64_t &offset) { return offset < 0; })) {
+  if (std::any_of(index_offsets_.begin(), index_offsets_.end(), [](const int32_t &offset) { return offset < 0; })) {
     MS_LOG(ERROR) << "Failure: Index offset must not less than 0.";
     return FAILED;
   }
@@ -269,8 +269,8 @@ Status GatherV2PInfo::InferTensorMap() {
   size_t param_size = inputs_shape_.at(0).size();
   size_t index_size = inputs_shape_.at(1).size();
   size_t total_size = param_size + index_size;
-  Shape tensor_map_index;
-  Shape tensor_map_params;
+  std::vector<int32_t> tensor_map_index;
+  std::vector<int32_t> tensor_map_params;
   auto param_strategy = strategy_->GetInputDim().at(0);
   if (param_strategy.at(IntToSize(axis_)) != 1) {
     tensor_map_index.insert(tensor_map_index.begin(), index_size, -1);
@@ -288,7 +288,7 @@ Status GatherV2PInfo::InferTensorMap() {
   }
 
   // infer output tensor map
-  Shape tensor_map_out;
+  std::vector<int32_t> tensor_map_out;
   if (param_strategy.at(IntToSize(axis_)) == 1) {
     // param_strategy(axis) == 1
     for (size_t i = 0; i < param_size; ++i) {
@@ -427,8 +427,8 @@ Status GatherV2PInfo::InferGroup() {
   return SUCCESS;
 }
 
-RankList GetRankFromGroup(const Group &group) {
-  RankList rank_list;
+std::vector<int32_t> GetRankFromGroup(const Group &group) {
+  std::vector<int32_t> rank_list;
   auto device_list = group.GetDevicesList();
   for (auto &device : device_list) {
     rank_list.insert(rank_list.end(), device.rank() % 8);
@@ -634,7 +634,7 @@ Status GatherV2PInfo::GenerateStrategies(int32_t stage_id) {
   return SUCCESS;
 }
 
-std::shared_ptr<Strategys> GatherV2PInfo::GenerateBatchStrategies() {
+std::shared_ptr<std::vector<std::vector<int32_t>>> GatherV2PInfo::GenerateBatchStrategies() {
   CheckGlobalDeviceManager();
   size_t dev_num = g_device_manager->GetDeviceListByStageId(0).size();
   Dimensions param_strategy(inputs_shape_[0].size(), 1);
@@ -643,8 +643,8 @@ std::shared_ptr<Strategys> GatherV2PInfo::GenerateBatchStrategies() {
   for (size_t i = 1; i < inputs_shape_[1].size(); i++) {
     index_strategy.push_back(1);
   }
-  Strategys strategy_v = {param_strategy, index_strategy};
-  return std::make_shared<Strategys>(strategy_v);
+  std::vector<Dimensions> strategy_v = {param_strategy, index_strategy};
+  return std::make_shared<std::vector<std::vector<int32_t>>>(strategy_v);
 }
 }  // namespace parallel
 }  // namespace mindspore

mindspore/ccsrc/frontend/parallel/ops_info/gather_v2_p_info.h

@@ -45,7 +45,7 @@ class GatherV2PInfo : public OperatorInfo {
   Status GenerateStrategies(int32_t stage_id) override;
   Status SetCostUnderStrategy(const StrategyPtr &strategy) override;
   ReplaceGraphPtr replace_graph(const CNodePtr &cnode) override;
-  std::shared_ptr<Strategys> GenerateBatchStrategies() override;
+  std::shared_ptr<std::vector<std::vector<int32_t>>> GenerateBatchStrategies() override;
 
  protected:
   Status CheckStrategy(const StrategyPtr &strategy) override;
@@ -67,13 +67,13 @@ class GatherV2PInfo : public OperatorInfo {
   std::string target_ = DEVICE;
   std::string replace_op_name_ = GATHERV2;
   int32_t bias_;
-  int64_t index_offset_;
+  int32_t index_offset_;
   int32_t slice_size_;
   Shape out_dev_matrix_shape_;
   Group group_;
   bool manual_split_ = false;
-  std::vector<int64_t> param_split_shapes_;
-  std::vector<int64_t> index_offsets_;
+  std::vector<int32_t> param_split_shapes_;
+  std::vector<int32_t> index_offsets_;
 };
 
 class SparseGatherV2Info : public GatherV2PInfo {

mindspore/ccsrc/frontend/parallel/ops_info/get_next_info.cc

@@ -118,7 +118,7 @@ Status GetNextInfo::Init(const StrategyPtr &strategy) {
 }
 
 Status GetNextInfo::CheckStrategy(const StrategyPtr &strategy) {
-  Strategys stras = strategy->GetInputDim();
+  std::vector<Dimensions> stras = strategy->GetInputDim();
   for (Dimensions stra : stras) {
     if (stra.size() != 0) {
       if (is_auto_parallel_) {
@@ -254,7 +254,7 @@ Status GetNextInfo::SetCostUnderStrategy(const StrategyPtr &strategy) {
 
 Status GetNextInfo::GenerateStrategies(int32_t stage_id) {
   is_auto_parallel_ = true;
-  Strategys stra;
+  std::vector<Dimensions> stra;
   StrategyPtr sp = std::make_shared<Strategy>(stage_id, stra);
   if (SetCostUnderStrategy(sp) == SUCCESS) {
     MS_LOG(INFO) << name_ << " : Successfully generated strategy.";

mindspore/ccsrc/frontend/parallel/ops_info/l2_normalize_info.cc

@@ -37,7 +37,7 @@ Status L2NormalizeInfo::CheckStrategy(const StrategyPtr &strategy) {
     return FAILED;
   }
 
-  Strategys stra = strategy->GetInputDim();
+  std::vector<Dimensions> stra = strategy->GetInputDim();
   Dimensions input_strategy = stra.at(0);
   int32_t axis_index = axis_;
   if (axis_ < 0) {

mindspore/ccsrc/frontend/parallel/ops_info/layer_norm_info.cc

@@ -49,7 +49,7 @@ Status LayerNormInfo::GetAttrs() {
 
 Status LayerNormInfo::CheckStrategy(const StrategyPtr &strategy) {
   MS_EXCEPTION_IF_NULL(strategy);
-  Strategys stra = strategy->GetInputDim();
+  std::vector<Dimensions> stra = strategy->GetInputDim();
   if (stra.size() != LAYER_NORM_INPUT_SIZE) {
     MS_LOG(ERROR) << name_ << ": Invalid strategy size " << stra.size();
     return FAILED;
@@ -104,7 +104,7 @@ Status LayerNormInfo::InferDevMatrixShape() {
     MS_LOG(ERROR) << name_ << ": The strategy is null";
     return FAILED;
   }
-  Strategys stra = strategy_->GetInputDim();
+  std::vector<Dimensions> stra = strategy_->GetInputDim();
   if (stra.empty()) {
     MS_LOG(ERROR) << name_ << ": The strategy is empty";
     return FAILED;
@@ -228,7 +228,7 @@ Status LayerNormInfo::GenerateGammaAndBetaStrategies(const std::vector<StrategyP
       MS_LOG(ERROR) << name_ << ": Invalid strategy";
       return FAILED;
     }
-    Strategys tmp_strategy;
+    std::vector<Dimensions> tmp_strategy;
     Dimensions input_strategy = sp->GetInputDim()[0];
     Dimensions gamma_strategy = input_strategy;
     (void)gamma_strategy.erase(gamma_strategy.begin(),

mindspore/ccsrc/frontend/parallel/ops_info/loss_info.cc

@@ -38,7 +38,7 @@ Status SoftmaxCrossEntropyWithLogitsInfo::CheckStrategy(const mindspore::paralle
     return FAILED;
   }
 
-  Strategys stra = strategy->GetInputDim();
+  std::vector<Dimensions> stra = strategy->GetInputDim();
   Dimensions input_strategy = stra.at(0);
   Dimensions label_strategy = stra.at(1);
   if (input_strategy != label_strategy) {
@@ -52,8 +52,8 @@ Status SoftmaxCrossEntropyWithLogitsInfo::CheckStrategy(const mindspore::paralle
     axis_index = static_cast<int32_t>(input_dim) + axis_;
   }
 
-  int64_t input_axis_strategy = input_strategy.at(IntToSize(axis_index));
-  int64_t label_axis_strategy = label_strategy.at(IntToSize(axis_index));
+  int32_t input_axis_strategy = input_strategy.at(IntToSize(axis_index));
+  int32_t label_axis_strategy = label_strategy.at(IntToSize(axis_index));
   // Dimension corresponding to axis is un-splittable
   if ((input_axis_strategy != MIN_SLICE_NUM) && (label_axis_strategy != MIN_SLICE_NUM)) {
     if (is_auto_parallel_) {
@@ -82,21 +82,21 @@ Status SoftmaxCrossEntropyWithLogitsInfo::GetAttrs() {
 }
 
 Status SoftmaxCrossEntropyWithLogitsInfo::InferDevMatrixShape() {
-  Strategys stra = strategy_->GetInputDim();
+  std::vector<Dimensions> stra = strategy_->GetInputDim();
   Dimensions input_strategy = stra.at(0);
   dev_matrix_shape_ = input_strategy;
   return SUCCESS;
 }
 
 Status SoftmaxCrossEntropyWithLogitsInfo::InferTensorMap() {
-  Shape tensor_map_index;
+  std::vector<int32_t> tensor_map_index;
   size_t size = inputs_shape_[0].size();
   // such as 4: tensor_map_index [3,2,1,0]
   for (size_t i = 0; i < size; ++i) {
-    tensor_map_index.push_back((int64_t)(size - i - 1));
+    tensor_map_index.push_back((int32_t)(size - i - 1));
   }
 
-  Shape first_output_tensor_map = {tensor_map_index[0]};
+  std::vector<int32_t> first_output_tensor_map = {tensor_map_index[0]};
   inputs_tensor_map_.push_back(tensor_map_index);          // input
   inputs_tensor_map_.push_back(tensor_map_index);          // label
   outputs_tensor_map_.push_back(first_output_tensor_map);  // output-0

mindspore/ccsrc/frontend/parallel/ops_info/matmul_info.cc

@@ -158,7 +158,7 @@ Status MatMul::CheckStrategy(const StrategyPtr &strategy) {
     return FAILED;
   }
 
-  Strategys stra = strategy->GetInputDim();
+  std::vector<Dimensions> stra = strategy->GetInputDim();
   Dimensions mat_a_strategy = stra.at(0);
   Dimensions mat_b_strategy = stra.at(1);
 
@@ -207,7 +207,7 @@ Status MatMul::CheckStrategy(const StrategyPtr &strategy) {
 }
 
 Status MatMulBase::InferDevMatrixShape() {
-  Strategys stra = strategy_->GetInputDim();
+  std::vector<Dimensions> stra = strategy_->GetInputDim();
   Dimensions mat_a_strategy = stra.at(0);
   Dimensions mat_b_strategy = stra.at(1);
 
@@ -279,10 +279,10 @@ Status MatMulBase::InferTensorMap() {
     size = dev_matrix_shape_.size() - 1;
   }
 
-  Shape tensor_map_index;
+  std::vector<int32_t> tensor_map_index;
   // such as 5: tensor_map_index [4,3,2,1,0]
   for (size_t i = 0; i < size; ++i) {
-    tensor_map_index.push_back((int64_t)(LAST_INDEX(size) - i));
+    tensor_map_index.push_back((int32_t)(LAST_INDEX(size) - i));
   }
 
   // infer output tensor map: [4,3,2,0], delete the second-from-end element
@@ -309,7 +309,7 @@ Status MatMulBase::InferTensorMap() {
     mat_b_tensor_map.begin() + static_cast<different_type>(LAST_INDEX(size) - mat_b_dimension_));
   if (transpose_b_) {
     // swap the last two elements
-    int64_t last_value = mat_b_tensor_map.back();
+    int32_t last_value = mat_b_tensor_map.back();
     mat_b_tensor_map.pop_back();
     (void)mat_b_tensor_map.insert(
       mat_b_tensor_map.begin() + static_cast<different_type>(LAST_INDEX(mat_b_tensor_map.size())), last_value);
@@ -436,7 +436,7 @@ Status MatMulBase::GenerateStrategies(int32_t stage_id) {
     return FAILED;
   }
   CheckGlobalDeviceManager();
-  RankList dev_list = g_device_manager->GetDeviceListByStageId(stage_id);
+  std::vector<int32_t> dev_list = g_device_manager->GetDeviceListByStageId(stage_id);
   size_t dev_num = dev_list.size();
   Shape input0_shape = inputs_shape_[0], input1_shape = inputs_shape_[1];
   if (transpose_a_) {
@@ -503,14 +503,13 @@ Status MatMulBase::GenerateStrategies(int32_t stage_id) {
 Status MatMulBase::PrepareStrategy(int32_t stage_id, size_t dev_num,
                                    mindspore::parallel::Dimensions combined_partitions, size_t input0_shape_size,
                                    size_t input1_shape_size, mindspore::parallel::StrategyPtr *const sp) {
-  int64_t product =
-    std::accumulate(combined_partitions.begin(), combined_partitions.end(), 1, std::multiplies<int64_t>());
+  int32_t product = std::accumulate(combined_partitions.begin(), combined_partitions.end(), 1, std::multiplies<int>());
   if (!FULLY_USE_DEVICES) {
-    if (LongToSize(product) > dev_num) {
+    if (IntToSize(product) > dev_num) {
       return FAILED;
     }
   } else {
-    if (LongToSize(product) != dev_num) {
+    if (IntToSize(product) != dev_num) {
       return FAILED;
     }
   }
@@ -551,7 +550,7 @@ Status MatMulBase::PrepareStrategy(int32_t stage_id, size_t dev_num,
       MS_LOG(ERROR) << name_ << " : Swap last two elements failed.";
     }
   }
-  Strategys stras;
+  std::vector<Dimensions> stras;
   stras.push_back(input0_partitions);
   stras.push_back(input1_partitions);
   (*sp) = std::make_shared<Strategy>(stage_id, stras);

mindspore/ccsrc/frontend/parallel/ops_info/onehot_info.cc

@@ -77,7 +77,7 @@ Status OneHotInfo::CheckStrategy(const StrategyPtr &strategy) {
 }
 
 Status OneHotInfo::InferDevMatrixShape() {
-  Strategys stra = strategy_->GetInputDim();
+  std::vector<Dimensions> stra = strategy_->GetInputDim();
   Dimensions input_strategy = stra.at(0);
 
   // Now input only support 1-D tensor, so the output is a 2-D tensor
@@ -96,16 +96,16 @@ Status OneHotInfo::InferDevMatrixShape() {
 }
 
 Status OneHotInfo::InferTensorMap() {
-  Shape input_tensor_map_index, output_tensor_map_index;
+  std::vector<int32_t> input_tensor_map_index, output_tensor_map_index;
   size_t size = outputs_shape_[0].size();
   // such as 2: tensor_map_index [1,0]
   if (axis_ == 0) {
     for (size_t i = 0; i < size; ++i) {
-      output_tensor_map_index.push_back((int64_t)(i));
+      output_tensor_map_index.push_back((int32_t)(i));
     }
   } else {
     for (size_t i = 0; i < size; ++i) {
-      output_tensor_map_index.push_back((int64_t)(LAST_INDEX(size) - i));
+      output_tensor_map_index.push_back((int32_t)(LAST_INDEX(size) - i));
     }
   }
   outputs_tensor_map_.push_back(output_tensor_map_index);
@@ -299,13 +299,13 @@ Status OneHotInfo::SetCostUnderStrategy(const StrategyPtr &strategy) {
   return SUCCESS;
 }
 
-std::shared_ptr<Strategys> OneHotInfo::GenerateBatchStrategies() {
+std::shared_ptr<std::vector<std::vector<int32_t>>> OneHotInfo::GenerateBatchStrategies() {
   CheckGlobalDeviceManager();
   size_t dev_num = g_device_manager->GetDeviceListByStageId(0).size();
   Dimensions strategy = {SizeToInt(dev_num), 1};
   Dimensions empty_strategy;
-  Strategys strategy_v = {strategy, empty_strategy, empty_strategy};
-  return std::make_shared<Strategys>(strategy_v);
+  std::vector<Dimensions> strategy_v = {strategy, empty_strategy, empty_strategy};
+  return std::make_shared<std::vector<std::vector<int32_t>>>(strategy_v);
 }
 }  // namespace parallel
 }  // namespace mindspore

mindspore/ccsrc/frontend/parallel/ops_info/onehot_info.h

@@ -41,7 +41,7 @@ class OneHotInfo : public OperatorInfo {
   Status GenerateStrategies(int32_t stage_id) override;
   Status SetCostUnderStrategy(const StrategyPtr &strategy) override;
   ReplaceGraphPtr replace_graph(const CNodePtr &cnode) override;
-  std::shared_ptr<Strategys> GenerateBatchStrategies() override;
+  std::shared_ptr<std::vector<std::vector<int32_t>>> GenerateBatchStrategies() override;
 
  protected:
   Status CheckStrategy(const StrategyPtr &strategy) override;

mindspore/ccsrc/frontend/parallel/ops_info/operator_info.cc

@@ -52,7 +52,7 @@ Status CheckStrategyValue(const StrategyPtr &strategy, const Shapes &inputs_shap
     return FAILED;
   }
 
-  Strategys stra = strategy->GetInputDim();
+  std::vector<Dimensions> stra = strategy->GetInputDim();
   for (size_t i = 0; i < strategy_size; ++i) {
     Shape sub_strategy = stra.at(i);
     Shape sub_input_shape = inputs_shape.at(i);
@@ -70,7 +70,7 @@ Status CheckStrategyValue(const StrategyPtr &strategy, const Shapes &inputs_shap
     }
 
     for (size_t j = 0; j < strategy_len; ++j) {
-      int64_t strategy_value = sub_strategy.at(j);
+      int32_t strategy_value = sub_strategy.at(j);
       if (strategy_value < MIN_SLICE_NUM) {
         if (is_auto_parallel) {
           MS_LOG(DEBUG) << "Invalid strategy value: " << strategy_value;
@@ -89,7 +89,7 @@ Status CheckStrategyValue(const StrategyPtr &strategy, const Shapes &inputs_shap
         return FAILED;
       }
 
-      int64_t shape_value = sub_input_shape.at(j);
+      int32_t shape_value = sub_input_shape.at(j);
       if ((shape_value % strategy_value) != 0) {
         if (is_auto_parallel) {
           MS_LOG(DEBUG) << "Shape " << shape_value << " cannot be divisible by strategy " << strategy_value;
@@ -138,9 +138,9 @@ void OperatorInfo::SetDeviceListByStrategy() {
 }
 
 Status OperatorInfo::InferRepeatedCalcInfo() {
-  int64_t g_dev_list_size = SizeToLong(global_device_list_.size());
-  int64_t dev_matrix_size =
-    std::accumulate(dev_matrix_shape_.begin(), dev_matrix_shape_.end(), 1, std::multiplies<int64_t>());
+  int32_t g_dev_list_size = SizeToInt(global_device_list_.size());
+  int32_t dev_matrix_size =
+    std::accumulate(dev_matrix_shape_.begin(), dev_matrix_shape_.end(), 1, std::multiplies<int>());
   if (dev_matrix_size == 0) {
     MS_LOG(ERROR) << name_ << ": The dev matrix size is 0";
     return FAILED;
@@ -149,7 +149,7 @@ Status OperatorInfo::InferRepeatedCalcInfo() {
   if (g_dev_list_size == dev_matrix_size) {
     repeated_calc_num_ = 1;
   } else if (g_dev_list_size % dev_matrix_size == 0) {
-    repeated_calc_num_ = ((int32_t)(g_dev_list_size / dev_matrix_size));
+    repeated_calc_num_ = g_dev_list_size / dev_matrix_size;
   } else {
     MS_LOG(ERROR) << name_ << ": Dev list size " << g_dev_list_size << " can not be divisible by dev matrix size "
                   << dev_matrix_size;
@@ -326,7 +326,7 @@ Status OperatorInfo::CreateGroupByDim(size_t axis, std::vector<Group> *group) {
 
 Shape GetSliceShape(const Shape &tensor_shape, const Dimensions &strategy) {
   Shape slice_shape;
-  if (std::any_of(strategy.begin(), strategy.end(), [](int64_t value) { return value <= 0; })) {
+  if (std::any_of(strategy.begin(), strategy.end(), [](int32_t value) { return value <= 0; })) {
     MS_LOG(ERROR) << "Invalid strategy: " << ShapeToString(strategy) << ", the element is less than or equal to 0";
     return slice_shape;
   }
@@ -430,8 +430,7 @@ Status OperatorInfo::InitForCostModelWithAutoRepeatCalc(const StrategyPtr &strat
     return FAILED;
   }
 
-  used_devices_ =
-    ((int32_t)(std::accumulate(dev_matrix_shape_.begin(), dev_matrix_shape_.end(), 1, std::multiplies<int64_t>())));
+  used_devices_ = std::accumulate(dev_matrix_shape_.begin(), dev_matrix_shape_.end(), 1, std::multiplies<int32_t>());
 
   // must be after InferDevMatrixShape
   if (InferRepeatedCalcInfo() != SUCCESS) {
@@ -647,8 +646,8 @@ void OperatorInfo::ReplaceSuccEdges(const std::shared_ptr<OperatorInfo> &op, con
   succ_edges_ = new_succ_edges;
 }
 
-std::shared_ptr<Strategys> GenerateBatchStrategiesBySplitFlag(const Shapes &shapes,
-                                                              const std::vector<bool> &split_flag_list) {
+std::shared_ptr<std::vector<std::vector<int32_t>>> GenerateBatchStrategiesBySplitFlag(
+  const Shapes &shapes, const std::vector<bool> &split_flag_list) {
   if (shapes.size() != split_flag_list.size()) {
     MS_LOG(ERROR) << "Split_flag_list do not have the same size as inputs shape, " << split_flag_list.size() << " : "
                   << shapes.size();
@@ -656,21 +655,21 @@ std::shared_ptr<Strategys> GenerateBatchStrategiesBySplitFlag(const Shapes &shap
   }
   CheckGlobalDeviceManager();
   int32_t dev_num = SizeToInt(g_device_manager->GetDeviceListByStageId(0).size());
-  Strategys strategy_v;
+  std::vector<std::vector<int32_t>> strategy_v;
   for (size_t i = 0; i != shapes.size(); i++) {
     if (shapes[i].empty()) {
       MS_LOG(INFO) << "Elements of shapes is empty.";
-      Dimensions empty_element;
+      std::vector<int32_t> empty_element;
       strategy_v.push_back(empty_element);
     } else {
-      Dimensions element(shapes[i].size(), 1);
+      std::vector<int32_t> element(shapes[i].size(), 1);
       if (split_flag_list[i]) {
         element[0] = dev_num;
       }
       strategy_v.push_back(element);
     }
   }
-  return std::make_shared<Strategys>(strategy_v);
+  return std::make_shared<std::vector<std::vector<int32_t>>>(strategy_v);
 }
 
 void OperatorInfo::ReComputeBatchSplitFlagList() {
@@ -693,26 +692,26 @@ Status PrepareStrategyBase(int32_t stage_id, size_t dev_num, const Shapes &input
     MS_LOG(ERROR) << "The strategy is null.";
     return FAILED;
   }
-  int64_t product = 1;
+  int32_t product = 1;
 
   for (auto &input_partition : inputs_partitions) {
-    product *= std::accumulate(input_partition.begin(), input_partition.end(), 1, std::multiplies<int64_t>());
+    product *= std::accumulate(input_partition.begin(), input_partition.end(), 1, std::multiplies<int>());
   }
   if (!FULLY_USE_DEVICES) {
-    if (LongToSize(product) > dev_num) {
+    if (IntToSize(product) > dev_num) {
       return FAILED;
     }
   } else {
-    if ((product != 1) && (LongToSize(product) != dev_num)) {
+    if ((product != 1) && (IntToSize(product) != dev_num)) {
       return FAILED;
     }
   }
-  Strategys stras(inputs_partitions);
+  std::vector<Dimensions> stras(inputs_partitions);
   (*sp) = std::make_shared<Strategy>(stage_id, stras);
   return SUCCESS;
 }
 
-std::shared_ptr<Strategys> OperatorInfo::GenerateBatchStrategies() {
+std::shared_ptr<std::vector<std::vector<int32_t>>> OperatorInfo::GenerateBatchStrategies() {
   ComputeBatchSplitFlagList();
   return GenerateBatchStrategiesBySplitFlag(inputs_shape_, split_flag_list_);
 }
@@ -794,7 +793,7 @@ Status GenerateStrategiesForBroadcastLeft(int32_t stage_id, const Shapes &inputs
 
   // second, get the correct strategy for input0
   for (auto &sp : *sp_vector) {
-    Strategys tmp_strategy;
+    std::vector<Dimensions> tmp_strategy;
     Dimensions input0_strategy = sp->GetInputDim()[0];
     size_t size_diff = inputs_shape[1].size() - inputs_shape[0].size();
 
@@ -843,7 +842,7 @@ Status GenerateStrategiesForBroadcastRight(int32_t stage_id, const Shapes &input
 
   // second, get the correct strategy for input1
   for (auto &sp : *sp_vector) {
-    Strategys tmp_strategy;
+    std::vector<Dimensions> tmp_strategy;
     tmp_strategy.push_back(sp->GetInputDim()[0]);  // input0
 
     Dimensions input1_strategy = sp->GetInputDim()[1];
@@ -1176,7 +1175,7 @@ int32_t ComputeRepeatDeviceNumByTensorMap(const Shape &dev_matrix_shape, const S
 
   // The number of repetitions is equal to the number of all devices divided by the number of devices use for
   // tensor map.
-  int64_t device_num = std::accumulate(dev_matrix_shape.begin(), dev_matrix_shape.end(), 1, std::multiplies<int64_t>());
+  int32_t device_num = std::accumulate(dev_matrix_shape.begin(), dev_matrix_shape.end(), 1, std::multiplies<int>());
   for (auto &element : tensor_map) {
     // -1 means the corresponding dimension is not split.
     if (element == MAP_NONE) {
@@ -1195,7 +1194,7 @@ int32_t ComputeRepeatDeviceNumByTensorMap(const Shape &dev_matrix_shape, const S
     }
   }
 
-  return (int32_t)device_num;
+  return device_num;
 }
 
 Status OperatorInfo::InferAsLossDivisor() {

mindspore/ccsrc/frontend/parallel/ops_info/operator_info.h

@@ -43,10 +43,11 @@ using ForwardOp = OperatorVector;
 using MirrorOps = std::vector<OperatorVector>;
 using Ops = std::vector<OperatorVector>;
 using VirtualDivOp = OperatorVector;
-using TensorMaps = std::vector<Shape>;
+using TensorMaps = std::vector<std::vector<int32_t>>;
 using TensorLayouts = std::vector<TensorLayout>;
 using different_type = std::vector<int32_t>::difference_type;
 using PrimitiveAttrs = std::unordered_map<std::string, ValuePtr>;
+using Strategys = std::vector<Dimensions>;
 using ReplaceGraphPtr = std::shared_ptr<std::pair<std::vector<std::pair<AnfNodePtr, int>>, AnfNodePtr>>;
 
 class Edge;
@@ -87,7 +88,7 @@ class OperatorInfo {
   void set_cost(const OperatorCostPtr &cost) { operator_cost_ = cost; }
   virtual Status SetCostUnderStrategy(const StrategyPtr &strategy) = 0;
 
-  virtual std::shared_ptr<Strategys> GenerateBatchStrategies();
+  virtual std::shared_ptr<std::vector<std::vector<int32_t>>> GenerateBatchStrategies();
   virtual void ReComputeBatchSplitFlagList();
   void ComputeBatchSplitFlagList();
 
@@ -270,8 +271,8 @@ Operator CreateReduceScatterOp(const std::string &reduce_op, const std::string &
 Operator CreateGetTensorSliceOp(const TensorLayout &tensor_layout);
 OperatorVector CreateMirrorOps(const std::string &group_name, size_t dev_num);
 int32_t ComputeRepeatDeviceNumByTensorMap(const Shape &dev_matrix_shape, const Shape &tensor_map);
-std::shared_ptr<Strategys> GenerateBatchStrategiesBySplitFlag(const Shapes &shapes,
-                                                              const std::vector<bool> &split_flag_list);
+std::shared_ptr<std::vector<std::vector<int32_t>>> GenerateBatchStrategiesBySplitFlag(
+  const Shapes &shapes, const std::vector<bool> &split_flag_list);
 
 void PrintStrategy(const StrategyPtr &strategy);
 // generate strategies for that all inputs' dimensions are independent, such as: ([a, b, c, d])

mindspore/ccsrc/frontend/parallel/ops_info/prelu_info.cc

@@ -43,7 +43,7 @@ Status PReLUInfo::CheckStrategy(const StrategyPtr &strategy) {
     }
     return FAILED;
   }
-  Strategys stra = strategy->GetInputDim();
+  std::vector<Dimensions> stra = strategy->GetInputDim();
   if (stra[1].size() != PRELU_SECOND_INPUT_SIZE) {
     if (is_auto_parallel_) {
       MS_LOG(DEBUG) << name_ << ": Invalid strategy size.";
@@ -67,7 +67,7 @@ Status PReLUInfo::CheckStrategy(const StrategyPtr &strategy) {
  * device matrix is same with the strategy matrix
  */
 Status PReLUInfo::InferDevMatrixShape() {
-  Strategys stra = strategy_->GetInputDim();
+  std::vector<Dimensions> stra = strategy_->GetInputDim();
   Dimensions input_strategy = stra.at(0);
   input_strategy_ = input_strategy;
   dev_matrix_shape_ = input_strategy;
@@ -103,7 +103,7 @@ Status PReLUInfo::InferTensorMap() {
   TensorMap input_tensor_map;
   // such as 4: input_tensor_map [3,2,1,0]
   for (size_t i = 0; i < inputs_shape_[0].size(); ++i) {
-    input_tensor_map.push_back((int64_t)(inputs_shape_[0].size() - i - 1));
+    input_tensor_map.push_back((int32_t)(inputs_shape_[0].size() - i - 1));
   }
 
   TensorMap param_tensor_map;

mindspore/ccsrc/frontend/parallel/ops_info/reduce_method_info.cc

@@ -43,7 +43,7 @@ Status ReduceMethod::CheckStrategy(const StrategyPtr &strategy) {
 }
 
 Status ReduceMethod::InferDevMatrixShape() {
-  Strategys stra = strategy_->GetInputDim();
+  std::vector<Dimensions> stra = strategy_->GetInputDim();
   Dimensions input_strategy = stra.at(0);
 
   dev_matrix_shape_ = input_strategy;
@@ -119,12 +119,11 @@ Status ReduceMethod::GetAttrs() {
 }
 
 Status ReduceMethod::InferTensorMap() {
-  Shape tensor_map_index, output_tensor_map;
-  std::vector<int32_t> dim_list;
+  std::vector<int32_t> tensor_map_index, dim_list, output_tensor_map;
   size_t size = inputs_shape_.at(0).size();
   // such as 4: tensor_map_index [3,2,1,0]
   for (size_t i = 0; i < size; ++i) {
-    tensor_map_index.push_back((int64_t)(size - 1 - i));
+    tensor_map_index.push_back((int32_t)(size - 1 - i));
   }
   dim_list = reduce_dim();
   for (size_t i = 0; i < size; ++i) {
@@ -463,7 +462,7 @@ Status ArgMaxWithValueInfo::CheckStrategy(const StrategyPtr &strategy) {
   std::vector<int32_t> dim_list = reduce_dim();
   MS_ASSERT(dim_list.size() == 1);
 
-  Strategys stra = strategy->GetInputDim();
+  std::vector<Dimensions> stra = strategy->GetInputDim();
   MS_ASSERT(stra.size() == 1);
   Shape input_strategy = stra.at(0);
   MS_ASSERT(dim_list.at(0) < input_strategy.size());

mindspore/ccsrc/frontend/parallel/ops_info/reshape_info.cc

@@ -57,7 +57,7 @@ Status ReshapeInfo::CheckStrategy(const StrategyPtr &strategy) {
  * only support batch parallel reshape operator in ReID (batch parallel degree can be smaller than device number)
  */
 Status ReshapeInfo::InferDevMatrixShape() {
-  Strategys stra = strategy_->GetInputDim();
+  std::vector<Dimensions> stra = strategy_->GetInputDim();
   input_strategy_ = stra.at(0);
   dev_matrix_shape_.push_back(input_strategy_[0]);
   return SUCCESS;
@@ -181,7 +181,7 @@ Status ReshapeInfo::InferTensorMap() {
     return FAILED;
   }
 
-  Shape tensor_map_index_input;
+  std::vector<int32_t> tensor_map_index_input;
   tensor_map_index_input.push_back(0);
 
   for (size_t j = 1; j < inputs_shape_[0].size(); ++j) {
@@ -189,7 +189,7 @@ Status ReshapeInfo::InferTensorMap() {
   }
   inputs_tensor_map_.push_back(tensor_map_index_input);
 
-  Shape tensor_map_index_output;
+  std::vector<int32_t> tensor_map_index_output;
   tensor_map_index_output.push_back(0);
 
   for (size_t j = 1; j < outputs_shape_[0].size(); ++j) {
@@ -205,7 +205,7 @@ Status ReshapeInfo::InferTensorMap() {
  */
 Strategys ReshapeInfo::GetOutputsStrategy() {
   Strategys outputs_strategy;
-  Dimensions strategy;
+  std::vector<int32_t> strategy;
   strategy.push_back(input_strategy_[0]);
   for (size_t j = 1; j < outputs_shape_[0].size(); ++j) {
     strategy.push_back(1);
@@ -325,7 +325,7 @@ void ReshapeInfo::device_number(const StrategyPtr &strategy) {
 }
 
 Status ReshapeInfo::InferDefaultLayout(const Shape &shape, TensorLayout *const layout) {
-  Shape tensor_map_index;
+  std::vector<int32_t> tensor_map_index;
   for (size_t i = 0; i < shape.size(); i++) {
     tensor_map_index.push_back(MAP_NONE);
   }
@@ -504,7 +504,7 @@ Status ReshapeInfo::GenetateStrategyCosts(const std::vector<std::shared_ptr<Stra
       MS_LOG(ERROR) << "Infer strategy by tensor_info failed";
       return FAILED;
     }
-    Strategys stra_inputs = {stra};
+    std::vector<Dimensions> stra_inputs = {stra};
     StrategyPtr reshape_stra = std::make_shared<Strategy>(pre_stra_cost->strategy_ptr->GetInputStage(), stra_inputs);
     if (next_stra_costs.empty()) {
       if (Init(nullptr) == FAILED) {

mindspore/ccsrc/frontend/parallel/ops_info/strided_slice_info.cc

@@ -227,7 +227,7 @@ Status StridedSliceInfo::InferTensorInfo() {
 }
 
 // Note: if the batch dimension is not fully fetched, the batch strategy may not work.
-std::shared_ptr<Strategys> StridedSliceInfo::GenerateBatchStrategies() {
+std::shared_ptr<std::vector<std::vector<int32_t>>> StridedSliceInfo::GenerateBatchStrategies() {
   split_flag_list_ = {true};
   return GenerateBatchStrategiesBySplitFlag(inputs_shape_, split_flag_list_);
 }

mindspore/ccsrc/frontend/parallel/ops_info/strided_slice_info.h

@@ -41,7 +41,7 @@ class StridedSliceInfo : public OperatorInfo {
   Status InitForCostModel(const StrategyPtr &strategy) override;
   Status GenerateStrategies(int32_t) override;
   Status SetCostUnderStrategy(const StrategyPtr &) override;
-  std::shared_ptr<Strategys> GenerateBatchStrategies() override;
+  std::shared_ptr<std::vector<std::vector<int32_t>>> GenerateBatchStrategies() override;
 
  protected:
   Status GetAttrs() override;

mindspore/ccsrc/frontend/parallel/ops_info/tile_info.cc

@@ -54,7 +54,7 @@ Status TileInfo::GetAttrs() {
   for (auto &element : elements) {
     MS_EXCEPTION_IF_NULL(element);
     if (element->isa<Int32Imm>()) {
-      int64_t axis = static_cast<int64_t>(element->cast<Int32ImmPtr>()->value());
+      int32_t axis = element->cast<Int32ImmPtr>()->value();
       full_multiples_.push_back(axis);
     } else {
       MS_LOG(ERROR) << name_ << ": The value of axis must be int32.";
@@ -180,15 +180,12 @@ void TileInfo::UpdateMultiples(const CNodePtr &cnode) {
   auto manager = func_graph->manager();
   MS_EXCEPTION_IF_NULL(manager);
 
-  std::vector<int32_t> slice_multiples_int;
-  (void)std::transform(slice_multiples_.begin(), slice_multiples_.end(), std::back_inserter(slice_multiples_int),
-                       [](const int64_t &value) { return static_cast<int32_t>(value); });
-  ValuePtr new_multiples = MakeValue(slice_multiples_int);
+  ValuePtr new_multiples = MakeValue(slice_multiples_);
   AnfNodePtr val = NewValueNode(new_multiples);
   (void)manager->Replace(cnode->input(2), val);
 }
 
-std::shared_ptr<Strategys> TileInfo::GenerateBatchStrategies() {
+std::shared_ptr<std::vector<std::vector<int32_t>>> TileInfo::GenerateBatchStrategies() {
   if (InferAttrs() != SUCCESS) {
     MS_LOG(EXCEPTION) << name_ << ": Infer attrs failed";
   }

mindspore/ccsrc/frontend/parallel/ops_info/tile_info.h

@@ -41,7 +41,7 @@ class TileInfo : public OperatorInfo {
   Status InitForCostModel(const StrategyPtr &strategy) override;
   Status GenerateStrategies(int32_t) override;
   Status SetCostUnderStrategy(const StrategyPtr &) override;
-  std::shared_ptr<Strategys> GenerateBatchStrategies() override;
+  std::shared_ptr<std::vector<std::vector<int32_t>>> GenerateBatchStrategies() override;
   void UpdateMultiples(const CNodePtr &cnode);
 
  protected:
@@ -54,8 +54,8 @@ class TileInfo : public OperatorInfo {
   Status InferTensorMap() override;
 
  private:
-  std::vector<int64_t> full_multiples_;
-  std::vector<int64_t> slice_multiples_;
+  std::vector<int32_t> full_multiples_;
+  std::vector<int32_t> slice_multiples_;
 };
 
 using TileInfoPtr = std::shared_ptr<TileInfo>;

mindspore/ccsrc/frontend/parallel/ops_info/tmp_identity_info.cc

@@ -37,18 +37,18 @@ Status TmpIdentityInfo::CheckStrategy(const mindspore::parallel::StrategyPtr &st
 }
 
 Status TmpIdentityInfo::InferDevMatrixShape() {
-  Strategys stra = strategy_->GetInputDim();
+  std::vector<Dimensions> stra = strategy_->GetInputDim();
   Dimensions input_strategy = stra.at(0);
   dev_matrix_shape_ = input_strategy;
   return SUCCESS;
 }
 
 Status TmpIdentityInfo::InferTensorMap() {
-  Shape tensor_map_index;
+  std::vector<int32_t> tensor_map_index;
   size_t size = inputs_shape_[0].size();
   // such as 4: tensor_map_index [3,2,1,0]
   for (size_t i = 0; i < size; ++i) {
-    tensor_map_index.push_back((int64_t)(size - 1 - i));
+    tensor_map_index.push_back((int32_t)(size - 1 - i));
   }
 
   inputs_tensor_map_.push_back(tensor_map_index);

mindspore/ccsrc/frontend/parallel/ops_info/transpose_info.cc

@@ -41,7 +41,7 @@ Status TransposeInfo::CheckStrategy(const StrategyPtr &strategy) {
 }
 
 Status TransposeInfo::InferDevMatrixShape() {
-  Strategys stra = strategy_->GetInputDim();
+  std::vector<Dimensions> stra = strategy_->GetInputDim();
   input_strategy_ = stra.at(0);
   for (auto &iter : input_strategy_) {
     dev_matrix_shape_.push_back(iter);
@@ -105,13 +105,13 @@ Status TransposeInfo::InferTensorMap() {
     return FAILED;
   }
 
-  Shape tensor_map_index_input;
+  std::vector<int32_t> tensor_map_index_input;
   for (size_t j = 0; j < inputs_shape_[0].size(); ++j) {
     tensor_map_index_input.push_back(SizeToInt(inputs_shape_[0].size() - j - 1));
   }
   inputs_tensor_map_.push_back(tensor_map_index_input);
 
-  Shape tensor_map_index_output = tensor_map_index_input;
+  std::vector<int32_t> tensor_map_index_output = tensor_map_index_input;
   for (uint32_t i = 0; i < tensor_map_index_output.size(); i++) {
     tensor_map_index_output[i] = tensor_map_index_input[IntToUint(axis_v_[i])];
   }
@@ -122,7 +122,7 @@ Status TransposeInfo::InferTensorMap() {
 // the output tensor strategy is the permutation of input tensor strategy, the permutation is axis_v
 Strategys TransposeInfo::GetOutputsStrategy() {
   Strategys outputs_strategy;
-  Dimensions strategy = input_strategy_;
+  std::vector<int32_t> strategy = input_strategy_;
   for (uint32_t i = 0; i < strategy.size(); i++) {
     strategy[i] = input_strategy_[IntToUint(axis_v_[i])];
   }

mindspore/ccsrc/frontend/parallel/ops_info/virtual_dataset_info.cc

@@ -38,7 +38,7 @@ Status VirtualDatasetInfo::CheckStrategy(const StrategyPtr &strategy) {
     return FAILED;
   }
 
-  Strategys stra = strategy->GetInputDim();
+  std::vector<Dimensions> stra = strategy->GetInputDim();
   if (stra.size() < 1) {
     if (is_auto_parallel_) {
       MS_LOG(DEBUG) << name_ << ": Strategy size must be larger than 1.";
@@ -80,12 +80,12 @@ Status VirtualDatasetInfo::CheckStrategy(const StrategyPtr &strategy) {
 }
 
 Status VirtualDatasetInfo::InferDevMatrixShape() {
-  Strategys stra = strategy_->GetInputDim();
+  std::vector<Dimensions> stra = strategy_->GetInputDim();
   Dimensions strategy_first = stra.at(0);
   int32_t stage = strategy_->GetInputStage();
   CheckGlobalDeviceManager();
   int32_t dev_num = SizeToInt(g_device_manager->GetDeviceListByStageId(stage).size());
-  int32_t batch_split_num = ((int32_t)(strategy_first.at(0)));
+  int32_t batch_split_num = strategy_first.at(0);
   dev_matrix_shape_.push_back(batch_split_num);
   if (dev_num > batch_split_num) {
     dev_matrix_shape_.push_back(dev_num / batch_split_num);
@@ -103,11 +103,11 @@ Status VirtualDatasetInfo::InferTensorMap() {
   bool full_batch = ParallelContext::GetInstance()->full_batch();
 
   for (size_t i = 0; i < strategy_->GetInputNumber(); i++) {
-    Shape tensor_map_index;
+    std::vector<int32_t> tensor_map_index;
     if (full_batch) {
       tensor_map_index.push_back(MAP_NONE);
     } else {
-      tensor_map_index.push_back((int64_t)(LAST_INDEX(dev_matrix_shape_.size())));
+      tensor_map_index.push_back((int32_t)(LAST_INDEX(SizeToUint(dev_matrix_shape_.size()))));
     }
     for (size_t j = 1; j < strategy_->GetInputDim()[i].size(); ++j) {
       tensor_map_index.push_back(MAP_NONE);
@@ -193,7 +193,7 @@ Status VirtualDatasetInfo::GenerateStrategies(int32_t stage_id) {
     total_dev_num = g_device_manager->GetDeviceListByStageId(stage_id).size();
   }
   StrategyPtr sp;
-  Strategys strategy;
+  std::vector<Dimensions> strategy;
   for (auto &shape : inputs_shape_) {
     Shape temp;
     temp.emplace_back(SizeToInt(total_dev_num));

mindspore/ccsrc/frontend/parallel/step_parallel.cc

@@ -1019,16 +1019,14 @@ StrategyPtr ExtractStrategy(std::unordered_map<std::string, ValuePtr> attrs) {
   }
   if (var->size() > 0) {
     std::vector<ValuePtr> elements = var->value();
-    Strategys strategy;
+    std::vector<Dimensions> strategy;
     for (uint32_t index = 0; index < elements.size(); ++index) {
       Dimensions dim;
       if (elements[index]->isa<ValueSequeue>()) {
         ValueTuplePtr value_tuple = elements[index]->cast<ValueTuplePtr>();
         std::vector<ValuePtr> value_vector = value_tuple->value();
-        (void)std::transform(
-          value_vector.begin(), value_vector.end(), std::back_inserter(dim), [](const ValuePtr &value) {
-            return value->isa<Int64Imm>() ? GetValue<int64_t>(value) : static_cast<int64_t>(GetValue<int>(value));
-          });
+        (void)std::transform(value_vector.begin(), value_vector.end(), std::back_inserter(dim),
+                             [](const ValuePtr &value) { return static_cast<int32_t>(GetValue<int>(value)); });
         strategy.push_back(dim);
       } else {
         MS_LOG(EXCEPTION) << "Failure:Strategy's format is wrong! Need ValueSequeue";
@@ -1077,20 +1075,12 @@ Shapes GetNodeShape(const AnfNodePtr &node) {
     for (auto &shape : tuple_shape) {
       auto each_shape = dyn_cast<abstract::Shape>(shape);
       MS_EXCEPTION_IF_NULL(each_shape);
-      std::vector<int> shape_int = each_shape->shape();
-      Shape new_shape;
-      (void)std::transform(shape_int.begin(), shape_int.end(), std::back_inserter(new_shape),
-                           [](const int &value) { return static_cast<int64_t>(value); });
-      shapes.push_back(new_shape);
+      shapes.push_back(each_shape->shape());
     }
   } else {
     auto shape_ptr = dyn_cast<abstract::Shape>(base_shape_ptr);
     MS_EXCEPTION_IF_NULL(shape_ptr);
-    std::vector<int> shape_int = shape_ptr->shape();
-    Shape new_shape;
-    (void)std::transform(shape_int.begin(), shape_int.end(), std::back_inserter(new_shape),
-                         [](const int &value) { return static_cast<int64_t>(value); });
-    shapes.push_back(new_shape);
+    shapes.push_back(shape_ptr->shape());
   }
   return shapes;
 }
@@ -1422,7 +1412,7 @@ void SetVirtualDatasetStrategy(const CNodePtr &node) {
       if (shape_list[0][i].empty()) {
         MS_LOG(EXCEPTION) << "shape_list[ " << i << " ].size() is zero";
       }
-      Dimensions input_strategy = {dev_num};
+      std::vector<int32_t> input_strategy = {dev_num};
       for (size_t j = 1; j < shape_list[0][i].size(); j++) {
         input_strategy.push_back(1);
       }
@@ -1486,7 +1476,7 @@ void ExtractInformation(const std::vector<AnfNodePtr> &all_nodes) {
       if (!StrategyFound(attrs) && !load_strategy_from_ckpt) {
         MS_LOG(INFO) << "ExtractInformation: the strategy of node " << node->ToString() << " prim " << prim->name()
                      << " is empty, using batch parallel";
-        std::shared_ptr<Strategys> strategy_v_ptr = operator_->GenerateBatchStrategies();
+        std::shared_ptr<std::vector<Dimensions>> strategy_v_ptr = operator_->GenerateBatchStrategies();
         if (strategy_v_ptr == nullptr) {
           MS_LOG(EXCEPTION) << "Failure:Generate batch parallel strategy failed";
         }

mindspore/ccsrc/frontend/parallel/strategy.h

@@ -24,20 +24,19 @@
 #include <vector>
 
 #include "frontend/parallel/status.h"
-#include "frontend/parallel/device_matrix.h"
 
 namespace mindspore {
 namespace parallel {
 #define MIN_SLICE_NUM 1
 
-using Dimensions = Shape;
-using Strategys = std::vector<Dimensions>;
+using Dimensions = std::vector<int32_t>;
+
 class Strategy;
 using StrategyPtr = std::shared_ptr<Strategy>;
 
 class Strategy {
  public:
-  Strategy(int32_t stage, Strategys inputs)
+  Strategy(int32_t stage, std::vector<Dimensions> inputs)
       : stage_(stage), inputs_(std::move(inputs)), internal_size_(0), internal_stragies_() {}
 
   Strategy(const Strategy &another_stra) : stage_(another_stra.GetInputStage()) {
@@ -52,14 +51,14 @@ class Strategy {
 
   ~Strategy() = default;
   size_t GetInputNumber() const { return inputs_.size(); }
-  Strategys GetInputDim() const { return inputs_; }
+  std::vector<Dimensions> GetInputDim() const { return inputs_; }
   int32_t GetInputStage() const { return stage_; }
   void ExpandInputDimFromOneToTwo() {
     if (inputs_.size() == 1) {
       inputs_.push_back(inputs_[0]);
     }
   }
-  void ResetInputs(const Strategys &input) { inputs_ = input; }
+  void ResetInputs(const std::vector<Dimensions> &input) { inputs_ = input; }
   std::vector<StrategyPtr> GetInternalStrategies() const { return internal_stragies_; }
   size_t GetInternalSize() const { return internal_size_; }
 
@@ -84,12 +83,12 @@ class Strategy {
   const int32_t stage_;
 
   // The size of Dimensions must equal to inputs_ tensor dimension.
-  Strategys inputs_;
+  std::vector<Dimensions> inputs_;
   size_t internal_size_ = 0;
   std::vector<StrategyPtr> internal_stragies_;
 };
 
-inline StrategyPtr NewStrategy(const int32_t stage, const Strategys &inputs) {
+inline StrategyPtr NewStrategy(const int32_t stage, const std::vector<Dimensions> &inputs) {
   return std::make_shared<Strategy>(stage, inputs);
 }
 }  // namespace parallel

mindspore/ccsrc/frontend/parallel/strategy_checkpoint/parallel_strategy_checkpoint.cc

@@ -66,10 +66,10 @@ Status StrategyCheckpoint::Load(StrategyMap *strategy_map) {
     straspb::ParallelStrategys parallel_strategys = parallel_strategy_item.parallel_strategys();
     auto stage = (int32_t)parallel_strategys.stage();
     size_t strategys_num = IntToSize(parallel_strategys.parallel_strategy_size());
-    Strategys strategy_inputs;
+    std::vector<std::vector<int32_t>> strategy_inputs;
     for (size_t j = 0; j < strategys_num; j++) {
       straspb::ParallelStrategy parallel_strategy = parallel_strategys.parallel_strategy(SizeToInt(j));
-      Dimensions dimension;
+      std::vector<int32_t> dimension;
       size_t dim_num = IntToSize(parallel_strategy.dim_size());
       for (size_t k = 0; k < dim_num; k++) {
         dimension.push_back(parallel_strategy.dim(SizeToInt(k)));

mindspore/ccsrc/frontend/parallel/tensor_layout/arrangement.cc

@@ -26,7 +26,7 @@
 
 namespace mindspore {
 namespace parallel {
-Status Arrangement::Init(const Shape &array) {
+Status Arrangement::Init(const std::vector<int32_t> &array) {
   Status status = Array::Init(array);
   if (status != Status::SUCCESS) {
     return Status::FAILED;
@@ -40,7 +40,7 @@ Status Arrangement::Init(const Shape &array) {
 }
 
 bool Arrangement::IsValidArrangement() {
-  return !std::any_of(array_.begin(), array_.end(), [](int64_t value) { return value <= 0; });
+  return !std::any_of(array_.begin(), array_.end(), [](int32_t value) { return value <= 0; });
 }
 
 void Arrangement::ComputeSize() {
@@ -57,14 +57,14 @@ void Arrangement::ComputeSize() {
  * where size_[i-1] = shape[0] * shape[1] * ... * shape[i-1],
  * if value > size_, return []
  */
-Shape Arrangement::GetFrontElementByValue(int64_t value) const {
-  Shape out;
+std::vector<int32_t> Arrangement::GetFrontElementByValue(int32_t value) const {
+  std::vector<int32_t> out;
   if (GetDimSize() == 0) {
     return out;
   }
   if (value <= size_) {
-    int64_t size = 1;
-    size_t shape_list_idx = 0;
+    int32_t size = 1;
+    uint32_t shape_list_idx = 0;
     while (size < value) {
       size *= array_[shape_list_idx];
       if (size <= value) {
@@ -88,9 +88,9 @@ std::shared_ptr<Arrangement> Arrangement::GetExpandedShapeByExpandListRemoveLeft
   if (expand_list.size() != GetDimSize()) {
     return nullptr;
   }
-  Shape new_shape;
-  for (size_t i = 0; i < expand_list.size(); i++) {
-    Shape expand_shape = expand_list[i].GetFrontElementByValue(GetDimByIdx(i));
+  std::vector<int32_t> new_shape;
+  for (uint32_t i = 0; i < expand_list.size(); i++) {
+    std::vector<int32_t> expand_shape = expand_list[i].GetFrontElementByValue(GetDimByIdx(i));
     if (expand_shape.empty()) {
       new_shape.push_back(GetDimByIdx(i));
     } else {
@@ -109,11 +109,11 @@ std::shared_ptr<Arrangement> Arrangement::GetExpandedShapeByExpandListRemoveLeft
  *    arrangement_list = [[4, 2], [2, 2]]
  */
 std::shared_ptr<std::vector<Arrangement>> Arrangement::GetExpandShapeList(const Arrangement &expand_shape) const {
-  int64_t size = 1;
-  size_t ind = 0;
+  int32_t size = 1;
+  uint32_t ind = 0;
   std::vector<Arrangement> arrangement_list;
-  Shape shape;
-  for (size_t i = 0; i < expand_shape.GetDimSize(); i++) {
+  std::vector<int32_t> shape;
+  for (uint32_t i = 0; i < expand_shape.GetDimSize(); i++) {
     size *= expand_shape.GetDimByIdx(i);
     if (size > GetDimByIdx(ind)) {
       MS_LOG(ERROR) << "invalid expand_shape";
@@ -145,7 +145,7 @@ std::shared_ptr<std::pair<std::vector<Arrangement>, Arrangement>> Arrangement::G
   if (expand_shape_list_ptr == nullptr) {
     return nullptr;
   }
-  Shape expand_num_list_shape;
+  std::vector<int32_t> expand_num_list_shape;
   (void)std::transform(expand_shape_list_ptr->begin(), expand_shape_list_ptr->end(),
                        std::back_inserter(expand_num_list_shape),
                        [](const Arrangement &arr) { return SizeToInt(arr.GetDimSize()); });
@@ -158,9 +158,9 @@ std::shared_ptr<std::pair<std::vector<Arrangement>, Arrangement>> Arrangement::G
   return std::make_shared<std::pair<std::vector<Arrangement>, Arrangement>>(out_value);
 }
 
-Shape Arrangement::ComputeReverseAccumulateSumInReverseOrder() const {
-  Shape shape_accum;
-  int64_t size = 0;
+std::vector<int32_t> Arrangement::ComputeReverseAccumulateSumInReverseOrder() const {
+  std::vector<int32_t> shape_accum;
+  int32_t size = 0;
   for (auto iter = array_.end() - 1; iter >= array_.begin(); --iter) {
     shape_accum.push_back(size);
     size += *iter;
@@ -173,11 +173,11 @@ std::shared_ptr<Arrangement> Arrangement::GetExpandedShapeByExpandListReserveLef
   if (expand_list.size() != GetDimSize()) {
     return nullptr;
   }
-  Shape new_shape;
-  for (size_t i = 0; i < expand_list.size(); i++) {
+  std::vector<int32_t> new_shape;
+  for (uint32_t i = 0; i < expand_list.size(); i++) {
     if (expand_list[i].GetDimSize() >= 1) {
-      int64_t size = 1;
-      for (size_t k = 0; k < expand_list[i].GetDimSize() - 1; k++) {
+      int32_t size = 1;
+      for (uint32_t k = 0; k < expand_list[i].GetDimSize() - 1; k++) {
         new_shape.push_back(expand_list[i].GetDimByIdx(k));
         size *= expand_list[i].GetDimByIdx(k);
       }
@@ -207,7 +207,7 @@ std::shared_ptr<Arrangement> Arrangement::GetUnifiedShape(const Arrangement &in2
   if (status != Status::SUCCESS) {
     return nullptr;
   }
-  Shape out_shape;
+  std::vector<int32_t> out_shape;
   status = AccumulateProductToShape(out_accum, &out_shape);
   if (status != Status::SUCCESS) {
     return nullptr;
@@ -231,8 +231,8 @@ std::vector<size_t> Arrangement::GetSqueezeIdx() const {
 }
 
 Arrangement Arrangement::GetSqueezeArrangement() const {
-  Shape out_shape(array_.size());
-  auto it = std::copy_if(array_.begin(), array_.end(), out_shape.begin(), [](int64_t value) { return value != 1; });
+  std::vector<int32_t> out_shape(array_.size());
+  auto it = std::copy_if(array_.begin(), array_.end(), out_shape.begin(), [](int32_t value) { return value != 1; });
   out_shape.resize(LongToSize(std::distance(out_shape.begin(), it)));
 
   // if all elements are 1, out_shape = {1}

mindspore/ccsrc/frontend/parallel/tensor_layout/arrangement.h

@@ -32,11 +32,11 @@ class Arrangement : public Array {
  public:
   Arrangement() : size_(1) {}
   ~Arrangement() override = default;
-  Status Init(const Shape &array) override;
-  int64_t size() const { return size_; }
-  Shape GetFrontElementByValue(int64_t value) const;
+  Status Init(const std::vector<int32_t> &array) override;
+  int32_t size() const { return size_; }
+  std::vector<int32_t> GetFrontElementByValue(int32_t value) const;
   std::shared_ptr<std::vector<Arrangement>> GetExpandShapeList(const Arrangement &expand_shape) const;
-  Shape ComputeReverseAccumulateSumInReverseOrder() const;
+  std::vector<int32_t> ComputeReverseAccumulateSumInReverseOrder() const;
   std::shared_ptr<Arrangement> GetExpandedShapeByExpandListReserveLeft(
     const std::vector<Arrangement> &expand_list) const;
   std::shared_ptr<Arrangement> GetExpandedShapeByExpandListRemoveLeft(
@@ -50,7 +50,7 @@ class Arrangement : public Array {
  private:
   bool IsValidArrangement();
   void ComputeSize();
-  int64_t size_;
+  int32_t size_;
 };
 }  // namespace parallel
 }  // namespace mindspore

mindspore/ccsrc/frontend/parallel/tensor_layout/array.cc

@@ -31,14 +31,14 @@ std::string Array::ToString() const {
   return buffer.str();
 }
 
-Status Array::Init(const Shape &array) {
+Status Array::Init(const std::vector<int32_t> &array) {
   array_ = array;
   return IsvalidArray() ? Status::SUCCESS : Status::FAILED;
 }
 
 bool Array::IsvalidArray() const { return true; }
 
-int64_t Array::GetDimByIdx(size_t idx) const {
+int32_t Array::GetDimByIdx(uint32_t idx) const {
   size_t mod_idx = idx;
   if (idx >= GetDimSize()) {
     MS_LOG(EXCEPTION) << "idx is " << idx << ", but array size is " << GetDimSize();
@@ -46,7 +46,7 @@ int64_t Array::GetDimByIdx(size_t idx) const {
   return array_[mod_idx];
 }
 
-int64_t Array::GetDimByReverseIdx(size_t idx) const {
+int32_t Array::GetDimByReverseIdx(uint32_t idx) const {
   size_t mod_idx = idx;
   if (idx >= GetDimSize()) {
     MS_LOG(EXCEPTION) << "idx is " << idx << " but array size is " << GetDimSize();

mindspore/ccsrc/frontend/parallel/tensor_layout/array.h

@@ -23,7 +23,6 @@
 #include <string>
 #include <vector>
 #include "frontend/parallel/status.h"
-#include "frontend/parallel/device_matrix.h"
 
 namespace mindspore {
 namespace parallel {
@@ -32,16 +31,16 @@ class Array {
   Array() = default;
   virtual ~Array() = default;
   std::string ToString() const;
-  virtual Status Init(const Shape &array);
+  virtual Status Init(const std::vector<int32_t> &array);
   bool IsvalidArray() const;
-  Shape array() const { return array_; }
+  std::vector<int32_t> array() const { return array_; }
   size_t GetDimSize() const { return array_.size(); }
-  int64_t GetDimByIdx(size_t idx) const;
-  int64_t GetDimByReverseIdx(size_t idx) const;
+  int32_t GetDimByIdx(uint32_t idx) const;
+  int32_t GetDimByReverseIdx(uint32_t idx) const;
   bool operator==(const Array &a1) const;
 
  protected:
-  Shape array_;
+  std::vector<int32_t> array_;
 };
 }  // namespace parallel
 }  // namespace mindspore

mindspore/ccsrc/frontend/parallel/tensor_layout/construct_operator.cc

@@ -18,7 +18,6 @@
 
 #include <functional>
 #include <numeric>
-#include <algorithm>
 
 namespace mindspore {
 namespace parallel {
@@ -43,8 +42,8 @@ OperatorVector ConstructOperator::SkipRedisReshapeOP(Shape shape) {
 }
 
 Status ConstructOperator::ReshapeOP(Shape shape) {
-  int64_t prod = std::accumulate(shape.begin(), shape.end(), 1, std::multiplies<int64_t>());
-  int64_t prod_expect = std::accumulate(tensor_shape_.begin(), tensor_shape_.end(), 1, std::multiplies<int64_t>());
+  int32_t prod = std::accumulate(shape.begin(), shape.end(), 1, std::multiplies<int>());
+  int32_t prod_expect = std::accumulate(tensor_shape_.begin(), tensor_shape_.end(), 1, std::multiplies<int>());
   if (prod != prod_expect) {
     ValuePtr ptr = MakeValue(shape);
     MS_EXCEPTION_IF_NULL(ptr);
@@ -69,21 +68,12 @@ Operator CreateStridedSliceOp(int32_t value, const Shape &begin, const Shape &en
   Attr attr_shrink_axis_mask = std::make_pair(SHRINK_AXIS_MASK, attr_value);
   OperatorAttrs attrs = {attr_begin_mask, attr_end_mask, attr_ellipsis_mask, attr_new_axis_mask, attr_shrink_axis_mask};
 
-  std::vector<int32_t> begin_int;
-  (void)std::transform(begin.begin(), begin.end(), std::back_inserter(begin_int),
-                       [](const int64_t &value) { return static_cast<int32_t>(value); });
-  ValuePtr param_begin_value = MakeValue(begin_int);
+  ValuePtr param_begin_value = MakeValue(begin);
   Param param_begin = std::make_pair(std::make_pair(BEGIN, param_begin_value), 2);
-  std::vector<int32_t> end_int;
-  (void)std::transform(end.begin(), end.end(), std::back_inserter(end_int),
-                       [](const int64_t &value) { return static_cast<int32_t>(value); });
-  ValuePtr param_end_value = MakeValue(end_int);
+  ValuePtr param_end_value = MakeValue(end);
   Param param_end = std::make_pair(std::make_pair(END, param_end_value), 3);
 
-  std::vector<int32_t> strides_int;
-  (void)std::transform(strides.begin(), strides.end(), std::back_inserter(strides_int),
-                       [](const int64_t &value) { return static_cast<int32_t>(value); });
-  ValuePtr param_strides_value = MakeValue(strides_int);
+  ValuePtr param_strides_value = MakeValue(strides);
   Param param_strides = std::make_pair(std::make_pair(STRIDES, param_strides_value), 4);
   OperatorParams params = {param_begin, param_end, param_strides};
   OperatorArgs op_args = std::make_pair(attrs, params);
@@ -96,16 +86,16 @@ Status ConstructOperator::StridedSliceOP(Args args) {
     MS_LOG(ERROR) << "args size should not be less than 3!";
     return Status::FAILED;
   }
-  int64_t split_count = args[0];
+  int32_t split_count = args[0];
   if (split_count <= 0) {
     MS_LOG(ERROR) << "split_count should not be less than 0!";
     return Status::FAILED;
   }
-  int64_t split_dim = args[1];
-  int64_t dev_dim = args[2];
+  int32_t split_dim = args[1];
+  int32_t dev_dim = args[2];
   std::vector<Group> group_list;
 
-  if (CreateGroupByDim(dev_size_ - LongToSize(dev_dim) - 1, &group_list) != SUCCESS) {
+  if (CreateGroupByDim(dev_size_ - IntToSize(dev_dim) - 1, &group_list) != SUCCESS) {
     MS_LOG(ERROR) << "stride slice op: create group failed";
     return FAILED;
   } else if (group_list.empty()) {  // this group only has one device, don't need do StridedSlice
@@ -124,7 +114,7 @@ Status ConstructOperator::StridedSliceOP(Args args) {
   Shape strides(size, 1);
   size_t index = 0;
   for (auto num : tensor_shape_) {
-    if (index != LongToSize(split_dim)) {
+    if (index != IntToSize(split_dim)) {
       begin[index] = 0;
       end[index] = num;
     } else {
@@ -133,9 +123,9 @@ Status ConstructOperator::StridedSliceOP(Args args) {
                       << "! when construct StridedSlice operator";
         return Status::INVALID_ARGUMENT;
       }
-      int64_t count = num / split_count;
-      begin[index] = SizeToLong(rank) * count;
-      end[index] = (SizeToLong(rank) + 1) * count;
+      int32_t count = num / split_count;
+      begin[index] = SizeToInt(rank) * count;
+      end[index] = (SizeToInt(rank) + 1) * count;
     }
     index++;
   }
@@ -145,7 +135,7 @@ Status ConstructOperator::StridedSliceOP(Args args) {
   return Status::SUCCESS;
 }
 
-Status ConstructOperator::AllGatherOP(int64_t dev_dim) {
+Status ConstructOperator::AllGatherOP(int32_t dev_dim) {
   if ((IntToSize(dev_dim) >= dev_size_) || (dev_dim < 0)) {
     MS_LOG(ERROR) << "Invalid device dimension " << dev_dim << " when construct AllGather operator!";
     return Status::INVALID_ARGUMENT;
@@ -170,7 +160,7 @@ Status ConstructOperator::AllGatherOP(int64_t dev_dim) {
   return Status::SUCCESS;
 }
 
-Status ConstructOperator::ConcatOP(int64_t concat_dim) {
+Status ConstructOperator::ConcatOP(int32_t concat_dim) {
   if (IntToSize(concat_dim) >= tensor_shape_.size()) {
     MS_LOG(ERROR) << "Invalid tensor dimension " << concat_dim << " when construct Concat operator!";
     return Status::INVALID_ARGUMENT;
@@ -184,7 +174,7 @@ Status ConstructOperator::ConcatOP(int64_t concat_dim) {
   return Status::SUCCESS;
 }
 
-Status ConstructOperator::SplitOP(int64_t split_count) {
+Status ConstructOperator::SplitOP(int32_t split_count) {
   if (split_count <= 0) {
     MS_LOG(ERROR) << "Invalid split count when construct Split operator!";
     return Status::FAILED;
@@ -206,30 +196,30 @@ Status ConstructOperator::AlltoAllOP(Args args) {
     MS_LOG(ERROR) << "args size should not be less than 4!";
     return Status::FAILED;
   }
-  int64_t split_count = args[0];
-  int64_t split_dim = args[1];
-  int64_t concat_dim = args[2];
-  int64_t dev_dim = args[3];
+  int32_t split_count = args[0];
+  int32_t split_dim = args[1];
+  int32_t concat_dim = args[2];
+  int32_t dev_dim = args[3];
   if (split_count <= 0) {
     MS_LOG(ERROR) << "Invalid split count when construct AlltoAll operator!";
     return Status::FAILED;
   }
-  if (tensor_shape_[LongToSize(split_dim)] % split_count != 0) {
+  if (tensor_shape_[IntToSize(split_dim)] % split_count != 0) {
     MS_LOG(ERROR) << "Tensor can not be split into " << split_count << " slices in the dimension " << split_dim
                   << "when construct AlltoAll operator!";
     return Status::INVALID_ARGUMENT;
   }
-  if (LongToSize(concat_dim) >= tensor_shape_.size()) {
+  if (IntToSize(concat_dim) >= tensor_shape_.size()) {
     MS_LOG(ERROR) << "Invalid split count " << split_count << " when construct AlltoAll operator!";
     return Status::INVALID_ARGUMENT;
   }
-  if ((LongToSize(dev_dim) >= dev_size_) || (dev_dim < 0)) {
+  if ((IntToSize(dev_dim) >= dev_size_) || (dev_dim < 0)) {
     MS_LOG(ERROR) << "Invalid device dimension " << dev_dim << " when construct AlltoAll operator!";
     return Status::INVALID_ARGUMENT;
   }
 
   std::vector<Group> group_list;
-  if (CreateGroupByDim(dev_size_ - LongToSize(dev_dim) - 1, &group_list) != SUCCESS) {
+  if (CreateGroupByDim(dev_size_ - IntToSize(dev_dim) - 1, &group_list) != SUCCESS) {
     MS_LOG(ERROR) << "AlltoAll op: create group failed";
     return FAILED;
   } else if (group_list.empty()) {  // this group only has one device, don't need do alltoall

mindspore/ccsrc/frontend/parallel/tensor_layout/construct_operator.h

@@ -27,7 +27,7 @@
 
 namespace mindspore {
 namespace parallel {
-using Args = std::vector<std::int64_t>;
+using Args = std::vector<std::int32_t>;
 
 class ConstructOperator {
  public:
@@ -38,9 +38,9 @@ class ConstructOperator {
   OperatorVector SkipRedisReshapeOP(Shape shape);
   Status ReshapeOP(Shape shape);
   Status StridedSliceOP(Args args);
-  Status AllGatherOP(int64_t dev_dim);
-  Status SplitOP(int64_t split_count);
-  Status ConcatOP(int64_t concat_dim);
+  Status AllGatherOP(int32_t dev_dim);
+  Status SplitOP(int32_t split_count);
+  Status ConcatOP(int32_t concat_dim);
   Status AlltoAllOP(Args args);
   Operator GetOperator() const { return op_; }
   void UpdateTensorShape(const Shape &tensor_shape) { tensor_shape_ = tensor_shape; }

mindspore/ccsrc/frontend/parallel/tensor_layout/map.cc

@@ -26,7 +26,7 @@
 
 namespace mindspore {
 namespace parallel {
-Status Map::Init(const Shape &array) {
+Status Map::Init(const std::vector<int32_t> &array) {
   Status status = Array::Init(array);
   if (status != Status::SUCCESS) {
     return Status::FAILED;
@@ -39,11 +39,11 @@ Status Map::Init(const Shape &array) {
 }
 
 bool Map::IsValidMap() {
-  if (std::any_of(array_.begin(), array_.end(), [](int64_t value) { return ((value < 0) && (value != MAP_NONE)); })) {
+  if (std::any_of(array_.begin(), array_.end(), [](int32_t value) { return ((value < 0) && (value != MAP_NONE)); })) {
     return false;
   }
   // check that all none -1 value in array_ is different
-  Shape sorted_array = array_;
+  std::vector<int32_t> sorted_array = array_;
   std::sort(sorted_array.begin(), sorted_array.end());
   int32_t value = MAP_NONE;
   for (auto &element : sorted_array) {
@@ -58,7 +58,7 @@ bool Map::IsValidMap() {
   return true;
 }
 
-int64_t Map::GetMaxItem() const {
+int32_t Map::GetMaxItem() const {
   if (!array_.empty()) {
     return *std::max_element(array_.begin(), array_.end());
   } else {
@@ -66,7 +66,7 @@ int64_t Map::GetMaxItem() const {
   }
 }
 
-int32_t Map::GetIndexByValue(int64_t value) const {
+int32_t Map::GetIndexByValue(int32_t value) const {
   auto iter = find(array_.begin(), array_.end(), value);
   if (iter != array_.end()) {
     return static_cast<int32_t>(std::distance(array_.begin(), iter));
@@ -82,15 +82,15 @@ std::shared_ptr<Map> Map::ExpandMapByNone(const Arrangement &expand_num_list) co
   if (expand_num_list.GetDimSize() != GetDimSize()) {
     return nullptr;
   }
-  Shape new_shape;
-  for (size_t i = 0; i != GetDimSize(); i++) {
+  std::vector<int32_t> new_shape;
+  for (uint32_t i = 0; i != GetDimSize(); i++) {
     if (GetDimByIdx(i) == MAP_NONE) {
-      for (int64_t j = 0; j < expand_num_list.GetDimByIdx(i); j++) {
+      for (int32_t j = 0; j < expand_num_list.GetDimByIdx(i); j++) {
         new_shape.push_back(MAP_NONE);
       }
     } else {
       new_shape.push_back(GetDimByIdx(i));
-      int64_t j = 1;
+      int32_t j = 1;
       while (j < expand_num_list.GetDimByIdx(i)) {
         new_shape.push_back(MAP_NONE);
         j++;
@@ -106,17 +106,17 @@ std::shared_ptr<Map> Map::ExpandMapByNone(const Arrangement &expand_num_list) co
  * expand.size() should be equal to array_.size()
  */
 std::shared_ptr<Map> Map::ExpandMapByDecreaseNumber(const Arrangement &expand_num_list) const {
-  if (GetMaxItem() >= static_cast<int64_t>(expand_num_list.GetDimSize())) {
+  if (GetMaxItem() >= static_cast<int32_t>(expand_num_list.GetDimSize())) {
     return nullptr;
   }
-  Shape new_shape;
-  for (size_t i = 0; i < GetDimSize(); i++) {
+  std::vector<int32_t> new_shape;
+  for (uint32_t i = 0; i < GetDimSize(); i++) {
     if (GetDimByIdx(i) == MAP_NONE) {
       new_shape.push_back(MAP_NONE);
     } else {
-      int64_t start_map =
-        expand_num_list.ComputeReverseAccumulateSumInReverseOrder()[static_cast<size_t>(GetDimByIdx(i))];
-      for (int32_t k = expand_num_list.GetDimByReverseIdx(static_cast<size_t>(GetDimByIdx(i))) - 1; k >= 0; k--) {
+      int32_t start_map =
+        expand_num_list.ComputeReverseAccumulateSumInReverseOrder()[static_cast<uint32_t>(GetDimByIdx(i))];
+      for (int32_t k = expand_num_list.GetDimByReverseIdx(static_cast<uint32_t>(GetDimByIdx(i))) - 1; k >= 0; k--) {
         new_shape.push_back(k + start_map);
       }
     }
@@ -127,16 +127,16 @@ std::shared_ptr<Map> Map::ExpandMapByDecreaseNumber(const Arrangement &expand_nu
 }
 
 std::shared_ptr<std::vector<Arrangement>> Map::ReMapVector(const std::vector<Arrangement> &input_vector) const {
-  if (GetMaxItem() >= static_cast<int64_t>(input_vector.size())) {
+  if (GetMaxItem() >= static_cast<int32_t>(input_vector.size())) {
     return nullptr;
   }
   std::vector<Arrangement> out;
   Arrangement empty_arrangement;
-  for (size_t i = 0; i < GetDimSize(); i++) {
+  for (uint32_t i = 0; i < GetDimSize(); i++) {
     if (GetDimByIdx(i) == MAP_NONE) {
       out.push_back(empty_arrangement);
     } else {
-      out.push_back(input_vector[input_vector.size() - 1 - LongToSize(GetDimByIdx(i))]);
+      out.push_back(input_vector[IntToUint(SizeToInt(input_vector.size()) - 1 - GetDimByIdx(i))]);
     }
   }
   return std::make_shared<std::vector<Arrangement>>(out);
@@ -144,7 +144,7 @@ std::shared_ptr<std::vector<Arrangement>> Map::ReMapVector(const std::vector<Arr
 
 bool Map::CheckNoneByIdxList(std::vector<size_t> idx_list) const {
   for (auto &value : idx_list) {
-    if (GetDimByIdx(value) != MAP_NONE) {
+    if (GetDimByIdx(SizeToUint(value)) != MAP_NONE) {
       return false;
     }
   }
@@ -152,11 +152,11 @@ bool Map::CheckNoneByIdxList(std::vector<size_t> idx_list) const {
 }
 
 Map Map::SqueezeMapByIdxList(std::vector<size_t> idx_list) const {
-  Shape out_shape;
+  std::vector<int32_t> out_shape;
   for (size_t i = 0; i < GetDimSize(); i++) {
     auto it = std::find(idx_list.begin(), idx_list.end(), i);
     if (it == idx_list.end()) {
-      out_shape.push_back(GetDimByIdx(i));
+      out_shape.push_back(GetDimByIdx(SizeToUint(i)));
     }
   }
   if (out_shape.empty()) {

mindspore/ccsrc/frontend/parallel/tensor_layout/map.h

@@ -34,9 +34,9 @@ class Map : public Array {
  public:
   Map() = default;
   ~Map() override = default;
-  Status Init(const Shape &array) override;
-  int64_t GetMaxItem() const;
-  int32_t GetIndexByValue(int64_t value) const;
+  Status Init(const std::vector<int32_t> &array) override;
+  int32_t GetMaxItem() const;
+  int32_t GetIndexByValue(int32_t value) const;
   std::shared_ptr<Map> ExpandMapByNone(const Arrangement &expand_num_list) const;
   std::shared_ptr<Map> ExpandMapByDecreaseNumber(const Arrangement &expand_num_list) const;
   std::shared_ptr<std::vector<Arrangement>> ReMapVector(const std::vector<Arrangement> &input_vector) const;

mindspore/ccsrc/frontend/parallel/tensor_layout/redistribution_operator_infer.cc

@@ -47,8 +47,8 @@ Status RedistributionOperatorInfer::Init(const TensorLayout &tensor_layout, cons
   constructor_.UpdateTensorShape(cur_tensor_layout_.slice_shape().array());
 
   size_t key = 0;
-  Shape map = in_tensor_map_.array();
-  for (int64_t item : map) {
+  std::vector<int32_t> map = in_tensor_map_.array();
+  for (int32_t item : map) {
     map_[key++] = item;
   }
 
@@ -83,9 +83,9 @@ Status RedistributionOperatorInfer::InferRedistributionOperator() {
     // break loop structure with concat_by_axis
     if (len_global == operator_list_.size() && !map_.empty()) {
       size_t index = map_.begin()->first;
-      int64_t in_dim = map_[index];
+      int32_t in_dim = map_[index];
       map_[index] = NONE;
-      Args args = {SizeToInt(index), in_dim, dev_mat_.GetDimByReverseIdx(LongToSize(in_dim))};
+      Args args = {SizeToInt(index), in_dim, dev_mat_.GetDimByReverseIdx(IntToUint(in_dim))};
       if (InsertOperator(CONCAT_BY_AXIS, args) == Status::FAILED) {
         return Status::FAILED;
       }
@@ -97,8 +97,8 @@ Status RedistributionOperatorInfer::InferRedistributionOperator() {
 Status RedistributionOperatorInfer::InferSplitByAxis() {
   for (auto iter = map_.begin(); iter != map_.end();) {
     uint32_t index = iter->first;
-    int64_t in_dim = iter->second;
-    int64_t out_dim = out_tensor_map_.GetDimByIdx(index);
+    int32_t in_dim = iter->second;
+    int32_t out_dim = out_tensor_map_.GetDimByIdx(index);
     if (in_dim == out_dim) {
       (void)map_.erase(iter++);
       continue;
@@ -122,8 +122,8 @@ Status RedistributionOperatorInfer::InferSplitByAxis() {
 Status RedistributionOperatorInfer::InferPermuteByAxis() {
   for (auto iter = map_.begin(); iter != map_.end();) {
     uint32_t index = iter->first;
-    int64_t in_dim = map_[index];
-    int64_t out_dim = out_tensor_map_.GetDimByIdx(index);
+    int32_t in_dim = map_[index];
+    int32_t out_dim = out_tensor_map_.GetDimByIdx(index);
     if (in_dim == out_dim) {
       (void)map_.erase(iter++);
       continue;
@@ -132,9 +132,9 @@ Status RedistributionOperatorInfer::InferPermuteByAxis() {
         std::any_of(map_.begin(), map_.end(),
                     [out_dim](const RedistributionOperatorMap::value_type &a) { return a.second == out_dim; })) {
       int32_t cat_dim = in_tensor_map_.GetIndexByValue(out_dim);
-      int64_t dev_num = dev_mat_.GetDimByReverseIdx(LongToSize(out_dim));
+      int32_t dev_num = dev_mat_.GetDimByReverseIdx(IntToUint(out_dim));
       if (is_cost_model_) {
-        int64_t dev_dim = in_tensor_map_.GetDimByIdx(IntToUint(cat_dim));
+        int32_t dev_dim = in_tensor_map_.GetDimByIdx(IntToUint(cat_dim));
         Args args_alltoall = {dev_mat_.GetDimByReverseIdx(IntToUint(dev_dim)), UintToInt(index), cat_dim, dev_dim,
                               dev_num};
         if (InsertOperator(PERMUTE_BY_AXIS, args_alltoall) == Status::FAILED) {
@@ -165,10 +165,10 @@ Status RedistributionOperatorInfer::InferPermuteByAxis() {
 Status RedistributionOperatorInfer::InferConcatByAxis() {
   for (auto iter = map_.begin(); iter != map_.end();) {
     uint32_t index = iter->first;
-    int64_t in_dim = map_[index];
-    int64_t out_dim = out_tensor_map_.GetDimByIdx(index);
+    int32_t in_dim = map_[index];
+    int32_t out_dim = out_tensor_map_.GetDimByIdx(index);
     if (in_dim != NONE && out_tensor_map_.GetIndexByValue(in_dim) == NONE) {
-      Args args = {SizeToInt(index), in_dim, dev_mat_.GetDimByReverseIdx(LongToSize(in_dim))};
+      Args args = {SizeToInt(index), in_dim, dev_mat_.GetDimByReverseIdx(IntToUint(in_dim))};
       if (InsertOperator(CONCAT_BY_AXIS, args) == Status::FAILED) {
         MS_LOG(ERROR) << "Insert ConcatByAxis Error!";
         return Status::FAILED;
@@ -215,7 +215,7 @@ Status RedistributionOperatorInfer::TransferSplitByAxis(Args args) {
     MS_LOG(ERROR) << "args size should not be less than 3!";
     return Status::FAILED;
   }
-  size_t index = LongToSize(args[1]);
+  uint32_t index = IntToUint(args[1]);
   if (constructor_.StridedSliceOP(args) != Status::SUCCESS) {
     return Status::FAILED;
   } else {
@@ -239,11 +239,11 @@ Status RedistributionOperatorInfer::TransferPermuteByAxis(Args args) {
     operator_vector_.push_back(constructor_.GetOperator());
     output_info_vector_.push_back(std::make_pair(false, 0));
   }
-  size_t index = LongToSize(args[1]);
-  int64_t val = args[2];
-  int64_t out_dim = out_tensor_map_.GetDimByIdx(index);
+  uint32_t index = IntToUint(args[1]);
+  int32_t val = args[2];
+  int32_t out_dim = out_tensor_map_.GetDimByIdx(index);
 
-  if (cur_tensor_layout_.UpdateTensorMap(LongToSize(val), NONE) == Status::FAILED) {
+  if (cur_tensor_layout_.UpdateTensorMap(IntToUint(val), NONE) == Status::FAILED) {
     return Status::FAILED;
   }
   if (cur_tensor_layout_.UpdateTensorMap(index, out_dim) == Status::FAILED) {
@@ -257,9 +257,9 @@ Status RedistributionOperatorInfer::TransferConcatByAxis(Args args) {
     MS_LOG(ERROR) << "args size should not be less than 3!";
     return Status::FAILED;
   }
-  int64_t tensor_dim = args[0];
-  int64_t dev_dim = args[1];
-  int64_t split_count = args[2];
+  int32_t tensor_dim = args[0];
+  int32_t dev_dim = args[1];
+  int32_t split_count = args[2];
   if (constructor_.AllGatherOP(dev_dim) != Status::SUCCESS) {
     return Status::FAILED;
   } else {
@@ -280,7 +280,7 @@ Status RedistributionOperatorInfer::TransferConcatByAxis(Args args) {
       output_info_vector_.push_back(std::make_pair(false, 0));
     }
   }
-  if (cur_tensor_layout_.UpdateTensorMap(LongToSize(tensor_dim), NONE) == Status::FAILED) {
+  if (cur_tensor_layout_.UpdateTensorMap(IntToUint(tensor_dim), NONE) == Status::FAILED) {
     return Status::FAILED;
   }
   return Status::SUCCESS;

mindspore/ccsrc/frontend/parallel/tensor_layout/redistribution_operator_infer.h

@@ -28,10 +28,10 @@
 #include "utils/convert_utils.h"
 namespace mindspore {
 namespace parallel {
-using DeviceArrangement = Shape;
-using TensorMap = Shape;
-using TensorShape = Shape;
-using RedistributionOperatorMap = std::unordered_map<uint32_t, int64_t>;
+using DeviceArrangement = std::vector<int32_t>;
+using TensorMap = std::vector<int32_t>;
+using TensorShape = std::vector<int32_t>;
+using RedistributionOperatorMap = std::unordered_map<uint32_t, int32_t>;
 using OperatorR = std::pair<OperatorName, Args>;
 using OperatorC = std::pair<OperatorR, Shape>;
 using OperatorList = std::vector<OperatorC>;

mindspore/ccsrc/frontend/parallel/tensor_layout/shape_util.cc

@@ -26,7 +26,7 @@ namespace parallel {
  * shape = [2, 8, 32]
  * shape_accum = [2, 2 * 8, 2 * 8 * 32]
  */
-Status ShapeToAccumulateProduct(const Shape &shape, Shape *shape_accum) {
+Status ShapeToAccumulateProduct(const std::vector<int32_t> &shape, std::vector<int64_t> *shape_accum) {
   MS_EXCEPTION_IF_NULL(shape_accum);
   shape_accum->clear();
   int64_t size = 1;
@@ -47,7 +47,7 @@ Status ShapeToAccumulateProduct(const Shape &shape, Shape *shape_accum) {
  * shape_accum = [2 * 8 * 32, 8 * 32, 32]
  *
  */
-Status ShapeToAccumulateProductReverse(const Shape &shape, Shape *shape_accum) {
+Status ShapeToAccumulateProductReverse(const std::vector<int32_t> &shape, std::vector<int64_t> *shape_accum) {
   MS_EXCEPTION_IF_NULL(shape_accum);
   shape_accum->clear();
   int64_t size = 1;
@@ -68,7 +68,7 @@ Status ShapeToAccumulateProductReverse(const Shape &shape, Shape *shape_accum) {
  * shape = [2, 8, 32]
  *
  */
-Status AccumulateProductToShape(const Shape &shape_accum, Shape *shape) {
+Status AccumulateProductToShape(const std::vector<int64_t> &shape_accum, std::vector<int32_t> *shape) {
   MS_EXCEPTION_IF_NULL(shape);
   shape->clear();
   int64_t value = 1;
@@ -81,7 +81,7 @@ Status AccumulateProductToShape(const Shape &shape_accum, Shape *shape) {
       MS_LOG(ERROR) << "shape_accum is not a accumulate product in ascending order";
       return Status::FAILED;
     }
-    shape->push_back(static_cast<int64_t>((*iter) / value));
+    shape->push_back(static_cast<int32_t>((*iter) / value));
     value = (*iter);
   }
   return Status::SUCCESS;
@@ -92,7 +92,7 @@ Status AccumulateProductToShape(const Shape &shape_accum, Shape *shape) {
  * shape_accum_reverse = [2 * 8 * 32, 8 * 32, 32]
  * shape = [2, 8, 32]
  */
-Status AccumulateProductReverseToShape(const Shape &shape_accum_reverse, Shape *shape) {
+Status AccumulateProductReverseToShape(const std::vector<int64_t> &shape_accum_reverse, std::vector<int32_t> *shape) {
   MS_EXCEPTION_IF_NULL(shape);
   shape->clear();
   int64_t value = 1;
@@ -105,7 +105,7 @@ Status AccumulateProductReverseToShape(const Shape &shape_accum_reverse, Shape *
       MS_LOG(ERROR) << "shape_accum is not a accumulate product in ascending order";
       return Status::FAILED;
     }
-    (void)shape->insert(shape->begin(), static_cast<int64_t>((*iter) / value));
+    (void)shape->insert(shape->begin(), static_cast<int32_t>((*iter) / value));
     value = *iter;
   }
   return Status::SUCCESS;
@@ -122,7 +122,8 @@ Status AccumulateProductReverseToShape(const Shape &shape_accum_reverse, Shape *
  * in2 = [8, 16]
  * *out = [2, 4, 8, 16]
  */
-Status UnifyAccumulateProduct(const Shape &in1_accum, const Shape &in2_accum, Shape *out_accum) {
+Status UnifyAccumulateProduct(const std::vector<int64_t> &in1_accum, const std::vector<int64_t> &in2_accum,
+                              std::vector<int64_t> *out_accum) {
   MS_EXCEPTION_IF_NULL(out_accum);
   out_accum->clear();
   auto in1_iter = in1_accum.begin();
@@ -158,19 +159,19 @@ Status UnifyAccumulateProduct(const Shape &in1_accum, const Shape &in2_accum, Sh
  * in2 = [2, 16]
  * out = [2, 4, 4]
  */
-Status UnifyShape(const Shape &in1, const Shape &in2, Shape *out) {
+Status UnifyShape(const std::vector<int32_t> &in1, const std::vector<int32_t> &in2, std::vector<int32_t> *out) {
   MS_EXCEPTION_IF_NULL(out);
-  Shape in1_accum;
+  std::vector<int64_t> in1_accum;
   Status status = ShapeToAccumulateProduct(in1, &in1_accum);
   if (status != Status::SUCCESS) {
     return status;
   }
-  Shape in2_accum;
+  std::vector<int64_t> in2_accum;
   status = ShapeToAccumulateProduct(in2, &in2_accum);
   if (status != Status::SUCCESS) {
     return status;
   }
-  Shape out_accum;
+  std::vector<int64_t> out_accum;
   status = UnifyAccumulateProduct(in1_accum, in2_accum, &out_accum);
   if (status != Status::SUCCESS) {
     return status;
@@ -193,8 +194,9 @@ Status UnifyShape(const Shape &in1, const Shape &in2, Shape *out) {
  * expand_accum_reverse = [2 * 4 * 8, 4 * 8, 8]
  * out_accum_reverse = [2 * 4 * 2 * 4 * 8, 4 * 2 * 4 * 8, 2 * 4 * 8, 4 * 8, 8]
  */
-Status ExpandAccumulateProduct(const Shape &in_accum_reverse, const Shape &expand_accum_reverse,
-                               Shape *out_accum_reverse) {
+Status ExpandAccumulateProduct(const std::vector<int64_t> &in_accum_reverse,
+                               const std::vector<int64_t> &expand_accum_reverse,
+                               std::vector<int64_t> *out_accum_reverse) {
   MS_EXCEPTION_IF_NULL(out_accum_reverse);
   out_accum_reverse->clear();
   auto in_riter = in_accum_reverse.rbegin();
@@ -234,19 +236,19 @@ Status ExpandAccumulateProduct(const Shape &in_accum_reverse, const Shape &expan
  * expand = [2, 4, 8]
  * out = [2, 4, 2, 4, 8]
  */
-Status ExpandShape(const Shape &in, const Shape &expand, Shape *out) {
+Status ExpandShape(const std::vector<int32_t> &in, const std::vector<int32_t> &expand, std::vector<int32_t> *out) {
   MS_EXCEPTION_IF_NULL(out);
-  Shape in_accum_reverse;
+  std::vector<int64_t> in_accum_reverse;
   Status status = ShapeToAccumulateProductReverse(in, &in_accum_reverse);
   if (status != Status::SUCCESS) {
     return status;
   }
-  Shape expand_accum_reverse;
+  std::vector<int64_t> expand_accum_reverse;
   status = ShapeToAccumulateProductReverse(expand, &expand_accum_reverse);
   if (status != Status::SUCCESS) {
     return status;
   }
-  Shape out_accum_reverse;
+  std::vector<int64_t> out_accum_reverse;
   status = ExpandAccumulateProduct(in_accum_reverse, expand_accum_reverse, &out_accum_reverse);
   if (status != Status::SUCCESS) {
     return status;

mindspore/ccsrc/frontend/parallel/tensor_layout/shape_util.h

@@ -24,7 +24,6 @@
 #include <vector>
 
 #include "frontend/parallel/status.h"
-#include "frontend/parallel/device_matrix.h"
 
 namespace mindspore {
 namespace parallel {
@@ -40,7 +39,7 @@ namespace parallel {
  * shape_accum = [2, 2 * 8, 2 * 8 * 32]
  *
  */
-Status ShapeToAccumulateProduct(const Shape &shape, Shape *shape_accum);
+Status ShapeToAccumulateProduct(const std::vector<int32_t> &shape, std::vector<int64_t> *shape_accum);
 
 /*
  * compute the accumulating product of all the values in shape from right to left,
@@ -54,7 +53,7 @@ Status ShapeToAccumulateProduct(const Shape &shape, Shape *shape_accum);
  * shape_accum = [2 * 8 * 32, 8 * 32, 32]
  *
  */
-Status ShapeToAccumulateProductReverse(const Shape &shape, Shape *shape_accum);
+Status ShapeToAccumulateProductReverse(const std::vector<int32_t> &shape, std::vector<int64_t> *shape_accum);
 
 /*
  * compute the original shape from the accumulating product shape_accum,
@@ -69,7 +68,7 @@ Status ShapeToAccumulateProductReverse(const Shape &shape, Shape *shape_accum);
  * shape = [2, 8, 32]
  *
  */
-Status AccumulateProductToShape(const Shape &shape_accum, Shape *shape);
+Status AccumulateProductToShape(const std::vector<int64_t> &shape_accum, std::vector<int32_t> *shape);
 
 /*
  * compute the original shape from the accumulating product shape_accum,
@@ -84,7 +83,7 @@ Status AccumulateProductToShape(const Shape &shape_accum, Shape *shape);
  * shape = [2, 8, 32]
  *
  */
-Status AccumulateProductReverseToShape(const Shape &shape_accum_reverse, Shape *shape);
+Status AccumulateProductReverseToShape(const std::vector<int64_t> &shape_accum_reverse, std::vector<int32_t> *shape);
 
 /*
  * given two accumulate product in1_accum and in2_accum, compute the union of in1_accum and in2_accum,
@@ -102,7 +101,8 @@ Status AccumulateProductReverseToShape(const Shape &shape_accum_reverse, Shape *
  * in2_accum = [8, 16]
  * out_accum = [2, 4, 8, 16]
  */
-Status UnifyAccumulateProduct(const Shape &in1_accum, const Shape &in2_accum, Shape *out_accum);
+Status UnifyAccumulateProduct(const std::vector<int64_t> &in1_accum, const std::vector<int64_t> &in2_accum,
+                              std::vector<int64_t> *out_accum);
 
 /*
  * given two shape in1 = [din1_n-1, din1_n-2, ..., din1_0] and in2 = [din2_m-1, din2_m-2, ..., din2_m]
@@ -117,7 +117,7 @@ Status UnifyAccumulateProduct(const Shape &in1_accum, const Shape &in2_accum, Sh
  * in2 = [2, 16]
  * out = [2, 4, 4]
  */
-Status UnifyShape(const Shape &in1, const Shape &in2, Shape *out);
+Status UnifyShape(const std::vector<int32_t> &in1, const std::vector<int32_t> &in2, std::vector<int32_t> *out);
 
 /*
  * given two accumulate product in reverse order of in and expand,
@@ -141,8 +141,9 @@ Status UnifyShape(const Shape &in1, const Shape &in2, Shape *out);
  * expand_accum_reverse = [2 * 4 * 8, 4 * 8, 8]
  * out_accum_reverse = [2 * 4 * 2 * 4 * 8, 4 * 2 * 4 * 8, 2 * 4 * 8, 4 * 8, 8]
  */
-Status ExpandAccumulateProduct(const Shape &in_accum_reverse, const Shape &expand_accum_reverse,
-                               Shape *out_accum_reverse);
+Status ExpandAccumulateProduct(const std::vector<int64_t> &in_accum_reverse,
+                               const std::vector<int64_t> &expand_accum_reverse,
+                               std::vector<int64_t> *out_accum_reverse);
 
 /*
  * given a shape in = [din_n-1, din_n-2, ..., d_0], and the expand shape expand= [dexp_m-1, dexp_m-2, ..., dexp_0],
@@ -164,7 +165,7 @@ Status ExpandAccumulateProduct(const Shape &in_accum_reverse, const Shape &expan
  * expand = [2, 4, 8]
  * out = [2, 4, 2, 4, 8]
  */
-Status ExpandShape(const Shape &in, const Shape &expand, Shape *out);
+Status ExpandShape(const std::vector<int32_t> &in, const std::vector<int32_t> &expand, std::vector<int32_t> *out);
 }  // namespace parallel
 }  // namespace mindspore
 

mindspore/ccsrc/frontend/parallel/tensor_layout/tensor_layout.cc

@@ -64,8 +64,8 @@ Status TensorLayout::Init(const Arrangement &device_arrangement, const Map &tens
   }
 }
 
-Status TensorLayout::InitFromVector(const Shape &device_arrangement, const Shape &tensor_map,
-                                    const Shape &tensor_shape) {
+Status TensorLayout::InitFromVector(const std::vector<int32_t> &device_arrangement,
+                                    const std::vector<int32_t> &tensor_map, const std::vector<int32_t> &tensor_shape) {
   if (device_arrangement_origin_.Init(device_arrangement) != SUCCESS) {
     return FAILED;
   }
@@ -82,7 +82,7 @@ Status TensorLayout::InitFromVector(const Shape &device_arrangement, const Shape
 }
 
 bool TensorLayout::IsValidTensorLayout() const {
-  if (tensor_map_origin_.GetMaxItem() >= static_cast<int64_t>(device_arrangement_origin_.GetDimSize())) {
+  if (tensor_map_origin_.GetMaxItem() >= static_cast<int32_t>(device_arrangement_origin_.GetDimSize())) {
     MS_LOG(ERROR) << "the max element in tensor_map_origin_ must be smaller than device_arrangement_origin_ size!";
     return false;
   }
@@ -114,18 +114,18 @@ bool TensorLayout::TensorShapeDimensionIsDividedBySplitDeviceDimension() const {
 }
 
 void TensorLayout::RemoveElementEqualToOneInDeviceArrangement() {
-  Shape device_arrangement_shape;
-  Shape tensor_map_shape = tensor_map_origin_.array();
-  size_t dev_num = device_arrangement_origin_.GetDimSize();
-  size_t dev_num_left = device_arrangement_origin_.GetDimSize();
-  for (size_t i = 0; i < dev_num; i++) {
+  std::vector<int32_t> device_arrangement_shape;
+  std::vector<int32_t> tensor_map_shape = tensor_map_origin_.array();
+  uint32_t dev_num = SizeToUint(device_arrangement_origin_.GetDimSize());
+  int32_t dev_num_left = SizeToInt(device_arrangement_origin_.GetDimSize());
+  for (uint32_t i = 0; i < dev_num; i++) {
     if (device_arrangement_origin_.GetDimByIdx(i) == 1) {
-      int32_t idx = GetTensorDimensionIndexByDeviceDimensionIndex(static_cast<int64_t>(dev_num - 1 - i));
+      int32_t idx = GetTensorDimensionIndexByDeviceDimensionIndex(static_cast<int32_t>(dev_num - 1 - i));
       if (idx != -1) {
         tensor_map_shape[static_cast<uint32_t>(idx)] = -1;
       }
       for (auto &value : tensor_map_shape) {
-        if (value >= SizeToLong(dev_num_left) - 1 - static_cast<int64_t>(i)) {
+        if (value >= dev_num_left - 1 - static_cast<int32_t>(i)) {
           value--;
         }
       }
@@ -139,7 +139,7 @@ void TensorLayout::RemoveElementEqualToOneInDeviceArrangement() {
 }
 
 // if idx is not in tensor_map, return -1
-int32_t TensorLayout::GetTensorDimensionIndexByDeviceDimensionIndex(int64_t idx) const {
+int32_t TensorLayout::GetTensorDimensionIndexByDeviceDimensionIndex(int32_t idx) const {
   return tensor_map_.GetIndexByValue(idx);
 }
 
@@ -288,7 +288,7 @@ std::shared_ptr<TensorLayout> TensorLayout::ExpandDeviceArrangement(const Arrang
 }
 
 bool TensorLayout::TensorShapeCanBeExpanded(const Arrangement &expand_shape) const {
-  Shape in_expand_shape_shape;
+  std::vector<int32_t> in_expand_shape_shape;
   Status status = ExpandShape(tensor_shape_.array(), expand_shape.array(), &in_expand_shape_shape);
   if (status != Status::SUCCESS) {
     return false;
@@ -297,7 +297,7 @@ bool TensorLayout::TensorShapeCanBeExpanded(const Arrangement &expand_shape) con
 }
 
 std::shared_ptr<Arrangement> TensorLayout::ComputeExpandedTensorShape(const Arrangement &expand_shape) const {
-  Shape in_expand_shape_shape;
+  std::vector<int32_t> in_expand_shape_shape;
   Status status = ExpandShape(tensor_shape_.array(), expand_shape.array(), &in_expand_shape_shape);
   if (status != Status::SUCCESS) {
     return nullptr;
@@ -311,14 +311,14 @@ std::shared_ptr<Arrangement> TensorLayout::ComputeExpandedTensorShape(const Arra
 }
 
 Arrangement TensorLayout::slice_shape() const {
-  Shape shape;
-  for (size_t index = 0; index < tensor_map_.GetDimSize(); index++) {
-    int64_t dim = tensor_map_.GetDimByIdx(index);
-    int64_t num = tensor_shape_.GetDimByIdx(index);
+  std::vector<int32_t> shape;
+  for (uint32_t index = 0; index < tensor_map_.GetDimSize(); index++) {
+    int32_t dim = tensor_map_.GetDimByIdx(index);
+    int32_t num = tensor_shape_.GetDimByIdx(index);
     if (dim == -1) {
       shape.push_back(num);
     } else {
-      int64_t divisor = device_arrangement_.GetDimByReverseIdx(IntToUint(dim));
+      int32_t divisor = device_arrangement_.GetDimByReverseIdx(IntToUint(dim));
       shape.push_back(num / divisor);
     }
   }
@@ -331,7 +331,7 @@ Arrangement TensorLayout::slice_shape() const {
   }
 }
 
-Status TensorLayout::UpdateTensorMap(size_t index, int64_t value) {
+Status TensorLayout::UpdateTensorMap(uint32_t index, int32_t value) {
   if (index >= tensor_map_.GetDimSize()) {
     MS_LOG(ERROR) << "Index is out of the size of the tensor map!";
     return Status::FAILED;

mindspore/ccsrc/frontend/parallel/tensor_layout/tensor_layout.h

@@ -38,7 +38,8 @@ class TensorLayout {
   std::string StandardToString() const;
   std::string OriginToString() const;
   Status Init(const Arrangement &device_arrangement, const Map &tensor_map, const Arrangement &tensor_shape);
-  Status InitFromVector(const Shape &device_arrangement, const Shape &tensor_map, const Shape &tensor_shape);
+  Status InitFromVector(const std::vector<int32_t> &device_arrangement, const std::vector<int32_t> &tensor_map,
+                        const std::vector<int32_t> &tensor_shape);
 
   bool skip_redistribution() const { return skip_redistribution_; }
 
@@ -78,7 +79,7 @@ class TensorLayout {
 
   Arrangement slice_shape() const;
 
-  Status UpdateTensorMap(size_t index, int64_t value);
+  Status UpdateTensorMap(uint32_t index, int32_t value);
 
   TensorLayout SqueezeShape() const;
 
@@ -94,7 +95,7 @@ class TensorLayout {
   int32_t GetSliceDeviceDimensionByTensorDimensionIndex(uint32_t idx) const;
   int32_t GetSliceNumByTensorDimensionIndex(uint32_t idx) const;
   bool TensorShapeDimensionIsDividedBySplitDeviceDimension() const;
-  int32_t GetTensorDimensionIndexByDeviceDimensionIndex(int64_t idx) const;
+  int32_t GetTensorDimensionIndexByDeviceDimensionIndex(int32_t idx) const;
 
   Arrangement device_arrangement_origin_;
   Map tensor_map_origin_;

mindspore/ccsrc/utils/convert_utils.cc

@@ -48,10 +48,6 @@ py::object ValuePtrToPyData(const ValuePtr &value) {
     MS_LOG(DEBUG) << "int";
     py::int_ v = value->cast<Int32ImmPtr>()->value();
     ret = v;
-  } else if (value->isa<Int64Imm>()) {
-    MS_LOG(DEBUG) << "int64";
-    py::int_ v = value->cast<Int64ImmPtr>()->value();
-    ret = v;
   } else if (value->isa<UInt64Imm>()) {
     MS_LOG(DEBUG) << "uint64";
     py::int_ v = value->cast<UInt64ImmPtr>()->value();

mindspore/core/abstract/dshape.h

@@ -25,7 +25,6 @@
 #include <unordered_map>
 #include <typeindex>
 #include <memory>
-#include <algorithm>
 
 #include "utils/log_adapter.h"
 #include "base/base.h"
@@ -64,16 +63,7 @@ class Shape : public BaseShape {
   static const int SHP_ANY = -1;
   Shape() : shape_() {}
   Shape(const std::initializer_list<int> &list) : shape_(list) {}
-  Shape(const std::initializer_list<int64_t> &list) {
-    std::vector<int64_t> list_in(list);
-    (void)std::transform(list_in.begin(), list_in.end(), std::back_inserter(shape_),
-                         [](const int64_t &value) { return static_cast<int>(value); });
-  }
   explicit Shape(const std::vector<int> &list) : shape_(list) {}
-  explicit Shape(const std::vector<int64_t> &list) {
-    (void)std::transform(list.begin(), list.end(), std::back_inserter(shape_),
-                         [](const int64_t &value) { return static_cast<int>(value); });
-  }
   ~Shape() override = default;
   MS_DECLARE_PARENT(Shape, BaseShape)
   std::string ToString() const override;

tests/ut/cpp/parallel/auto_parallel/dp_algo_test.cc

@@ -154,13 +154,13 @@ class TestDPAlgo : public UT::Common {
 void TestDPAlgo::SetUp() {
   cost_graph = std::make_shared<CostGraph>();
   cost_graph->SetDeviceMemoryAndCostParameter();
-  RankList dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 10; i++) {
     dev_list.push_back(i);
   }
 
-  RankList stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(8);
   stage_map.push_back(2);
 
@@ -1327,8 +1327,8 @@ TEST_F(TestDPAlgo, test_GetStrategy_for_DoubleStarGraph) {
 
   for (auto &op : cost_graph->GetOperators()) {
     StrategyPtr s_strategy = op->selected_strategy();
-    Dimensions strategy_0 = s_strategy->GetInputDim()[0];
-    Dimensions strategy_1 = s_strategy->GetInputDim()[1];
+    std::vector<int32_t> strategy_0 = s_strategy->GetInputDim()[0];
+    std::vector<int32_t> strategy_1 = s_strategy->GetInputDim()[1];
 
     std::string string_strategy_0 = "[";
     for (size_t i = 0; i < strategy_0.size(); ++i) {

tests/ut/cpp/parallel/auto_parallel/edge_costmodel_test.cc

@@ -43,13 +43,13 @@ class TestEdgeCostModel : public UT::Common {
 };
 
 void TestEdgeCostModel::SetUp() {
-  RankList dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 10; i++) {
     dev_list.push_back(i);
   }
 
-  RankList stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(8);
   stage_map.push_back(2);
 

tests/ut/cpp/parallel/auto_parallel/graph_costmodel_test.cc

@@ -53,13 +53,13 @@ class TestCostGraph : public UT::Common {
 
 void TestCostGraph::SetUp() {
   cost_graph.SetDeviceMemoryAndCostParameter();
-  RankList dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 10; i++) {
     dev_list.push_back(i);
   }
 
-  RankList stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(8);
   stage_map.push_back(2);
 

tests/ut/cpp/parallel/auto_parallel/operator_costmodel_test.cc

@@ -33,13 +33,13 @@ class TestMatMulCost : public UT::Common {
 
 void TestMatMulCost::SetUp() {
   mmcost_ = MatMulCost();
-  RankList dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 1050; i++) {
     dev_list.push_back(i);
   }
 
-  RankList stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(1024);
   stage_map.push_back(26);
 
@@ -90,13 +90,13 @@ class TestActivationCost : public UT::Common {
 
 void TestActivationCost::SetUp() {
   ac_cost_ = ActivationCost();
-  RankList dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 1050; i++) {
     dev_list.push_back(i);
   }
 
-  RankList stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(1024);
   stage_map.push_back(26);
 
@@ -142,13 +142,13 @@ class TestPReLUCost : public UT::Common {
 
 void TestPReLUCost::SetUp() {
   prelu_cost_ = PReLUCost();
-  RankList dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 1050; i++) {
     dev_list.push_back(i);
   }
 
-  RankList stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(1024);
   stage_map.push_back(26);
 

tests/ut/cpp/parallel/device_manager_test.cc

@@ -69,8 +69,8 @@ void TestDeviceManager::TearDown() {
 }
 
 TEST_F(TestDeviceManager, test_dm_init_AND_get_device_list) {
-  RankList dev_list;
-  RankList stage_map;
+  std::vector<int32_t> dev_list;
+  std::vector<int32_t> stage_map;
   int32_t local_dev = 0;
 
   dev_list.push_back(5);
@@ -85,12 +85,12 @@ TEST_F(TestDeviceManager, test_dm_init_AND_get_device_list) {
   ASSERT_EQ(dm_.DeviceNum(), 4);
   ASSERT_EQ(dm_.GetStageNum(), (int32_t)(2));
 
-  RankList dev_list_0 = dm_.GetDeviceListByStageId(0);
-  RankList dev_list_1 = dm_.GetDeviceListByStageId(1);
+  std::vector<int32_t> dev_list_0 = dm_.GetDeviceListByStageId(0);
+  std::vector<int32_t> dev_list_1 = dm_.GetDeviceListByStageId(1);
   ASSERT_EQ(dev_list_0.size(), 2);
   ASSERT_EQ(dev_list_1.size(), 2);
 
-  RankList::iterator it = dev_list_0.begin();
+  std::vector<int32_t>::iterator it = dev_list_0.begin();
   ASSERT_EQ((*it), int32_t(5));
   it++;
   ASSERT_EQ((*it), int32_t(3));
@@ -112,7 +112,7 @@ TEST_F(TestDeviceManager, test_CreateNewDeviceByRank) {
 
 TEST_F(TestDeviceManager, test_CreateDeviceListByRankList) {
   std::vector<Device> dev_list;
-  RankList rlist;
+  std::vector<int32_t> rlist;
   rlist.push_back(int32_t(2));
   rlist.push_back(int32_t(1));
   dev_list = dm_.CreateDeviceListByRankList(rlist);

tests/ut/cpp/parallel/ops_info/activation_info_test.cc

@@ -38,13 +38,13 @@ class TestActivationInfo : public UT::Common {
 };
 
 void TestActivationInfo::SetUp() {
-  RankList dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 1050; i++) {
     dev_list.push_back(i);
   }
 
-  RankList stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(1024);
   stage_map.push_back(26);
 
@@ -64,18 +64,18 @@ void TestActivationInfo::SetUp() {
 }
 
 TEST_F(TestActivationInfo, InferDevMatrixShape1) {
-  Strategys inputs = {{2, 4, 8, 16}};
+  std::vector<Dimensions> inputs = {{2, 4, 8, 16}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   activation->Init(strategy);
-  Shape dev_matrix_shape = activation->dev_matrix_shape();
+  std::vector<int32_t> dev_matrix_shape = activation->dev_matrix_shape();
 
-  Shape expect = {2, 4, 8, 16};
+  std::vector<int32_t> expect = {2, 4, 8, 16};
   ASSERT_EQ(dev_matrix_shape, expect);
 }
 
 TEST_F(TestActivationInfo, InferSliceShape1) {
-  Strategys str = {{2, 4, 8, 16}};
+  std::vector<Dimensions> str = {{2, 4, 8, 16}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   activation->Init(strategy);
@@ -96,7 +96,7 @@ TEST_F(TestActivationInfo, InferSliceShape1) {
 }
 
 TEST_F(TestActivationInfo, GetTensorLayout1) {
-  Strategys str = {{2, 4, 8, 16}};
+  std::vector<Dimensions> str = {{2, 4, 8, 16}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   activation->Init(strategy);
@@ -117,7 +117,7 @@ TEST_F(TestActivationInfo, GetTensorLayout1) {
 }
 
 TEST_F(TestActivationInfo, GetForwardOp1) {
-  Strategys inputs = {{2, 4, 8, 16}};
+  std::vector<Dimensions> inputs = {{2, 4, 8, 16}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   activation->Init(strategy);
@@ -128,7 +128,7 @@ TEST_F(TestActivationInfo, GetForwardOp1) {
 }
 
 TEST_F(TestActivationInfo, GetMirrorOPs1) {
-  Strategys inputs = {{1, 4, 8, 16}};
+  std::vector<Dimensions> inputs = {{1, 4, 8, 16}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   activation->Init(strategy);
@@ -148,7 +148,7 @@ TEST_F(TestActivationInfo, GetMirrorOPs1) {
 }
 
 TEST_F(TestActivationInfo, GetMirrorOPs2) {
-  Strategys inputs = {{2, 4, 8, 16}};
+  std::vector<Dimensions> inputs = {{2, 4, 8, 16}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   activation->Init(strategy);
@@ -161,7 +161,7 @@ TEST_F(TestActivationInfo, GetMirrorOPs2) {
 
 TEST_F(TestActivationInfo, CheckStrategy1) {
   // Success: {{2,4,8,16}}
-  Strategys inputs = {{2, 2, 8, 16}, {2, 4, 16, 1}};
+  std::vector<Dimensions> inputs = {{2, 2, 8, 16}, {2, 4, 16, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = activation->Init(strategy);
@@ -170,7 +170,7 @@ TEST_F(TestActivationInfo, CheckStrategy1) {
 
 TEST_F(TestActivationInfo, CheckStrategy2) {
   // Success: {{2,4,8,16}}
-  Strategys inputs = {{2, 4, 8}};
+  std::vector<Dimensions> inputs = {{2, 4, 8}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = activation->Init(strategy);

tests/ut/cpp/parallel/ops_info/activation_test.cc

@@ -40,13 +40,13 @@ class TestActivation : public UT::Common {
 };
 
 void TestActivation::SetUp() {
-  RankList dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 1050; i++) {
     dev_list.push_back(i);
   }
 
-  RankList stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(1024);
   stage_map.push_back(26);
 
@@ -101,7 +101,7 @@ TEST_F(TestActivation, test_softmax_strategies) {
     ASSERT_NE(sp, nullptr);
     Cost cost = *(swc->cost_list[0]);
 
-    Strategys stra = sp->GetInputDim();
+    std::vector<Dimensions> stra = sp->GetInputDim();
     ASSERT_GT(stra.size(), 0);
     Dimensions input0_stra = stra[0];
     ASSERT_GT(input0_stra.size(), 2);

tests/ut/cpp/parallel/ops_info/gelu_info_test.cc

@@ -38,13 +38,13 @@ class TestGeluInfo : public UT::Common {
 };
 
 void TestGeluInfo::SetUp() {
-  RankList dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 130; i++) {
     dev_list.push_back(i);
   }
 
-  RankList stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(128);
   stage_map.push_back(2);
 
@@ -63,18 +63,18 @@ void TestGeluInfo::SetUp() {
 }
 
 TEST_F(TestGeluInfo, InferDevMatrixShape1) {
-  Strategys inputs = {{2, 4, 1, 16}};
+  std::vector<Dimensions> inputs = {{2, 4, 1, 16}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   gelu->Init(strategy);
-  Shape dev_matrix_shape = gelu->dev_matrix_shape();
+  std::vector<int32_t> dev_matrix_shape = gelu->dev_matrix_shape();
 
-  Shape expect = {2, 4, 1, 16};
+  std::vector<int32_t> expect = {2, 4, 1, 16};
   ASSERT_EQ(dev_matrix_shape, expect);
 }
 
 TEST_F(TestGeluInfo, InferSliceShape1) {
-  Strategys str = {{2, 4, 1, 16}};
+  std::vector<Dimensions> str = {{2, 4, 1, 16}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   gelu->Init(strategy);
@@ -95,7 +95,7 @@ TEST_F(TestGeluInfo, InferSliceShape1) {
 }
 
 TEST_F(TestGeluInfo, GetTensorLayout1) {
-  Strategys str = {{2, 4, 1, 16}};
+  std::vector<Dimensions> str = {{2, 4, 1, 16}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   gelu->Init(strategy);
@@ -116,7 +116,7 @@ TEST_F(TestGeluInfo, GetTensorLayout1) {
 }
 
 TEST_F(TestGeluInfo, GetForwardOp1) {
-  Strategys inputs = {{2, 4, 1, 16}};
+  std::vector<Dimensions> inputs = {{2, 4, 1, 16}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   gelu->Init(strategy);
@@ -127,7 +127,7 @@ TEST_F(TestGeluInfo, GetForwardOp1) {
 }
 
 TEST_F(TestGeluInfo, GetMirrorOPs1) {
-  Strategys inputs = {{2, 4, 1, 16}};
+  std::vector<Dimensions> inputs = {{2, 4, 1, 16}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   gelu->Init(strategy);
@@ -140,7 +140,7 @@ TEST_F(TestGeluInfo, GetMirrorOPs1) {
 
 TEST_F(TestGeluInfo, CheckStrategy1) {
   // Success: {{2,4,1,16}}
-  Strategys inputs = {{2, 2, 8, 16}, {2, 4, 16, 1}};
+  std::vector<Dimensions> inputs = {{2, 2, 8, 16}, {2, 4, 16, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = gelu->Init(strategy);
@@ -149,7 +149,7 @@ TEST_F(TestGeluInfo, CheckStrategy1) {
 
 TEST_F(TestGeluInfo, CheckStrategy2) {
   // Success: {{2,4,1,16}}
-  Strategys inputs = {{2, 4, 8}};
+  std::vector<Dimensions> inputs = {{2, 4, 8}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = gelu->Init(strategy);
@@ -158,7 +158,7 @@ TEST_F(TestGeluInfo, CheckStrategy2) {
 
 TEST_F(TestGeluInfo, CheckStrategy3) {
   // Success: {{2,4,1,16}}
-  Strategys inputs = {{2, 4, 1, 16}};
+  std::vector<Dimensions> inputs = {{2, 4, 1, 16}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = gelu->Init(strategy);

tests/ut/cpp/parallel/ops_info/generate_strategy_test.cc

@@ -34,13 +34,13 @@ class TestGenerateStrategy : public UT::Common {
 };
 
 void TestGenerateStrategy::SetUp() {
-  RankList dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 10; i++) {
     dev_list.push_back(i);
   }
 
-  RankList stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(8);
   stage_map.push_back(2);
 

tests/ut/cpp/parallel/ops_info/get_next_info_test.cc

@@ -38,13 +38,13 @@ class TestGetNextInfo : public UT::Common {
 };
 
 void TestGetNextInfo::SetUp() {
-  RankList dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 8; i++) {
     dev_list.push_back(i);
   }
 
-  RankList stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(8);
   int32_t local_dev = 0;
   // create a new g_device_manager
@@ -65,16 +65,16 @@ void TestGetNextInfo::SetUp() {
 }
 
 TEST_F(TestGetNextInfo, InferDevMatrixShape1) {
-  Strategys inputs = {{}, {}};
+  std::vector<Dimensions> inputs = {{}, {}};
   StrategyPtr strategy = NewStrategy(0, inputs);
   get_next->Init(strategy);
-  Shape dev_matrix_shape = get_next->dev_matrix_shape();
-  Shape expect = {8, 1};
+  std::vector<int32_t> dev_matrix_shape = get_next->dev_matrix_shape();
+  std::vector<int32_t> expect = {8, 1};
   ASSERT_EQ(dev_matrix_shape, expect);
 }
 
 TEST_F(TestGetNextInfo, InferSliceShape1) {
-  Strategys str = {{}, {}};
+  std::vector<Dimensions> str = {{}, {}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   get_next->Init(strategy);
@@ -90,7 +90,7 @@ TEST_F(TestGetNextInfo, InferSliceShape1) {
 }
 
 TEST_F(TestGetNextInfo, GetTensorLayout1) {
-  Strategys str = {{}, {}};
+  std::vector<Dimensions> str = {{}, {}};
   StrategyPtr strategy = NewStrategy(0, str);
   get_next->Init(strategy);
   std::vector<TensorInfo> outputs = get_next->outputs_tensor_info();
@@ -106,14 +106,14 @@ TEST_F(TestGetNextInfo, GetTensorLayout1) {
 }
 
 TEST_F(TestGetNextInfo, CheckStrategy1) {
-  Strategys inputs = {};
+  std::vector<Dimensions> inputs = {};
   StrategyPtr strategy = NewStrategy(0, inputs);
   Status ret = get_next->Init(strategy);
   ASSERT_EQ(ret, SUCCESS);
 }
 
 TEST_F(TestGetNextInfo, CheckStrategy2) {
-  Strategys inputs = {{8, 1}, {8}};
+  std::vector<Dimensions> inputs = {{8, 1}, {8}};
   StrategyPtr strategy = NewStrategy(0, inputs);
   Status ret = get_next->Init(strategy);
   ASSERT_EQ(ret, FAILED);

tests/ut/cpp/parallel/ops_info/l2_normalize_info_test.cc

@@ -38,13 +38,13 @@ class TestL2NormalizeInfo : public UT::Common {
 };
 
 void TestL2NormalizeInfo::SetUp() {
-  RankList dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 34; i++) {
     dev_list.push_back(i);
   }
 
-  RankList stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(32);
   stage_map.push_back(2);
 
@@ -64,18 +64,18 @@ void TestL2NormalizeInfo::SetUp() {
 }
 
 TEST_F(TestL2NormalizeInfo, InferDevMatrixShape1) {
-  Strategys inputs = {{4, 1, 8}};
+  std::vector<Dimensions> inputs = {{4, 1, 8}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   norm->Init(strategy);
-  Shape dev_matrix_shape = norm->dev_matrix_shape();
+  std::vector<int32_t> dev_matrix_shape = norm->dev_matrix_shape();
 
-  Shape expect = {4, 1, 8};
+  std::vector<int32_t> expect = {4, 1, 8};
   ASSERT_EQ(dev_matrix_shape, expect);
 }
 
 TEST_F(TestL2NormalizeInfo, InferSliceShape1) {
-  Strategys str = {{4, 1, 8}};
+  std::vector<Dimensions> str = {{4, 1, 8}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   norm->Init(strategy);
@@ -96,7 +96,7 @@ TEST_F(TestL2NormalizeInfo, InferSliceShape1) {
 }
 
 TEST_F(TestL2NormalizeInfo, GetTensorLayout1) {
-  Strategys str = {{4, 1, 8}};
+  std::vector<Dimensions> str = {{4, 1, 8}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   norm->Init(strategy);
@@ -117,7 +117,7 @@ TEST_F(TestL2NormalizeInfo, GetTensorLayout1) {
 }
 
 TEST_F(TestL2NormalizeInfo, GetForwardOp1) {
-  Strategys inputs = {{4, 1, 8}};
+  std::vector<Dimensions> inputs = {{4, 1, 8}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   norm->Init(strategy);
@@ -128,7 +128,7 @@ TEST_F(TestL2NormalizeInfo, GetForwardOp1) {
 }
 
 TEST_F(TestL2NormalizeInfo, GetMirrorOPs1) {
-  Strategys inputs = {{4, 1, 8}};
+  std::vector<Dimensions> inputs = {{4, 1, 8}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   norm->Init(strategy);
@@ -140,7 +140,7 @@ TEST_F(TestL2NormalizeInfo, GetMirrorOPs1) {
 }
 
 TEST_F(TestL2NormalizeInfo, CheckStrategy1) {
-  Strategys inputs = {{4, 1, 8}, {4, 1, 8}};
+  std::vector<Dimensions> inputs = {{4, 1, 8}, {4, 1, 8}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = norm->Init(strategy);
@@ -148,7 +148,7 @@ TEST_F(TestL2NormalizeInfo, CheckStrategy1) {
 }
 
 TEST_F(TestL2NormalizeInfo, CheckStrategy2) {
-  Strategys inputs = {{4, 2, 3}};
+  std::vector<Dimensions> inputs = {{4, 2, 3}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = norm->Init(strategy);
@@ -156,7 +156,7 @@ TEST_F(TestL2NormalizeInfo, CheckStrategy2) {
 }
 
 TEST_F(TestL2NormalizeInfo, CheckStrategy3) {
-  Strategys inputs = {{4, 2, 3, 4}};
+  std::vector<Dimensions> inputs = {{4, 2, 3, 4}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = norm->Init(strategy);
@@ -164,7 +164,7 @@ TEST_F(TestL2NormalizeInfo, CheckStrategy3) {
 }
 
 TEST_F(TestL2NormalizeInfo, CheckStrategy4) {
-  Strategys inputs = {{4, 1, 8}};
+  std::vector<Dimensions> inputs = {{4, 1, 8}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = norm->Init(strategy);
@@ -172,7 +172,7 @@ TEST_F(TestL2NormalizeInfo, CheckStrategy4) {
 }
 
 TEST_F(TestL2NormalizeInfo, mirror_ops) {
-  Strategys inputs = {{2, 1, 8}};
+  std::vector<Dimensions> inputs = {{2, 1, 8}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   norm->Init(strategy);

tests/ut/cpp/parallel/ops_info/log_softmax_info_test.cc

@@ -38,13 +38,13 @@ class TestLogSoftmaxInfo : public UT::Common {
 };
 
 void TestLogSoftmaxInfo::SetUp() {
-  RankList dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 130; i++) {
     dev_list.push_back(i);
   }
 
-  RankList stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(128);
   stage_map.push_back(2);
 
@@ -64,18 +64,18 @@ void TestLogSoftmaxInfo::SetUp() {
 }
 
 TEST_F(TestLogSoftmaxInfo, InferDevMatrixShape1) {
-  Strategys inputs = {{2, 4, 1, 16}};
+  std::vector<Dimensions> inputs = {{2, 4, 1, 16}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   log_softmax->Init(strategy);
-  Shape dev_matrix_shape = log_softmax->dev_matrix_shape();
+  std::vector<int32_t> dev_matrix_shape = log_softmax->dev_matrix_shape();
 
-  Shape expect = {2, 4, 1, 16};
+  std::vector<int32_t> expect = {2, 4, 1, 16};
   ASSERT_EQ(dev_matrix_shape, expect);
 }
 
 TEST_F(TestLogSoftmaxInfo, InferSliceShape1) {
-  Strategys str = {{2, 4, 1, 16}};
+  std::vector<Dimensions> str = {{2, 4, 1, 16}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   log_softmax->Init(strategy);
@@ -96,7 +96,7 @@ TEST_F(TestLogSoftmaxInfo, InferSliceShape1) {
 }
 
 TEST_F(TestLogSoftmaxInfo, GetTensorLayout1) {
-  Strategys str = {{2, 4, 1, 16}};
+  std::vector<Dimensions> str = {{2, 4, 1, 16}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   log_softmax->Init(strategy);
@@ -117,7 +117,7 @@ TEST_F(TestLogSoftmaxInfo, GetTensorLayout1) {
 }
 
 TEST_F(TestLogSoftmaxInfo, GetForwardOp1) {
-  Strategys inputs = {{2, 4, 1, 16}};
+  std::vector<Dimensions> inputs = {{2, 4, 1, 16}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   log_softmax->Init(strategy);
@@ -128,7 +128,7 @@ TEST_F(TestLogSoftmaxInfo, GetForwardOp1) {
 }
 
 TEST_F(TestLogSoftmaxInfo, GetMirrorOPs1) {
-  Strategys inputs = {{2, 4, 1, 16}};
+  std::vector<Dimensions> inputs = {{2, 4, 1, 16}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   log_softmax->Init(strategy);
@@ -141,7 +141,7 @@ TEST_F(TestLogSoftmaxInfo, GetMirrorOPs1) {
 
 TEST_F(TestLogSoftmaxInfo, CheckStrategy1) {
   // Success: {{2,4,1,16}}
-  Strategys inputs = {{2, 2, 8, 16}, {2, 4, 16, 1}};
+  std::vector<Dimensions> inputs = {{2, 2, 8, 16}, {2, 4, 16, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = log_softmax->Init(strategy);
@@ -150,7 +150,7 @@ TEST_F(TestLogSoftmaxInfo, CheckStrategy1) {
 
 TEST_F(TestLogSoftmaxInfo, CheckStrategy2) {
   // Success: {{2,4,1,16}}
-  Strategys inputs = {{2, 4, 8}};
+  std::vector<Dimensions> inputs = {{2, 4, 8}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = log_softmax->Init(strategy);
@@ -159,7 +159,7 @@ TEST_F(TestLogSoftmaxInfo, CheckStrategy2) {
 
 TEST_F(TestLogSoftmaxInfo, CheckStrategy3) {
   // Success: {{2,4,1,16}}
-  Strategys inputs = {{2, 4, 8, 16}};
+  std::vector<Dimensions> inputs = {{2, 4, 8, 16}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = log_softmax->Init(strategy);
@@ -167,7 +167,7 @@ TEST_F(TestLogSoftmaxInfo, CheckStrategy3) {
 }
 
 TEST_F(TestLogSoftmaxInfo, GetDeviceList1) {
-  Strategys inputs = {{2, 4, 1, 16}};
+  std::vector<Dimensions> inputs = {{2, 4, 1, 16}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   log_softmax->Init(strategy);

tests/ut/cpp/parallel/ops_info/matmul_info_test.cc

@@ -42,13 +42,13 @@ class TestMatmulInfo : public UT::Common {
 };
 
 void TestMatmulInfo::SetUp() {
-  RankList dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 1050; i++) {
     dev_list.push_back(i);
   }
 
-  RankList stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(1024);
   stage_map.push_back(26);
 
@@ -94,77 +94,77 @@ void TestMatmulInfo::SetUp() {
 }
 
 TEST_F(TestMatmulInfo, InferDevMatrixShape1) {
-  Strategys inputs = {{2, 4, 8, 16}, {2, 4, 16, 1}};
+  std::vector<Dimensions> inputs = {{2, 4, 8, 16}, {2, 4, 16, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   matmul1->Init(strategy);
-  Shape dev_matrix_shape = matmul1->dev_matrix_shape();
+  std::vector<int32_t> dev_matrix_shape = matmul1->dev_matrix_shape();
 
-  Shape expect = {2, 4, 8, 16, 1};
+  std::vector<int32_t> expect = {2, 4, 8, 16, 1};
   ASSERT_EQ(dev_matrix_shape, expect);
 }
 
 TEST_F(TestMatmulInfo, InferDevMatrixShape2) {
-  Strategys inputs = {{2, 4, 8, 8}, {2, 4, 8, 2}};
+  std::vector<Dimensions> inputs = {{2, 4, 8, 8}, {2, 4, 8, 2}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   matmul1->Init(strategy);
-  Shape dev_matrix_shape = matmul1->dev_matrix_shape();
+  std::vector<int32_t> dev_matrix_shape = matmul1->dev_matrix_shape();
 
-  Shape expect = {2, 4, 8, 8, 2};
+  std::vector<int32_t> expect = {2, 4, 8, 8, 2};
   ASSERT_EQ(dev_matrix_shape, expect);
 }
 
 // matmul2
 TEST_F(TestMatmulInfo, InferDevMatrixShape3) {
-  Strategys inputs = {{2, 4, 8, 16}, {1, 16}};
+  std::vector<Dimensions> inputs = {{2, 4, 8, 16}, {1, 16}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   matmul2->Init(strategy);
-  Shape dev_matrix_shape = matmul2->dev_matrix_shape();
+  std::vector<int32_t> dev_matrix_shape = matmul2->dev_matrix_shape();
 
-  Shape expect = {2, 4, 8, 16, 1};
+  std::vector<int32_t> expect = {2, 4, 8, 16, 1};
   ASSERT_EQ(dev_matrix_shape, expect);
 }
 
 // matmul2
 TEST_F(TestMatmulInfo, InferDevMatrixShape4) {
-  Strategys inputs = {{2, 4, 8, 8}, {2, 8}};
+  std::vector<Dimensions> inputs = {{2, 4, 8, 8}, {2, 8}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   matmul2->Init(strategy);
-  Shape dev_matrix_shape = matmul2->dev_matrix_shape();
+  std::vector<int32_t> dev_matrix_shape = matmul2->dev_matrix_shape();
 
-  Shape expect = {2, 4, 8, 8, 2};
+  std::vector<int32_t> expect = {2, 4, 8, 8, 2};
   ASSERT_EQ(dev_matrix_shape, expect);
 }
 
 // matmul3
 TEST_F(TestMatmulInfo, InferDevMatrixShape5) {
-  Strategys inputs = {{8, 16}, {2, 4, 1, 16}};
+  std::vector<Dimensions> inputs = {{8, 16}, {2, 4, 1, 16}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   matmul3->Init(strategy);
-  Shape dev_matrix_shape = matmul3->dev_matrix_shape();
+  std::vector<int32_t> dev_matrix_shape = matmul3->dev_matrix_shape();
 
-  Shape expect = {2, 4, 8, 16, 1};
+  std::vector<int32_t> expect = {2, 4, 8, 16, 1};
   ASSERT_EQ(dev_matrix_shape, expect);
 }
 
 // matmul3
 TEST_F(TestMatmulInfo, InferDevMatrixShape6) {
-  Strategys inputs = {{8, 8}, {2, 4, 2, 8}};
+  std::vector<Dimensions> inputs = {{8, 8}, {2, 4, 2, 8}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   matmul3->Init(strategy);
-  Shape dev_matrix_shape = matmul3->dev_matrix_shape();
+  std::vector<int32_t> dev_matrix_shape = matmul3->dev_matrix_shape();
 
-  Shape expect = {2, 4, 8, 8, 2};
+  std::vector<int32_t> expect = {2, 4, 8, 8, 2};
   ASSERT_EQ(dev_matrix_shape, expect);
 }
 
 TEST_F(TestMatmulInfo, InferTensorMap1) {
-  Strategys str = {{2, 4, 8, 16}, {2, 4, 16, 1}};
+  std::vector<Dimensions> str = {{2, 4, 8, 16}, {2, 4, 16, 1}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   matmul1->Init(strategy);
@@ -190,7 +190,7 @@ TEST_F(TestMatmulInfo, InferTensorMap1) {
 
 // matmul2
 TEST_F(TestMatmulInfo, InferTensorMap2) {
-  Strategys str = {{2, 4, 8, 16}, {1, 16}};
+  std::vector<Dimensions> str = {{2, 4, 8, 16}, {1, 16}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   matmul2->Init(strategy);
@@ -216,7 +216,7 @@ TEST_F(TestMatmulInfo, InferTensorMap2) {
 
 // matmul3
 TEST_F(TestMatmulInfo, InferTensorMap3) {
-  Strategys str = {{8, 16}, {2, 4, 1, 16}};
+  std::vector<Dimensions> str = {{8, 16}, {2, 4, 1, 16}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   matmul3->Init(strategy);
@@ -241,7 +241,7 @@ TEST_F(TestMatmulInfo, InferTensorMap3) {
 }
 
 TEST_F(TestMatmulInfo, InferSliceShape1) {
-  Strategys str = {{2, 4, 8, 16}, {2, 4, 16, 1}};
+  std::vector<Dimensions> str = {{2, 4, 8, 16}, {2, 4, 16, 1}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   matmul1->Init(strategy);
@@ -267,7 +267,7 @@ TEST_F(TestMatmulInfo, InferSliceShape1) {
 
 // matmul2
 TEST_F(TestMatmulInfo, InferSliceShape2) {
-  Strategys str = {{2, 4, 8, 16}, {1, 16}};
+  std::vector<Dimensions> str = {{2, 4, 8, 16}, {1, 16}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   matmul2->Init(strategy);
@@ -293,7 +293,7 @@ TEST_F(TestMatmulInfo, InferSliceShape2) {
 
 // matmul3
 TEST_F(TestMatmulInfo, InferSliceShape3) {
-  Strategys str = {{8, 16}, {2, 4, 1, 16}};
+  std::vector<Dimensions> str = {{8, 16}, {2, 4, 1, 16}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   matmul3->Init(strategy);
@@ -319,7 +319,7 @@ TEST_F(TestMatmulInfo, InferSliceShape3) {
 
 // matmul3
 TEST_F(TestMatmulInfo, GetTensorLayout3) {
-  Strategys str = {{8, 16}, {2, 4, 1, 16}};
+  std::vector<Dimensions> str = {{8, 16}, {2, 4, 1, 16}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   matmul3->Init(strategy);
@@ -344,7 +344,7 @@ TEST_F(TestMatmulInfo, GetTensorLayout3) {
 }
 
 TEST_F(TestMatmulInfo, GetForwardOp1) {
-  Strategys inputs = {{2, 4, 8, 16}, {2, 4, 16, 1}};
+  std::vector<Dimensions> inputs = {{2, 4, 8, 16}, {2, 4, 16, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   matmul1->Init(strategy);
@@ -370,7 +370,7 @@ TEST_F(TestMatmulInfo, GetForwardOp1) {
 }
 
 TEST_F(TestMatmulInfo, GetForwardOp2) {
-  Strategys inputs = {{2, 4, 8, 1}, {2, 4, 1, 16}};
+  std::vector<Dimensions> inputs = {{2, 4, 8, 1}, {2, 4, 1, 16}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   matmul1->Init(strategy);
@@ -380,7 +380,7 @@ TEST_F(TestMatmulInfo, GetForwardOp2) {
 }
 
 TEST_F(TestMatmulInfo, GetVirtualDivOp1) {
-  Strategys inputs = {{2, 4, 8, 16}, {2, 4, 16, 1}};
+  std::vector<Dimensions> inputs = {{2, 4, 8, 16}, {2, 4, 16, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   matmul1->Init(strategy);
@@ -399,7 +399,7 @@ TEST_F(TestMatmulInfo, GetVirtualDivOp1) {
 }
 
 TEST_F(TestMatmulInfo, GetMirrorOPs1) {
-  Strategys inputs = {{2, 4, 8, 16}, {2, 4, 16, 1}};
+  std::vector<Dimensions> inputs = {{2, 4, 8, 16}, {2, 4, 16, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   matmul1->Init(strategy);
@@ -419,7 +419,7 @@ TEST_F(TestMatmulInfo, GetMirrorOPs1) {
 
 // matmul2
 TEST_F(TestMatmulInfo, GetMirrorOPs2) {
-  Strategys inputs = {{2, 4, 1, 16}, {8, 16}};
+  std::vector<Dimensions> inputs = {{2, 4, 1, 16}, {8, 16}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   matmul2->Init(strategy);
@@ -439,7 +439,7 @@ TEST_F(TestMatmulInfo, GetMirrorOPs2) {
 
 // matmul3
 TEST_F(TestMatmulInfo, GetMirrorOPs3) {
-  Strategys inputs = {{8, 16}, {2, 4, 1, 16}};
+  std::vector<Dimensions> inputs = {{8, 16}, {2, 4, 1, 16}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   matmul3->Init(strategy);
@@ -457,7 +457,7 @@ TEST_F(TestMatmulInfo, GetMirrorOPs3) {
 }
 
 TEST_F(TestMatmulInfo, GetMirrorOPs4) {
-  Strategys inputs = {{2, 4, 1, 16}, {2, 4, 16, 8}};
+  std::vector<Dimensions> inputs = {{2, 4, 1, 16}, {2, 4, 16, 8}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   matmul1->Init(strategy);
@@ -467,7 +467,7 @@ TEST_F(TestMatmulInfo, GetMirrorOPs4) {
 }
 
 TEST_F(TestMatmulInfo, InitTwice) {
-  Strategys inputs = {{2, 4, 8, 16}, {2, 4, 16, 1}};
+  std::vector<Dimensions> inputs = {{2, 4, 8, 16}, {2, 4, 16, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   // init twice
@@ -489,7 +489,7 @@ TEST_F(TestMatmulInfo, InitTwice) {
 
 TEST_F(TestMatmulInfo, CheckStrategy1) {
   // Success: {{2,4,8,16}, {2,4,16,1}}
-  Strategys inputs = {{2, 2, 8, 16}, {2, 4, 16, 1}};
+  std::vector<Dimensions> inputs = {{2, 2, 8, 16}, {2, 4, 16, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = matmul1->Init(strategy);
@@ -498,7 +498,7 @@ TEST_F(TestMatmulInfo, CheckStrategy1) {
 
 TEST_F(TestMatmulInfo, CheckStrategy2) {
   // Success: {{2,4,8,16}, {2,4,16,1}}
-  Strategys inputs = {{2, 4, 8, 16}, {4, 16, 1}};
+  std::vector<Dimensions> inputs = {{2, 4, 8, 16}, {4, 16, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = matmul1->Init(strategy);
@@ -507,7 +507,7 @@ TEST_F(TestMatmulInfo, CheckStrategy2) {
 
 TEST_F(TestMatmulInfo, CheckStrategy3) {
   // Success: {{2,4,8,16}, {2,4,16,1}}
-  Strategys inputs = {{2, 4, 8, 16}, {2, 4, 8, 1}};
+  std::vector<Dimensions> inputs = {{2, 4, 8, 16}, {2, 4, 8, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = matmul1->Init(strategy);
@@ -516,7 +516,7 @@ TEST_F(TestMatmulInfo, CheckStrategy3) {
 
 TEST_F(TestMatmulInfo, CheckStrategy4) {
   // Success: {{2,4,8,16}, {2,4,16,1}}
-  Strategys inputs = {{2, 4, 8, 16}, {2, 3, 16, 1}};
+  std::vector<Dimensions> inputs = {{2, 4, 8, 16}, {2, 3, 16, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = matmul1->Init(strategy);
@@ -525,7 +525,7 @@ TEST_F(TestMatmulInfo, CheckStrategy4) {
 
 TEST_F(TestMatmulInfo, CheckStrategy5) {
   // Success: {{2,4,8,16}, {2,4,16,1}}
-  Strategys inputs = {{0, 4, 8, 16}, {2, 4, 16, 1}};
+  std::vector<Dimensions> inputs = {{0, 4, 8, 16}, {2, 4, 16, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = matmul1->Init(strategy);
@@ -534,7 +534,7 @@ TEST_F(TestMatmulInfo, CheckStrategy5) {
 
 TEST_F(TestMatmulInfo, CheckStrategy6) {
   // Success: {{2,4,8,16}, {2,4,16,1}}
-  Strategys inputs = {{-1, 4, 8, 16}, {2, 4, 16, 1}};
+  std::vector<Dimensions> inputs = {{-1, 4, 8, 16}, {2, 4, 16, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = matmul1->Init(strategy);
@@ -543,7 +543,7 @@ TEST_F(TestMatmulInfo, CheckStrategy6) {
 
 TEST_F(TestMatmulInfo, CheckStrategy7) {
   // Success: {{2,4,8,16}, {2,4,16,1}}
-  Strategys inputs = {{4, 4, 8, 16}, {2, 4, 16, 1}};
+  std::vector<Dimensions> inputs = {{4, 4, 8, 16}, {2, 4, 16, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = matmul1->Init(strategy);
@@ -552,7 +552,7 @@ TEST_F(TestMatmulInfo, CheckStrategy7) {
 
 TEST_F(TestMatmulInfo, InitFailed) {
   // matmul4 attr is wrong
-  Strategys inputs = {{4, 4, 8, 16}, {2, 4, 16, 1}};
+  std::vector<Dimensions> inputs = {{4, 4, 8, 16}, {2, 4, 16, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = matmul4->Init(strategy);

tests/ut/cpp/parallel/ops_info/onehot_info_test.cc

@@ -38,13 +38,13 @@ class TestOneHotInfo : public UT::Common {
 };
 
 void TestOneHotInfo::SetUp() {
-  RankList dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 10; i++) {
     dev_list.push_back(i);
   }
 
-  RankList stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(8);
   stage_map.push_back(2);
 
@@ -64,43 +64,43 @@ void TestOneHotInfo::SetUp() {
 }
 
 TEST_F(TestOneHotInfo, InferDevMatrixShape1) {
-  Strategys inputs = {{8, 1}, {}, {}};
+  std::vector<Dimensions> inputs = {{8, 1}, {}, {}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status status = onehot_info->Init(strategy);
   ASSERT_EQ(status, SUCCESS);
-  Shape dev_matrix_shape = onehot_info->dev_matrix_shape();
+  std::vector<int32_t> dev_matrix_shape = onehot_info->dev_matrix_shape();
 
-  Shape expect = {8, 1};
+  std::vector<int32_t> expect = {8, 1};
   ASSERT_EQ(dev_matrix_shape, expect);
 }
 
 TEST_F(TestOneHotInfo, InferDevMatrixShape2) {
-  Strategys inputs = {{4, 1}, {}, {}};
+  std::vector<Dimensions> inputs = {{4, 1}, {}, {}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status status = onehot_info->Init(strategy);
   ASSERT_EQ(status, SUCCESS);
-  Shape dev_matrix_shape = onehot_info->dev_matrix_shape();
+  std::vector<int32_t> dev_matrix_shape = onehot_info->dev_matrix_shape();
 
-  Shape expect = {2, 4, 1};
+  std::vector<int32_t> expect = {2, 4, 1};
   ASSERT_EQ(dev_matrix_shape, expect);
 }
 
 TEST_F(TestOneHotInfo, InferDevMatrixShape3) {
-  Strategys inputs = {{4, 2}, {}, {}};
+  std::vector<Dimensions> inputs = {{4, 2}, {}, {}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status status = onehot_info->Init(strategy);
   ASSERT_EQ(status, FAILED);
-  Shape dev_matrix_shape = onehot_info->dev_matrix_shape();
+  std::vector<int32_t> dev_matrix_shape = onehot_info->dev_matrix_shape();
 
-  Shape expect = {4, 2};
+  std::vector<int32_t> expect = {4, 2};
   ASSERT_EQ(dev_matrix_shape, expect);
 }
 
 TEST_F(TestOneHotInfo, InferTensorMap2) {
-  Strategys str = {{8, 1}, {}, {}};
+  std::vector<Dimensions> str = {{8, 1}, {}, {}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   Status status = onehot_info->Init(strategy);
@@ -122,7 +122,7 @@ TEST_F(TestOneHotInfo, InferTensorMap2) {
 }
 
 TEST_F(TestOneHotInfo, InferSliceShape1) {
-  Strategys str = {{8, 1}, {}, {}};
+  std::vector<Dimensions> str = {{8, 1}, {}, {}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   Status status = onehot_info->Init(strategy);
@@ -144,7 +144,7 @@ TEST_F(TestOneHotInfo, InferSliceShape1) {
 }
 
 TEST_F(TestOneHotInfo, InferSliceShape2) {
-  Strategys str = {{4, 2}, {}, {}};
+  std::vector<Dimensions> str = {{4, 2}, {}, {}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   Status status = onehot_info->Init(strategy);
@@ -166,7 +166,7 @@ TEST_F(TestOneHotInfo, InferSliceShape2) {
 }
 
 TEST_F(TestOneHotInfo, InferSliceShape3) {
-  Strategys str = {{2, 2}, {}, {}};
+  std::vector<Dimensions> str = {{2, 2}, {}, {}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   Status status = onehot_info->Init(strategy);
@@ -188,7 +188,7 @@ TEST_F(TestOneHotInfo, InferSliceShape3) {
 }
 
 TEST_F(TestOneHotInfo, GetMirrorOPs1) {
-  Strategys inputs = {{8, 1}, {}, {}};
+  std::vector<Dimensions> inputs = {{8, 1}, {}, {}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status status = onehot_info->Init(strategy);
@@ -199,7 +199,7 @@ TEST_F(TestOneHotInfo, GetMirrorOPs1) {
 }
 
 TEST_F(TestOneHotInfo, CheckStrategy1) {
-  Strategys inputs = {{16}, {}, {}};
+  std::vector<Dimensions> inputs = {{16}, {}, {}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = onehot_info->Init(strategy);

tests/ut/cpp/parallel/ops_info/onehot_info_test_axis_0.cc

@@ -38,13 +38,13 @@ class TestOneHotInfo2 : public UT::Common {
 };
 
 void TestOneHotInfo2::SetUp() {
-  RankList dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 10; i++) {
     dev_list.push_back(i);
   }
 
-  RankList stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(8);
   stage_map.push_back(2);
 
@@ -64,43 +64,43 @@ void TestOneHotInfo2::SetUp() {
 }
 
 TEST_F(TestOneHotInfo2, InferDevMatrixShape1) {
-  Strategys inputs = {{1, 8}, {}, {}};
+  std::vector<Dimensions> inputs = {{1, 8}, {}, {}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status status = onehot_info2->Init(strategy);
   ASSERT_EQ(status, SUCCESS);
-  Shape dev_matrix_shape = onehot_info2->dev_matrix_shape();
+  std::vector<int32_t> dev_matrix_shape = onehot_info2->dev_matrix_shape();
 
-  Shape expect = {8, 1};
+  std::vector<int32_t> expect = {8, 1};
   ASSERT_EQ(dev_matrix_shape, expect);
 }
 
 TEST_F(TestOneHotInfo2, InferDevMatrixShape2) {
-  Strategys inputs = {{1, 4}, {}, {}};
+  std::vector<Dimensions> inputs = {{1, 4}, {}, {}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status status = onehot_info2->Init(strategy);
   ASSERT_EQ(status, SUCCESS);
-  Shape dev_matrix_shape = onehot_info2->dev_matrix_shape();
+  std::vector<int32_t> dev_matrix_shape = onehot_info2->dev_matrix_shape();
 
-  Shape expect = {2, 4, 1};
+  std::vector<int32_t> expect = {2, 4, 1};
   ASSERT_EQ(dev_matrix_shape, expect);
 }
 
 TEST_F(TestOneHotInfo2, InferDevMatrixShape3) {
-  Strategys inputs = {{2, 4}, {}, {}};
+  std::vector<Dimensions> inputs = {{2, 4}, {}, {}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status status = onehot_info2->Init(strategy);
   ASSERT_EQ(status, FAILED);
-  Shape dev_matrix_shape = onehot_info2->dev_matrix_shape();
+  std::vector<int32_t> dev_matrix_shape = onehot_info2->dev_matrix_shape();
 
-  Shape expect = {4, 2};
+  std::vector<int32_t> expect = {4, 2};
   ASSERT_EQ(dev_matrix_shape, expect);
 }
 
 TEST_F(TestOneHotInfo2, InferTensorMap2) {
-  Strategys str = {{1, 8}, {}, {}};
+  std::vector<Dimensions> str = {{1, 8}, {}, {}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   Status status = onehot_info2->Init(strategy);
@@ -122,7 +122,7 @@ TEST_F(TestOneHotInfo2, InferTensorMap2) {
 }
 
 TEST_F(TestOneHotInfo2, InferSliceShape1) {
-  Strategys str = {{1, 8}, {}, {}};
+  std::vector<Dimensions> str = {{1, 8}, {}, {}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   Status status = onehot_info2->Init(strategy);
@@ -144,7 +144,7 @@ TEST_F(TestOneHotInfo2, InferSliceShape1) {
 }
 
 TEST_F(TestOneHotInfo2, InferSliceShape2) {
-  Strategys str = {{2, 4}, {}, {}};
+  std::vector<Dimensions> str = {{2, 4}, {}, {}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   Status status = onehot_info2->Init(strategy);
@@ -166,7 +166,7 @@ TEST_F(TestOneHotInfo2, InferSliceShape2) {
 }
 
 TEST_F(TestOneHotInfo2, InferSliceShape3) {
-  Strategys str = {{2, 2}, {}, {}};
+  std::vector<Dimensions> str = {{2, 2}, {}, {}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   Status status = onehot_info2->Init(strategy);

tests/ut/cpp/parallel/ops_info/pow_info_test.cc

@@ -38,13 +38,13 @@ class TestPowInfo : public UT::Common {
 };
 
 void TestPowInfo::SetUp() {
-  RankList dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 66; i++) {
     dev_list.push_back(i);
   }
 
-  RankList stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(64);
   stage_map.push_back(2);
 
@@ -63,18 +63,18 @@ void TestPowInfo::SetUp() {
 }
 
 TEST_F(TestPowInfo, InferDevMatrixShape1) {
-  Strategys inputs = {{2, 4, 8}, {2, 4, 8}};
+  std::vector<Dimensions> inputs = {{2, 4, 8}, {2, 4, 8}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   pow->Init(strategy);
-  Shape dev_matrix_shape = pow->dev_matrix_shape();
+  std::vector<int32_t> dev_matrix_shape = pow->dev_matrix_shape();
 
-  Shape expect = {2, 4, 8};
+  std::vector<int32_t> expect = {2, 4, 8};
   ASSERT_EQ(dev_matrix_shape, expect);
 }
 
 TEST_F(TestPowInfo, InferSliceShape1) {
-  Strategys str = {{2, 4, 8}, {2, 4, 8}};
+  std::vector<Dimensions> str = {{2, 4, 8}, {2, 4, 8}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   pow->Init(strategy);
@@ -95,7 +95,7 @@ TEST_F(TestPowInfo, InferSliceShape1) {
 }
 
 TEST_F(TestPowInfo, GetTensorLayout1) {
-  Strategys str = {{2, 4, 8}, {2, 4, 8}};
+  std::vector<Dimensions> str = {{2, 4, 8}, {2, 4, 8}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   pow->Init(strategy);
@@ -116,7 +116,7 @@ TEST_F(TestPowInfo, GetTensorLayout1) {
 }
 
 TEST_F(TestPowInfo, GetForwardOp1) {
-  Strategys inputs = {{2, 4, 8}, {2, 4, 8}};
+  std::vector<Dimensions> inputs = {{2, 4, 8}, {2, 4, 8}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   pow->Init(strategy);
@@ -127,7 +127,7 @@ TEST_F(TestPowInfo, GetForwardOp1) {
 }
 
 TEST_F(TestPowInfo, GetMirrorOPs1) {
-  Strategys inputs = {{2, 4, 8}, {2, 4, 8}};
+  std::vector<Dimensions> inputs = {{2, 4, 8}, {2, 4, 8}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   pow->Init(strategy);
@@ -139,7 +139,7 @@ TEST_F(TestPowInfo, GetMirrorOPs1) {
 }
 
 TEST_F(TestPowInfo, CheckStrategy1) {
-  Strategys inputs = {{2, 2, 8}, {2, 4, 8}};
+  std::vector<Dimensions> inputs = {{2, 2, 8}, {2, 4, 8}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = pow->Init(strategy);
@@ -147,7 +147,7 @@ TEST_F(TestPowInfo, CheckStrategy1) {
 }
 
 TEST_F(TestPowInfo, CheckStrategy2) {
-  Strategys inputs = {{2, 4, 8, 16}, {2, 4, 8, 16}};
+  std::vector<Dimensions> inputs = {{2, 4, 8, 16}, {2, 4, 8, 16}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = pow->Init(strategy);
@@ -155,7 +155,7 @@ TEST_F(TestPowInfo, CheckStrategy2) {
 }
 
 TEST_F(TestPowInfo, CheckStrategy3) {
-  Strategys inputs = {{2, 4, 8}, {2, 4, 8}};
+  std::vector<Dimensions> inputs = {{2, 4, 8}, {2, 4, 8}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = pow->Init(strategy);

tests/ut/cpp/parallel/ops_info/prelu_test.cc

@@ -39,13 +39,13 @@ class TestPReLUInfo : public UT::Common {
 };
 
 void TestPReLUInfo::SetUp() {
-  RankList dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 1050; i++) {
     dev_list.push_back(i);
   }
 
-  RankList stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(1024);
   stage_map.push_back(26);
   int32_t local_dev = 0;
@@ -64,18 +64,18 @@ void TestPReLUInfo::SetUp() {
 }
 
 TEST_F(TestPReLUInfo, InferDevMatrixShape1) {
-  Strategys inputs = {{2, 1, 8, 16}, {1}};
+  std::vector<Dimensions> inputs = {{2, 1, 8, 16}, {1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   prelu->Init(strategy);
-  Shape dev_matrix_shape = prelu->dev_matrix_shape();
+  std::vector<int32_t> dev_matrix_shape = prelu->dev_matrix_shape();
 
-  Shape expect = {4, 2, 1, 8, 16};
+  std::vector<int32_t> expect = {4, 2, 1, 8, 16};
   ASSERT_EQ(dev_matrix_shape, expect);
 }
 
 TEST_F(TestPReLUInfo, InferSliceShape1) {
-  Strategys str = {{2, 1, 8, 16}, {1}};
+  std::vector<Dimensions> str = {{2, 1, 8, 16}, {1}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   prelu->Init(strategy);
@@ -98,7 +98,7 @@ TEST_F(TestPReLUInfo, InferSliceShape1) {
 }
 
 TEST_F(TestPReLUInfo, GetTensorLayout1) {
-  Strategys str = {{2, 1, 8, 16}, {1}};
+  std::vector<Dimensions> str = {{2, 1, 8, 16}, {1}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   prelu->Init(strategy);
@@ -122,7 +122,7 @@ TEST_F(TestPReLUInfo, GetTensorLayout1) {
 }
 
 TEST_F(TestPReLUInfo, GetMirrorOPs1) {
-  Strategys str = {{2, 1, 2, 2}, {1}};
+  std::vector<Dimensions> str = {{2, 1, 2, 2}, {1}};
   StrategyPtr strategy = NewStrategy(0, str);
   prelu->Init(strategy);
   MirrorOps mirror_ops = prelu->mirror_ops();
@@ -139,14 +139,14 @@ TEST_F(TestPReLUInfo, GetMirrorOPs1) {
 
 TEST_F(TestPReLUInfo, CheckStrategy1) {
   // Success: {{2,1,8,16},{1}}
-  Strategys inputs = {{2, 1, 8, 16}};
+  std::vector<Dimensions> inputs = {{2, 1, 8, 16}};
   StrategyPtr strategy = NewStrategy(0, inputs);
   Status ret = prelu->Init(strategy);
   ASSERT_EQ(ret, FAILED);
 }
 
 TEST_F(TestPReLUInfo, CheckStrategy2) {
-  Strategys inputs = {{2, 4, 8, 16}, {4}};
+  std::vector<Dimensions> inputs = {{2, 4, 8, 16}, {4}};
   StrategyPtr strategy = NewStrategy(0, inputs);
   Status ret = prelu->Init(strategy);
   ASSERT_EQ(ret, SUCCESS);
@@ -169,18 +169,18 @@ TEST_F(TestPReLUInfo, AutoStrategy1) {
 }
 
 TEST_F(TestPReLUInfo, InferDevMatrixShape_2d1) {
-  Strategys inputs = {{128, 1}, {1}};
+  std::vector<Dimensions> inputs = {{128, 1}, {1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   prelu_2d->Init(strategy);
-  Shape dev_matrix_shape = prelu_2d->dev_matrix_shape();
+  std::vector<int32_t> dev_matrix_shape = prelu_2d->dev_matrix_shape();
 
-  Shape expect = {8, 128, 1};
+  std::vector<int32_t> expect = {8, 128, 1};
   ASSERT_EQ(dev_matrix_shape, expect);
 }
 
 TEST_F(TestPReLUInfo, InferSliceShape_2d1) {
-  Strategys str = {{128, 1}, {1}};
+  std::vector<Dimensions> str = {{128, 1}, {1}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   prelu_2d->Init(strategy);
@@ -203,7 +203,7 @@ TEST_F(TestPReLUInfo, InferSliceShape_2d1) {
 }
 
 TEST_F(TestPReLUInfo, GetTensorLayout_2d1) {
-  Strategys str = {{128, 1}, {1}};
+  std::vector<Dimensions> str = {{128, 1}, {1}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   prelu_2d->Init(strategy);
@@ -227,7 +227,7 @@ TEST_F(TestPReLUInfo, GetTensorLayout_2d1) {
 }
 
 TEST_F(TestPReLUInfo, GetMirrorOPs_2d1) {
-  Strategys str = {{128, 1}, {1}};
+  std::vector<Dimensions> str = {{128, 1}, {1}};
   StrategyPtr strategy = NewStrategy(0, str);
   prelu_2d->Init(strategy);
   MirrorOps mirror_ops = prelu_2d->mirror_ops();
@@ -244,14 +244,14 @@ TEST_F(TestPReLUInfo, GetMirrorOPs_2d1) {
 
 TEST_F(TestPReLUInfo, CheckStrategy_2d1) {
   // Success: {{2,1,8,16},{1}}
-  Strategys inputs = {{128, 1}};
+  std::vector<Dimensions> inputs = {{128, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
   Status ret = prelu_2d->Init(strategy);
   ASSERT_EQ(ret, FAILED);
 }
 
 TEST_F(TestPReLUInfo, CheckStrategy_2d2) {
-  Strategys inputs = {{128, 4}, {4}};
+  std::vector<Dimensions> inputs = {{128, 4}, {4}};
   StrategyPtr strategy = NewStrategy(0, inputs);
   Status ret = prelu_2d->Init(strategy);
   ASSERT_EQ(ret, SUCCESS);

tests/ut/cpp/parallel/ops_info/reduce_method_test.cc

@@ -39,13 +39,13 @@ class TestReduceSumInfo : public UT::Common {
 
 void TestReduceSumInfo::SetUp() {
   UT::InitPythonPath();
-  RankList dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 34; i++) {
     dev_list.push_back(i);
   }
 
-  RankList stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(32);
   stage_map.push_back(2);
 
@@ -68,18 +68,18 @@ void TestReduceSumInfo::SetUp() {
 }
 
 TEST_F(TestReduceSumInfo, InferDevMatrixShape1) {
-  Strategys inputs = {{4, 8, 1}};
+  std::vector<Dimensions> inputs = {{4, 8, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   reduce_sum->Init(strategy);
-  Shape dev_matrix_shape = reduce_sum->dev_matrix_shape();
+  std::vector<int32_t> dev_matrix_shape = reduce_sum->dev_matrix_shape();
 
-  Shape expect = {4, 8, 1};
+  std::vector<int32_t> expect = {4, 8, 1};
   ASSERT_EQ(dev_matrix_shape, expect);
 }
 
 TEST_F(TestReduceSumInfo, InferSliceShape1) {
-  Strategys str = {{4, 8, 1}};
+  std::vector<Dimensions> str = {{4, 8, 1}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   reduce_sum->Init(strategy);
@@ -100,7 +100,7 @@ TEST_F(TestReduceSumInfo, InferSliceShape1) {
 }
 
 TEST_F(TestReduceSumInfo, GetTensorLayout1) {
-  Strategys str = {{4, 8, 1}};
+  std::vector<Dimensions> str = {{4, 8, 1}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   reduce_sum->Init(strategy);
@@ -121,7 +121,7 @@ TEST_F(TestReduceSumInfo, GetTensorLayout1) {
 }
 
 TEST_F(TestReduceSumInfo, GetForwardOp1) {
-  Strategys inputs = {{4, 8, 1}};
+  std::vector<Dimensions> inputs = {{4, 8, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   reduce_sum->Init(strategy);
@@ -132,7 +132,7 @@ TEST_F(TestReduceSumInfo, GetForwardOp1) {
 }
 
 TEST_F(TestReduceSumInfo, GetForwardOp2) {
-  Strategys inputs = {{4, 4, 2}};
+  std::vector<Dimensions> inputs = {{4, 4, 2}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   reduce_sum->Init(strategy);
@@ -156,7 +156,7 @@ TEST_F(TestReduceSumInfo, GetForwardOp2) {
 }
 
 TEST_F(TestReduceSumInfo, GetMirrorOPs1) {
-  Strategys inputs = {{4, 8, 1}};
+  std::vector<Dimensions> inputs = {{4, 8, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   reduce_sum->Init(strategy);
@@ -168,7 +168,7 @@ TEST_F(TestReduceSumInfo, GetMirrorOPs1) {
 }
 
 TEST_F(TestReduceSumInfo, GetMirrorOPs2) {
-  Strategys inputs = {{4, 4, 1}};
+  std::vector<Dimensions> inputs = {{4, 4, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   reduce_sum->Init(strategy);
@@ -187,7 +187,7 @@ TEST_F(TestReduceSumInfo, GetMirrorOPs2) {
 }
 
 TEST_F(TestReduceSumInfo, CheckStrategy1) {
-  Strategys inputs = {{2, 2, 8, 16}};
+  std::vector<Dimensions> inputs = {{2, 2, 8, 16}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = reduce_sum->Init(strategy);
@@ -195,7 +195,7 @@ TEST_F(TestReduceSumInfo, CheckStrategy1) {
 }
 
 TEST_F(TestReduceSumInfo, CheckStrategy2) {
-  Strategys inputs = {{2, 4, 8}, {2, 4, 8}};
+  std::vector<Dimensions> inputs = {{2, 4, 8}, {2, 4, 8}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = reduce_sum->Init(strategy);
@@ -203,7 +203,7 @@ TEST_F(TestReduceSumInfo, CheckStrategy2) {
 }
 
 TEST_F(TestReduceSumInfo, CheckStrategy3) {
-  Strategys inputs = {{4, 4, 2}};
+  std::vector<Dimensions> inputs = {{4, 4, 2}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = reduce_sum->Init(strategy);
@@ -211,7 +211,7 @@ TEST_F(TestReduceSumInfo, CheckStrategy3) {
 }
 
 TEST_F(TestReduceSumInfo, CheckStrategy4) {
-  Strategys inputs = {{4, 8, 1}};
+  std::vector<Dimensions> inputs = {{4, 8, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = reduce_sum->Init(strategy);

tests/ut/cpp/parallel/ops_info/reshape_test.cc

@@ -38,13 +38,13 @@ class TestReshapeInfo : public UT::Common {
 };
 
 void TestReshapeInfo::SetUp() {
-  RankList dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 34; i++) {
     dev_list.push_back(i);
   }
 
-  RankList stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(32);
   stage_map.push_back(2);
 
@@ -68,29 +68,29 @@ void TestReshapeInfo::SetUp() {
 }
 
 TEST_F(TestReshapeInfo, InferDevMatrixShape1) {
-  Strategys inputs = {{4, 1, 1, 1}};
+  std::vector<Dimensions> inputs = {{4, 1, 1, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   reshape->Init(strategy);
-  Shape dev_matrix_shape = reshape->dev_matrix_shape();
+  std::vector<int32_t> dev_matrix_shape = reshape->dev_matrix_shape();
 
-  Shape expect = {8, 4};
+  std::vector<int32_t> expect = {8, 4};
   ASSERT_EQ(dev_matrix_shape, expect);
 }
 
 TEST_F(TestReshapeInfo, InferDevMatrixShape2) {
-  Strategys inputs = {{32, 1, 1, 1}};
+  std::vector<Dimensions> inputs = {{32, 1, 1, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   reshape->Init(strategy);
-  Shape dev_matrix_shape = reshape->dev_matrix_shape();
+  std::vector<int32_t> dev_matrix_shape = reshape->dev_matrix_shape();
 
-  Shape expect = {32};
+  std::vector<int32_t> expect = {32};
   ASSERT_EQ(dev_matrix_shape, expect);
 }
 
 TEST_F(TestReshapeInfo, InferSliceShape1) {
-  Strategys str = {{4, 1, 1, 1}};
+  std::vector<Dimensions> str = {{4, 1, 1, 1}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   reshape->Init(strategy);
@@ -111,7 +111,7 @@ TEST_F(TestReshapeInfo, InferSliceShape1) {
 }
 
 TEST_F(TestReshapeInfo, InferSliceShape2) {
-  Strategys str = {{32, 1, 1, 1}};
+  std::vector<Dimensions> str = {{32, 1, 1, 1}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   reshape->Init(strategy);
@@ -132,7 +132,7 @@ TEST_F(TestReshapeInfo, InferSliceShape2) {
 }
 
 TEST_F(TestReshapeInfo, GetTensorLayout1) {
-  Strategys str = {{4, 1, 1, 1}};
+  std::vector<Dimensions> str = {{4, 1, 1, 1}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   reshape->Init(strategy);
@@ -153,7 +153,7 @@ TEST_F(TestReshapeInfo, GetTensorLayout1) {
 }
 
 TEST_F(TestReshapeInfo, GetTensorLayout2) {
-  Strategys str = {{32, 1, 1, 1}};
+  std::vector<Dimensions> str = {{32, 1, 1, 1}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   reshape->Init(strategy);
@@ -174,7 +174,7 @@ TEST_F(TestReshapeInfo, GetTensorLayout2) {
 }
 
 TEST_F(TestReshapeInfo, GetForwardOp1) {
-  Strategys inputs = {{4, 1, 1, 1}};
+  std::vector<Dimensions> inputs = {{4, 1, 1, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   reshape->Init(strategy);
@@ -185,7 +185,7 @@ TEST_F(TestReshapeInfo, GetForwardOp1) {
 }
 
 TEST_F(TestReshapeInfo, GetMirrorOPs1) {
-  Strategys inputs = {{4, 1, 1, 1}};
+  std::vector<Dimensions> inputs = {{4, 1, 1, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   reshape->Init(strategy);
@@ -197,7 +197,7 @@ TEST_F(TestReshapeInfo, GetMirrorOPs1) {
 }
 
 TEST_F(TestReshapeInfo, CheckStrategy1) {
-  Strategys inputs = {{1, 4, 8}};
+  std::vector<Dimensions> inputs = {{1, 4, 8}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = reshape->Init(strategy);
@@ -205,7 +205,7 @@ TEST_F(TestReshapeInfo, CheckStrategy1) {
 }
 
 TEST_F(TestReshapeInfo, CheckStrategy2) {
-  Strategys inputs = {{2, 4, 8}, {2, 4, 8}};
+  std::vector<Dimensions> inputs = {{2, 4, 8}, {2, 4, 8}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = reshape->Init(strategy);
@@ -213,7 +213,7 @@ TEST_F(TestReshapeInfo, CheckStrategy2) {
 }
 
 TEST_F(TestReshapeInfo, CheckStrategy3) {
-  Strategys inputs = {{4, 1, 1, 1}};
+  std::vector<Dimensions> inputs = {{4, 1, 1, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = reshape->Init(strategy);

tests/ut/cpp/parallel/ops_info/softmax_entropy_loss_info_test.cc

@@ -38,13 +38,13 @@ class TestSoftmaxLoss : public UT::Common {
 };
 
 void TestSoftmaxLoss::SetUp() {
-  RankList dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 65; i++) {
     dev_list.push_back(i);
   }
 
-  RankList stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(64);
   stage_map.push_back(1);
 
@@ -64,18 +64,18 @@ void TestSoftmaxLoss::SetUp() {
 }
 
 TEST_F(TestSoftmaxLoss, InferDevMatrixShape1) {
-  Strategys inputs = {{2, 4, 8, 1}, {2, 4, 8, 1}};
+  std::vector<Dimensions> inputs = {{2, 4, 8, 1}, {2, 4, 8, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   loss->Init(strategy);
-  Shape dev_matrix_shape = loss->dev_matrix_shape();
+  std::vector<int32_t> dev_matrix_shape = loss->dev_matrix_shape();
 
-  Shape expect = {2, 4, 8, 1};
+  std::vector<int32_t> expect = {2, 4, 8, 1};
   ASSERT_EQ(dev_matrix_shape, expect);
 }
 
 TEST_F(TestSoftmaxLoss, InferSliceShape1) {
-  Strategys str = {{2, 4, 8, 1}, {2, 4, 8, 1}};
+  std::vector<Dimensions> str = {{2, 4, 8, 1}, {2, 4, 8, 1}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   loss->Init(strategy);
@@ -104,7 +104,7 @@ TEST_F(TestSoftmaxLoss, InferSliceShape1) {
 }
 
 TEST_F(TestSoftmaxLoss, GetTensorLayout1) {
-  Strategys str = {{2, 4, 8, 1}, {2, 4, 8, 1}};
+  std::vector<Dimensions> str = {{2, 4, 8, 1}, {2, 4, 8, 1}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   loss->Init(strategy);
@@ -133,7 +133,7 @@ TEST_F(TestSoftmaxLoss, GetTensorLayout1) {
 }
 
 TEST_F(TestSoftmaxLoss, GetForwardOp1) {
-  Strategys inputs = {{2, 4, 8, 1}, {2, 4, 8, 1}};
+  std::vector<Dimensions> inputs = {{2, 4, 8, 1}, {2, 4, 8, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   loss->Init(strategy);
@@ -144,7 +144,7 @@ TEST_F(TestSoftmaxLoss, GetForwardOp1) {
 }
 
 TEST_F(TestSoftmaxLoss, GetMirrorOPs1) {
-  Strategys inputs = {{2, 4, 8, 1}, {2, 4, 8, 1}};
+  std::vector<Dimensions> inputs = {{2, 4, 8, 1}, {2, 4, 8, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   loss->Init(strategy);
@@ -156,7 +156,7 @@ TEST_F(TestSoftmaxLoss, GetMirrorOPs1) {
 }
 
 TEST_F(TestSoftmaxLoss, GetVirtualDivOPs1) {
-  Strategys inputs = {{1, 4, 8, 1}, {1, 4, 8, 1}};
+  std::vector<Dimensions> inputs = {{1, 4, 8, 1}, {1, 4, 8, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   loss->Init(strategy);
@@ -176,7 +176,7 @@ TEST_F(TestSoftmaxLoss, GetVirtualDivOPs1) {
 
 TEST_F(TestSoftmaxLoss, CheckStrategy1) {
   // Success: {{2,4,8,16}}
-  Strategys inputs = {{2, 2, 8, 16}, {2, 4, 16, 1}};
+  std::vector<Dimensions> inputs = {{2, 2, 8, 16}, {2, 4, 16, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = loss->Init(strategy);
@@ -185,7 +185,7 @@ TEST_F(TestSoftmaxLoss, CheckStrategy1) {
 
 TEST_F(TestSoftmaxLoss, CheckStrategy2) {
   // Success: {{2,4,8,16}}
-  Strategys inputs = {{2, 4, 8}};
+  std::vector<Dimensions> inputs = {{2, 4, 8}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = loss->Init(strategy);

tests/ut/cpp/parallel/ops_info/softmax_info_test.cc

@@ -39,13 +39,13 @@ class TestSoftmaxInfo : public UT::Common {
 };
 
 void TestSoftmaxInfo::SetUp() {
-  RankList dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 130; i++) {
     dev_list.push_back(i);
   }
 
-  RankList stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(128);
   stage_map.push_back(2);
 
@@ -68,18 +68,18 @@ void TestSoftmaxInfo::SetUp() {
 }
 
 TEST_F(TestSoftmaxInfo, InferDevMatrixShape1) {
-  Strategys inputs = {{2, 4, 1, 16}};
+  std::vector<Dimensions> inputs = {{2, 4, 1, 16}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   softmax->Init(strategy);
-  Shape dev_matrix_shape = softmax->dev_matrix_shape();
+  std::vector<int32_t> dev_matrix_shape = softmax->dev_matrix_shape();
 
-  Shape expect = {2, 4, 1, 16};
+  std::vector<int32_t> expect = {2, 4, 1, 16};
   ASSERT_EQ(dev_matrix_shape, expect);
 }
 
 TEST_F(TestSoftmaxInfo, InferSliceShape1) {
-  Strategys str = {{2, 4, 1, 16}};
+  std::vector<Dimensions> str = {{2, 4, 1, 16}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   softmax->Init(strategy);
@@ -100,7 +100,7 @@ TEST_F(TestSoftmaxInfo, InferSliceShape1) {
 }
 
 TEST_F(TestSoftmaxInfo, GetTensorLayout1) {
-  Strategys str = {{2, 4, 1, 16}};
+  std::vector<Dimensions> str = {{2, 4, 1, 16}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   softmax->Init(strategy);
@@ -121,7 +121,7 @@ TEST_F(TestSoftmaxInfo, GetTensorLayout1) {
 }
 
 TEST_F(TestSoftmaxInfo, GetForwardOp1) {
-  Strategys inputs = {{2, 4, 1, 16}};
+  std::vector<Dimensions> inputs = {{2, 4, 1, 16}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   softmax->Init(strategy);
@@ -132,7 +132,7 @@ TEST_F(TestSoftmaxInfo, GetForwardOp1) {
 }
 
 TEST_F(TestSoftmaxInfo, GetMirrorOPs1) {
-  Strategys inputs = {{2, 4, 1, 16}};
+  std::vector<Dimensions> inputs = {{2, 4, 1, 16}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   softmax->Init(strategy);
@@ -145,7 +145,7 @@ TEST_F(TestSoftmaxInfo, GetMirrorOPs1) {
 
 TEST_F(TestSoftmaxInfo, CheckStrategy1) {
   // Success: {{2,4,1,16}}
-  Strategys inputs = {{2, 2, 8, 16}, {2, 4, 16, 1}};
+  std::vector<Dimensions> inputs = {{2, 2, 8, 16}, {2, 4, 16, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = softmax->Init(strategy);
@@ -154,7 +154,7 @@ TEST_F(TestSoftmaxInfo, CheckStrategy1) {
 
 TEST_F(TestSoftmaxInfo, CheckStrategy2) {
   // Success: {{2,4,1,16}}
-  Strategys inputs = {{2, 4, 8}};
+  std::vector<Dimensions> inputs = {{2, 4, 8}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = softmax->Init(strategy);
@@ -163,7 +163,7 @@ TEST_F(TestSoftmaxInfo, CheckStrategy2) {
 
 TEST_F(TestSoftmaxInfo, CheckStrategy3) {
   // Success: {{2,4,1,16}}
-  Strategys inputs = {{2, 4, 8, 16}};
+  std::vector<Dimensions> inputs = {{2, 4, 8, 16}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = softmax->Init(strategy);
@@ -172,7 +172,7 @@ TEST_F(TestSoftmaxInfo, CheckStrategy3) {
 
 TEST_F(TestSoftmaxInfo, InitFailed1) {
   // softmax2's axis is wrong
-  Strategys inputs = {{2, 4, 1, 16}};
+  std::vector<Dimensions> inputs = {{2, 4, 1, 16}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = softmax2->Init(strategy);
@@ -181,7 +181,7 @@ TEST_F(TestSoftmaxInfo, InitFailed1) {
 
 TEST_F(TestSoftmaxInfo, InitFailed2) {
   // dev num is wrong
-  Strategys inputs = {{2, 4, 1, 100}};
+  std::vector<Dimensions> inputs = {{2, 4, 1, 100}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = softmax2->Init(strategy);

tests/ut/cpp/parallel/ops_info/tanh_info_test.cc

@@ -38,13 +38,13 @@ class TestTanhInfo : public UT::Common {
 };
 
 void TestTanhInfo::SetUp() {
-  RankList dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 130; i++) {
     dev_list.push_back(i);
   }
 
-  RankList stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(128);
   stage_map.push_back(2);
 
@@ -63,18 +63,18 @@ void TestTanhInfo::SetUp() {
 }
 
 TEST_F(TestTanhInfo, InferDevMatrixShape1) {
-  Strategys inputs = {{2, 4, 1, 16}};
+  std::vector<Dimensions> inputs = {{2, 4, 1, 16}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   tanh->Init(strategy);
-  Shape dev_matrix_shape = tanh->dev_matrix_shape();
+  std::vector<int32_t> dev_matrix_shape = tanh->dev_matrix_shape();
 
-  Shape expect = {2, 4, 1, 16};
+  std::vector<int32_t> expect = {2, 4, 1, 16};
   ASSERT_EQ(dev_matrix_shape, expect);
 }
 
 TEST_F(TestTanhInfo, InferSliceShape1) {
-  Strategys str = {{2, 4, 1, 16}};
+  std::vector<Dimensions> str = {{2, 4, 1, 16}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   tanh->Init(strategy);
@@ -95,7 +95,7 @@ TEST_F(TestTanhInfo, InferSliceShape1) {
 }
 
 TEST_F(TestTanhInfo, GetTensorLayout1) {
-  Strategys str = {{2, 4, 1, 16}};
+  std::vector<Dimensions> str = {{2, 4, 1, 16}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   tanh->Init(strategy);
@@ -116,7 +116,7 @@ TEST_F(TestTanhInfo, GetTensorLayout1) {
 }
 
 TEST_F(TestTanhInfo, GetForwardOp1) {
-  Strategys inputs = {{2, 4, 1, 16}};
+  std::vector<Dimensions> inputs = {{2, 4, 1, 16}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   tanh->Init(strategy);
@@ -127,7 +127,7 @@ TEST_F(TestTanhInfo, GetForwardOp1) {
 }
 
 TEST_F(TestTanhInfo, GetMirrorOPs1) {
-  Strategys inputs = {{2, 4, 1, 16}};
+  std::vector<Dimensions> inputs = {{2, 4, 1, 16}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   tanh->Init(strategy);
@@ -140,7 +140,7 @@ TEST_F(TestTanhInfo, GetMirrorOPs1) {
 
 TEST_F(TestTanhInfo, CheckStrategy1) {
   // Success: {{2,4,1,16}}
-  Strategys inputs = {{2, 2, 8, 16}, {2, 4, 16, 1}};
+  std::vector<Dimensions> inputs = {{2, 2, 8, 16}, {2, 4, 16, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = tanh->Init(strategy);
@@ -149,7 +149,7 @@ TEST_F(TestTanhInfo, CheckStrategy1) {
 
 TEST_F(TestTanhInfo, CheckStrategy2) {
   // Success: {{2,4,1,16}}
-  Strategys inputs = {{2, 4, 8}};
+  std::vector<Dimensions> inputs = {{2, 4, 8}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = tanh->Init(strategy);
@@ -158,7 +158,7 @@ TEST_F(TestTanhInfo, CheckStrategy2) {
 
 TEST_F(TestTanhInfo, CheckStrategy3) {
   // Success: {{2,4,1,16}}
-  Strategys inputs = {{2, 4, 1, 16}};
+  std::vector<Dimensions> inputs = {{2, 4, 1, 16}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = tanh->Init(strategy);

tests/ut/cpp/parallel/ops_info/tensor_add_info_test.cc

@@ -38,13 +38,13 @@ class TestTensorAddInfo : public UT::Common {
 };
 
 void TestTensorAddInfo::SetUp() {
-  RankList dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 34; i++) {
     dev_list.push_back(i);
   }
 
-  RankList stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(32);
   stage_map.push_back(2);
 
@@ -66,18 +66,18 @@ void TestTensorAddInfo::SetUp() {
 }
 
 TEST_F(TestTensorAddInfo, InferDevMatrixShape1) {
-  Strategys inputs = {{2, 4, 4}, {2, 4, 4}};
+  std::vector<Dimensions> inputs = {{2, 4, 4}, {2, 4, 4}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   tensor_add->Init(strategy);
-  Shape dev_matrix_shape = tensor_add->dev_matrix_shape();
+  std::vector<int32_t> dev_matrix_shape = tensor_add->dev_matrix_shape();
 
-  Shape expect = {2, 4, 4};
+  std::vector<int32_t> expect = {2, 4, 4};
   ASSERT_EQ(dev_matrix_shape, expect);
 }
 
 TEST_F(TestTensorAddInfo, InferSliceShape1) {
-  Strategys str = {{2, 4, 4}, {2, 4, 4}};
+  std::vector<Dimensions> str = {{2, 4, 4}, {2, 4, 4}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   tensor_add->Init(strategy);
@@ -101,7 +101,7 @@ TEST_F(TestTensorAddInfo, InferSliceShape1) {
 }
 
 TEST_F(TestTensorAddInfo, GetTensorLayout1) {
-  Strategys str = {{2, 4, 4}, {2, 4, 4}};
+  std::vector<Dimensions> str = {{2, 4, 4}, {2, 4, 4}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   tensor_add->Init(strategy);
@@ -125,7 +125,7 @@ TEST_F(TestTensorAddInfo, GetTensorLayout1) {
 }
 
 TEST_F(TestTensorAddInfo, GetForwardOp1) {
-  Strategys inputs = {{2, 4, 4}, {2, 4, 4}};
+  std::vector<Dimensions> inputs = {{2, 4, 4}, {2, 4, 4}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   tensor_add->Init(strategy);
@@ -136,7 +136,7 @@ TEST_F(TestTensorAddInfo, GetForwardOp1) {
 }
 
 TEST_F(TestTensorAddInfo, GetMirrorOPs1) {
-  Strategys inputs = {{2, 4, 4}, {2, 4, 4}};
+  std::vector<Dimensions> inputs = {{2, 4, 4}, {2, 4, 4}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   tensor_add->Init(strategy);
@@ -148,7 +148,7 @@ TEST_F(TestTensorAddInfo, GetMirrorOPs1) {
 }
 
 TEST_F(TestTensorAddInfo, CheckStrategy1) {
-  Strategys inputs = {{2, 4, 4}, {2, 6, 4}};
+  std::vector<Dimensions> inputs = {{2, 4, 4}, {2, 6, 4}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = tensor_add->Init(strategy);
@@ -156,7 +156,7 @@ TEST_F(TestTensorAddInfo, CheckStrategy1) {
 }
 
 TEST_F(TestTensorAddInfo, CheckStrategy2) {
-  Strategys inputs = {{2, 4, 8}, {2, 4, 8}};
+  std::vector<Dimensions> inputs = {{2, 4, 8}, {2, 4, 8}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = tensor_add->Init(strategy);
@@ -164,7 +164,7 @@ TEST_F(TestTensorAddInfo, CheckStrategy2) {
 }
 
 TEST_F(TestTensorAddInfo, CheckStrategy3) {
-  Strategys inputs = {{2, 4, 6}};
+  std::vector<Dimensions> inputs = {{2, 4, 6}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = tensor_add->Init(strategy);
@@ -172,7 +172,7 @@ TEST_F(TestTensorAddInfo, CheckStrategy3) {
 }
 
 TEST_F(TestTensorAddInfo, CheckStrategy4) {
-  Strategys inputs = {{2, 4, 4}, {2, 4, 4}};
+  std::vector<Dimensions> inputs = {{2, 4, 4}, {2, 4, 4}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = tensor_add->Init(strategy);
@@ -224,7 +224,7 @@ TEST_F(TestTensorAddInfo, GenerateStrategies1) {
 }
 
 TEST_F(TestTensorAddInfo, mirror_ops) {
-  Strategys inputs = {{1, 8}, {4, 1}};
+  std::vector<Dimensions> inputs = {{1, 8}, {4, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   tensor_add1->Init(strategy);

tests/ut/cpp/parallel/ops_info/tmpidentity_test.cc

@@ -19,7 +19,6 @@
 #include "frontend/parallel/device_manager.h"
 #include "frontend/parallel/ops_info/operator_info.h"
 #include "frontend/parallel/ops_info/tmp_identity_info.h"
-#include "frontend/parallel/step_parallel.h"
 
 namespace mindspore {
 namespace parallel {
@@ -27,6 +26,7 @@ namespace parallel {
 class TmpIdentityInfo;
 using TmpIdentityInfoPtr = std::shared_ptr<TmpIdentityInfo>;
 TmpIdentityInfoPtr identity_ptr;
+using TensorMap = std::vector<int32_t>;
 
 class TestTmpIdentityInfo : public UT::Common {
  public:
@@ -38,13 +38,13 @@ class TestTmpIdentityInfo : public UT::Common {
 };
 
 void TestTmpIdentityInfo::SetUp() {
-  RankList dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 1050; i++) {
     dev_list.push_back(i);
   }
 
-  RankList stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(1024);
   stage_map.push_back(26);
 
@@ -65,18 +65,18 @@ void TestTmpIdentityInfo::SetUp() {
 }
 
 TEST_F(TestTmpIdentityInfo, InferDevMatrixShape1) {
-  Strategys inputs = {{2, 4, 8, 16}};
+  std::vector<Dimensions> inputs = {{2, 4, 8, 16}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   identity_ptr->Init(strategy);
-  Shape dev_matrix_shape = identity_ptr->dev_matrix_shape();
+  std::vector<int32_t> dev_matrix_shape = identity_ptr->dev_matrix_shape();
 
-  Shape expect = {2, 4, 8, 16};
+  std::vector<int32_t> expect = {2, 4, 8, 16};
   ASSERT_EQ(dev_matrix_shape, expect);
 }
 
 TEST_F(TestTmpIdentityInfo, InferSliceShape1) {
-  Strategys str = {{2, 4, 8, 16}};
+  std::vector<Dimensions> str = {{2, 4, 8, 16}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   identity_ptr->Init(strategy);
@@ -97,7 +97,7 @@ TEST_F(TestTmpIdentityInfo, InferSliceShape1) {
 }
 
 TEST_F(TestTmpIdentityInfo, GetTensorLayout1) {
-  Strategys str = {{2, 4, 8, 16}};
+  std::vector<Dimensions> str = {{2, 4, 8, 16}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   identity_ptr->Init(strategy);
@@ -119,7 +119,7 @@ TEST_F(TestTmpIdentityInfo, GetTensorLayout1) {
 
 TEST_F(TestTmpIdentityInfo, CheckStrategy1) {
   // Success: {{2,4,8,16}}
-  Strategys inputs = {{2, 2, 8, 16}, {2, 4, 16, 1}};
+  std::vector<Dimensions> inputs = {{2, 2, 8, 16}, {2, 4, 16, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = identity_ptr->Init(strategy);
@@ -128,7 +128,7 @@ TEST_F(TestTmpIdentityInfo, CheckStrategy1) {
 
 TEST_F(TestTmpIdentityInfo, CheckStrategy2) {
   // Success: {{2,4,8,16}}
-  Strategys inputs = {{2, 4, 8}};
+  std::vector<Dimensions> inputs = {{2, 4, 8}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = identity_ptr->Init(strategy);

tests/ut/cpp/parallel/ops_info/transpose_test.cc

@@ -38,13 +38,13 @@ class TestTransposeInfo : public UT::Common {
 };
 
 void TestTransposeInfo::SetUp() {
-  RankList dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 34; i++) {
     dev_list.push_back(i);
   }
 
-  RankList stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(32);
   stage_map.push_back(2);
 
@@ -68,29 +68,29 @@ void TestTransposeInfo::SetUp() {
 }
 
 TEST_F(TestTransposeInfo, InferDevMatrixShape1) {
-  Strategys inputs = {{4, 8}};
+  std::vector<Dimensions> inputs = {{4, 8}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   transpose->Init(strategy);
-  Shape dev_matrix_shape = transpose->dev_matrix_shape();
+  std::vector<int32_t> dev_matrix_shape = transpose->dev_matrix_shape();
 
-  Shape expect = {4, 8};
+  std::vector<int32_t> expect = {4, 8};
   ASSERT_EQ(dev_matrix_shape, expect);
 }
 
 TEST_F(TestTransposeInfo, InferDevMatrixShape2) {
-  Strategys inputs = {{4, 1}};
+  std::vector<Dimensions> inputs = {{4, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   transpose->Init(strategy);
-  Shape dev_matrix_shape = transpose->dev_matrix_shape();
+  std::vector<int32_t> dev_matrix_shape = transpose->dev_matrix_shape();
 
-  Shape expect = {8, 4, 1};
+  std::vector<int32_t> expect = {8, 4, 1};
   ASSERT_EQ(dev_matrix_shape, expect);
 }
 
 TEST_F(TestTransposeInfo, InferSliceShape1) {
-  Strategys str = {{4, 8}};
+  std::vector<Dimensions> str = {{4, 8}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   transpose->Init(strategy);
@@ -111,7 +111,7 @@ TEST_F(TestTransposeInfo, InferSliceShape1) {
 }
 
 TEST_F(TestTransposeInfo, GetTensorLayout1) {
-  Strategys str = {{4, 8}};
+  std::vector<Dimensions> str = {{4, 8}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   transpose->Init(strategy);
@@ -132,7 +132,7 @@ TEST_F(TestTransposeInfo, GetTensorLayout1) {
 }
 
 TEST_F(TestTransposeInfo, GetForwardOp1) {
-  Strategys inputs = {{4, 8}};
+  std::vector<Dimensions> inputs = {{4, 8}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   transpose->Init(strategy);
@@ -143,7 +143,7 @@ TEST_F(TestTransposeInfo, GetForwardOp1) {
 }
 
 TEST_F(TestTransposeInfo, GetMirrorOPs1) {
-  Strategys inputs = {{4, 8}};
+  std::vector<Dimensions> inputs = {{4, 8}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   transpose->Init(strategy);
@@ -155,7 +155,7 @@ TEST_F(TestTransposeInfo, GetMirrorOPs1) {
 }
 
 TEST_F(TestTransposeInfo, CheckStrategy1) {
-  Strategys inputs = {{1, 4, 8}};
+  std::vector<Dimensions> inputs = {{1, 4, 8}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = transpose->Init(strategy);
@@ -163,7 +163,7 @@ TEST_F(TestTransposeInfo, CheckStrategy1) {
 }
 
 TEST_F(TestTransposeInfo, CheckStrategy2) {
-  Strategys inputs = {{2, 4, 8}, {2, 4, 8}};
+  std::vector<Dimensions> inputs = {{2, 4, 8}, {2, 4, 8}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = transpose->Init(strategy);
@@ -171,7 +171,7 @@ TEST_F(TestTransposeInfo, CheckStrategy2) {
 }
 
 TEST_F(TestTransposeInfo, CheckStrategy3) {
-  Strategys inputs = {{4, 8}};
+  std::vector<Dimensions> inputs = {{4, 8}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = transpose->Init(strategy);

tests/ut/cpp/parallel/step_auto_parallel_test.cc

@@ -32,13 +32,13 @@ class TestStepAutoParallel : public UT::Common {
 };
 
 void TestStepAutoParallel::SetUp() {
-  RankList dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 20; i++) {
     dev_list.push_back(i);
   }
 
-  RankList stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(16);
   stage_map.push_back(4);
 

tests/ut/cpp/parallel/step_parallel_test.cc

@@ -34,13 +34,13 @@ class TestStepParallel : public UT::Common {
 void TestStepParallel::SetUp() { UT::InitPythonPath(); }
 
 void Init_Device_Manager() {
-  RankList dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 20; i++) {
     dev_list.push_back(i);
   }
 
-  RankList stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(16);
   stage_map.push_back(4);
 
@@ -52,26 +52,17 @@ void Init_Device_Manager() {
 }
 
 CNodePtr Make_Node(Shape x, Shape y, Shape out, int condition = 0) {
-  std::vector<int32_t> x_shape;
-  std::vector<int32_t> y_shape;
-  std::vector<int32_t> out_shape;
   FuncGraphPtr func_graph = std::make_shared<FuncGraph>();
   ParameterPtr param1 = func_graph->add_parameter();
   ParameterPtr param2 = func_graph->add_parameter();
-  (void)std::transform(x.begin(), x.end(), std::back_inserter(x_shape),
-                       [](const int64_t &value) { return static_cast<int>(value); });
-  (void)std::transform(y.begin(), y.end(), std::back_inserter(y_shape),
-                       [](const int64_t &value) { return static_cast<int>(value); });
-  (void)std::transform(out.begin(), out.end(), std::back_inserter(out_shape),
-                       [](const int64_t &value) { return static_cast<int>(value); });
   param1->set_name("x");
   param2->set_name("y");
   BaseShapePtr shape1 = std::make_shared<abstract::Shape>(x);
   BaseShapePtr shape2 = std::make_shared<abstract::Shape>(y);
   BaseShapePtr shape3 = std::make_shared<abstract::Shape>(out);
-  std::shared_ptr<tensor::Tensor> inputs_x = std::make_shared<tensor::Tensor>(kNumberTypeInt32, x_shape);
-  std::shared_ptr<tensor::Tensor> inputs_y = std::make_shared<tensor::Tensor>(kNumberTypeInt32, y_shape);
-  std::shared_ptr<tensor::Tensor> inputs_out = std::make_shared<tensor::Tensor>(kNumberTypeInt32, out_shape);
+  std::shared_ptr<tensor::Tensor> inputs_x = std::make_shared<tensor::Tensor>(kNumberTypeInt32, x);
+  std::shared_ptr<tensor::Tensor> inputs_y = std::make_shared<tensor::Tensor>(kNumberTypeInt32, y);
+  std::shared_ptr<tensor::Tensor> inputs_out = std::make_shared<tensor::Tensor>(kNumberTypeInt32, out);
   AbstractBasePtr abstract1 = abstract::FromValue(inputs_x, true);
   AbstractBasePtr abstract2 = abstract::FromValue(inputs_y, true);
   AbstractBasePtr abstract3 = abstract::FromValue(inputs_out, true);
@@ -121,11 +112,11 @@ CNodePtr Make_Node(Shape x, Shape y, Shape out, int condition = 0) {
 }
 
 FuncGraphManagerPtr Make_Manager(int condition = 0) {
-  std::vector<int32_t> inputs_x = {64, 32};
-  std::vector<int32_t> inputs_y = {32, 64};
-  std::vector<int32_t> inputs_z = {64, 128};
-  std::vector<int32_t> outputs_1 = {64, 64};
-  std::vector<int32_t> outputs_2 = {64, 128};
+  Shape inputs_x = {64, 32};
+  Shape inputs_y = {32, 64};
+  Shape inputs_z = {64, 128};
+  Shape outputs_1 = {64, 64};
+  Shape outputs_2 = {64, 128};
   FuncGraphPtr func_graph = std::make_shared<FuncGraph>();
   ParameterPtr param1 = func_graph->add_parameter();
   ParameterPtr param2 = func_graph->add_parameter();
@@ -143,8 +134,8 @@ FuncGraphManagerPtr Make_Manager(int condition = 0) {
   param1->set_abstract(abstract_x);
   param2->set_abstract(abstract_y);
   param3->set_abstract(abstract_z);
-  Dimensions v1 = {2, 2};
-  Dimensions v2 = {2, 4};
+  std::vector<int> v1 = {2, 2};
+  std::vector<int> v2 = {2, 4};
   std::vector<ValuePtr> elements = {MakeValue(v1), MakeValue(v2)};
   ValueTuplePtr var = std::make_shared<ValueTuple>(elements);
   std::vector<AnfNodePtr> inputs;
@@ -162,8 +153,8 @@ FuncGraphManagerPtr Make_Manager(int condition = 0) {
   prim1->AddAttr("instance_name", MakeValue("matmul1"));
   prim1->AddAttr("strategy", var);
   inputs.clear();
-  Dimensions v3 = {2, 2};
-  Dimensions v4 = {2, 4};
+  std::vector<int> v3 = {2, 2};
+  std::vector<int> v4 = {2, 4};
   std::vector<ValuePtr> elements2 = {MakeValue(v3), MakeValue(v4)};
   ValueTuplePtr var2 = std::make_shared<ValueTuple>(elements2);
   inputs.push_back(NewValueNode(prim::kPrimMatMul));
@@ -195,8 +186,8 @@ FuncGraphManagerPtr Make_Manager(int condition = 0) {
       break;
     }
     case 3: {
-      Dimensions vt1 = {2, 4};
-      Dimensions vt2 = {2, 4};
+      std::vector<int> vt1 = {2, 4};
+      std::vector<int> vt2 = {2, 4};
       std::vector<ValuePtr> elements_t2 = {MakeValue(vt1), MakeValue(vt2)};
       ValueTuplePtr var_t2 = std::make_shared<ValueTuple>(elements_t2);
       prim1->set_attr("strategy", var_t2);
@@ -233,9 +224,9 @@ TEST_F(TestStepParallel, ExtractStrategy) {
   std::vector<ValuePtr> elements = {val1, val2};
   ValueTuplePtr strategy_tuple = std::make_shared<ValueTuple>(elements);
   attrs["strategy"] = strategy_tuple;
-  Strategys strategy_expect = {v1, v2};
+  std::vector<Dimensions> strategy_expect = {v1, v2};
   StrategyPtr strategy = ExtractStrategy(attrs);
-  Strategys strategy_test = strategy->GetInputDim();
+  std::vector<Dimensions> strategy_test = strategy->GetInputDim();
 
   ASSERT_EQ(strategy_expect, strategy_test);
 }
@@ -362,7 +353,7 @@ TEST_F(TestStepParallel, OperatorInstance) {
   prim->set_attr("transpose_b", transpose_b);
   auto attrs = prim->attrs();
   // creat strategy
-  Strategys strategy = {{2, 2}, {2, 4}};
+  std::vector<Dimensions> strategy = {{2, 2}, {2, 4}};
   StrategyPtr strategyPtr = parallel::NewStrategy(0, strategy);
   // creat shape
   Shapes inputs_shape = std::vector<Shape>{{64, 32}, {32, 64}};
@@ -523,7 +514,7 @@ TEST_F(TestStepParallel, GetTensorInLayout) {
   prim->set_attr("transpose_b", transpose_b);
   auto attrs = prim->attrs();
   // creat strategy
-  Strategys strategy = {{2, 2}, {2, 4}};
+  std::vector<Dimensions> strategy = {{2, 2}, {2, 4}};
   StrategyPtr strategyPtr = parallel::NewStrategy(0, strategy);
   // creat shape
   Shapes inputs_shape = std::vector<Shape>{{64, 32}, {32, 64}};
@@ -534,9 +525,9 @@ TEST_F(TestStepParallel, GetTensorInLayout) {
   node->set_user_data<OperatorInfo>(matmul_info);
   OperatorInfoPtr distribute_operator_pre = node->user_data<OperatorInfo>();
   TensorLayout tensorlayout_e;
-  Shape array = {64, 64};
+  std::vector<int32_t> array = {64, 64};
   TensorLayout tensorlayout = GetTensorInLayout(node1, prim, distribute_operator_pre);
-  Shape tensor_shape_test = tensorlayout.tensor_shape().array();
+  std::vector<int32_t> tensor_shape_test = tensorlayout.tensor_shape().array();
   ASSERT_EQ(array, tensor_shape_test);
 }
 

tests/ut/cpp/parallel/strategy_test.cc

@@ -33,9 +33,9 @@ class TestStrategy : public UT::Common {
 TEST_F(TestStrategy, GetInputNumber) {
   int32_t number = 2;
   int32_t stage = 1;
-  Dimensions dimension1 = {2, 4};
-  Dimensions dimension2 = {2, 2};
-  Strategys inputs = {dimension1, dimension2};
+  std::vector<int32_t> dimension1 = {2, 4};
+  std::vector<int32_t> dimension2 = {2, 2};
+  std::vector<std::vector<int32_t>> inputs = {dimension1, dimension2};
 
   Strategy strategy(stage, inputs);
   int32_t number_test = strategy.GetInputNumber();
@@ -44,9 +44,9 @@ TEST_F(TestStrategy, GetInputNumber) {
 
 TEST_F(TestStrategy, GetInputStage) {
   int32_t stage = 1;
-  Dimensions dimension1 = {2, 4};
-  Dimensions dimension2 = {2, 2};
-  Strategys inputs = {dimension1, dimension2};
+  std::vector<int32_t> dimension1 = {2, 4};
+  std::vector<int32_t> dimension2 = {2, 2};
+  std::vector<std::vector<int32_t>> inputs = {dimension1, dimension2};
 
   Strategy strategy(stage, inputs);
   int32_t stage_test = strategy.GetInputStage();
@@ -55,23 +55,23 @@ TEST_F(TestStrategy, GetInputStage) {
 
 TEST_F(TestStrategy, GetInputDim) {
   int32_t stage = 1;
-  Dimensions dimension1 = {2, 4};
-  Dimensions dimension2 = {2, 2};
-  Strategys inputs = {dimension1, dimension2};
+  std::vector<int32_t> dimension1 = {2, 4};
+  std::vector<int32_t> dimension2 = {2, 2};
+  std::vector<std::vector<int32_t>> inputs = {dimension1, dimension2};
 
   Strategy strategy(stage, inputs);
-  Strategys inputs_test = strategy.GetInputDim();
+  std::vector<std::vector<int32_t>> inputs_test = strategy.GetInputDim();
   ASSERT_EQ(inputs, inputs_test);
 }
 
 TEST_F(TestStrategy, IsEqual) {
   int32_t stage1 = 0, stage2 = 0, stage3 = 1, stage4 = 0;
-  Dimensions dimension1 = {8, 1};
-  Dimensions dimension2 = {1, 8};
-  Strategys inputs1 = {dimension1};
-  Strategys inputs2 = {dimension1};
-  Strategys inputs3 = {dimension2};
-  Strategys inputs4 = {dimension1, dimension2};
+  std::vector<int32_t> dimension1 = {8, 1};
+  std::vector<int32_t> dimension2 = {1, 8};
+  std::vector<std::vector<int32_t>> inputs1 = {dimension1};
+  std::vector<std::vector<int32_t>> inputs2 = {dimension1};
+  std::vector<std::vector<int32_t>> inputs3 = {dimension2};
+  std::vector<std::vector<int32_t>> inputs4 = {dimension1, dimension2};
 
   StrategyPtr stra1 = std::make_shared<Strategy>(stage1, inputs1);
   StrategyPtr stra2 = std::make_shared<Strategy>(stage2, inputs2);

tests/ut/cpp/parallel/tensor_layout/construct_operator_test.cc

@@ -39,12 +39,12 @@ class TestConstructOperator : public UT::Common {
 };
 
 void TestConstructOperator::SetUp() {
-  RankList dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 1050; i++) {
     dev_list.push_back(i);
   }
-  RankList stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(1024);
   stage_map.push_back(26);
 
@@ -62,7 +62,7 @@ void TestConstructOperator::SetUp() {
 
   MatMulInfoPtr matmul = std::make_shared<MatMulInfo>("matmul_info", inputs_shape_1, outputs_shape_1, attr_1);
 
-  Strategys str = {{2, 4, 8, 16}, {2, 4, 16, 1}};
+  std::vector<Dimensions> str = {{2, 4, 8, 16}, {2, 4, 16, 1}};
   StrategyPtr strategy = NewStrategy(0, str);
   matmul->Init(strategy);
   Shape tensor_shape = {512, 1024};
@@ -79,8 +79,8 @@ TEST_F(TestConstructOperator, TestReshapeOP) {
 
 TEST_F(TestConstructOperator, TestStridedSliceOP) {
   Args args = {1, 2, 3};
-  int64_t split_count = args[0];
-  int64_t split_dim = args[1];
+  int32_t split_count = args[0];
+  int32_t split_dim = args[1];
   Shape device_arrangement = {8, 4};
   Arrangement dev_mat;
   dev_mat.Init(device_arrangement);
@@ -98,18 +98,12 @@ TEST_F(TestConstructOperator, TestStridedSliceOP) {
   OperatorParams params = op.second.second;
   ValuePtr begin_ptr = params[0].first.second;
   ValuePtr end_ptr = params[1].first.second;
-  std::vector<int32_t> begin_int = GetValue<const std::vector<int32_t>>(begin_ptr);
-  std::vector<int32_t> end_int = GetValue<const std::vector<int32_t>>(end_ptr);
-  Shape begin;
-  Shape end;
-  (void)std::transform(begin_int.begin(), begin_int.end(), std::back_inserter(begin),
-                       [](const int32_t &value) { return static_cast<int64_t>(value); });
-  (void)std::transform(end_int.begin(), end_int.end(), std::back_inserter(end),
-                       [](const int32_t &value) { return static_cast<int64_t>(value); });
+  Shape begin = GetValue<const std::vector<int>>(begin_ptr);
+  Shape end = GetValue<const std::vector<int>>(end_ptr);
   for (size_t i = 0; i < begin.size(); i++) {
-    int64_t diff = end[i] - begin[i];
-    int64_t num = shape[i];
-    if (SizeToLong(i) != split_dim) {
+    int32_t diff = end[i] - begin[i];
+    int32_t num = shape[i];
+    if (SizeToInt(i) != split_dim) {
       ASSERT_EQ(diff, shape[i]);
     } else {
       ASSERT_EQ(diff, num / split_count);

tests/ut/cpp/parallel/tensor_layout/redistribution_layout_transfer_test.cc

@@ -20,11 +20,14 @@
 #include "frontend/parallel/tensor_layout/tensor_layout.h"
 #include "frontend/parallel/tensor_layout/redistribution_layout_transfer.h"
 #include "util_layout_gen_test.h"
-#include "frontend/parallel/step_parallel.h"
 
 namespace mindspore {
 namespace parallel {
 
+using DeviceArrangement = std::vector<int32_t>;
+using TensorMap = std::vector<int32_t>;
+using TensorShape = std::vector<int32_t>;
+
 class TestRedistributionLayoutTransfer : public UT::Common {
  public:
   TestRedistributionLayoutTransfer() {}
@@ -242,13 +245,13 @@ void ValidRedistributionLayoutCheck(const DeviceArrangement& in_device_arrangeme
                          unified_out_tensor_map, unified_tensor_shape);
 }
 
-void ValidRedistributionLayoutCheckAll(int64_t device_pow_size, int64_t tensor_pow_size,
-                                       int64_t max_device_dim, int64_t max_shape_dim) {
+void ValidRedistributionLayoutCheckAll(int32_t device_pow_size, int32_t tensor_pow_size,
+                                       int32_t max_device_dim, int32_t max_shape_dim) {
   std::vector<std::tuple<DeviceArrangement, TensorMap, TensorShape>> layout_list;
   GenerateValidLayoutByDeviceSizeAndTensorSize(device_pow_size, tensor_pow_size, max_device_dim, max_shape_dim,
                                                &layout_list);
-  for (size_t in = 0; in < layout_list.size(); in++) {
-    for (size_t out = 0; out < layout_list.size(); out++) {
+  for (uint32_t in = 0; in < layout_list.size(); in++) {
+    for (uint32_t out = 0; out < layout_list.size(); out++) {
       DeviceArrangement in_device_arrangement = std::get<0>(layout_list[in]);
       TensorMap in_tensor_map = std::get<1>(layout_list[in]);
       TensorShape in_tensor_shape = std::get<2>(layout_list[in]);
@@ -270,15 +273,15 @@ void ValidRedistributionLayoutCheckAll(int64_t device_pow_size, int64_t tensor_p
 }
 
 TEST_F(TestRedistributionLayoutTransfer, RedistributionLayoutTransferCheckAll) {
-  int64_t device_pow_size_max = 4;
-  int64_t tensor_pow_size_max = 4;
-  int64_t device_pow_size_min = 1;
-  int64_t tensor_pow_size_min = 1;
-  const int64_t max_device_dim = 5;
-  const int64_t max_shape_dim = 5;
-  int64_t device_pow_size = device_pow_size_min;
+  int32_t device_pow_size_max = 4;
+  int32_t tensor_pow_size_max = 4;
+  int32_t device_pow_size_min = 1;
+  int32_t tensor_pow_size_min = 1;
+  const int32_t max_device_dim = 5;
+  const int32_t max_shape_dim = 5;
+  int32_t device_pow_size = device_pow_size_min;
   while (device_pow_size <= device_pow_size_max) {
-    int64_t tensor_pow_size = tensor_pow_size_min;
+    int32_t tensor_pow_size = tensor_pow_size_min;
     while (tensor_pow_size <= tensor_pow_size_max) {
       ValidRedistributionLayoutCheckAll(device_pow_size, tensor_pow_size, max_device_dim, max_shape_dim);
       tensor_pow_size++;

tests/ut/cpp/parallel/tensor_layout/redistribution_operator_infer_test.cc

@@ -28,13 +28,13 @@ class TestRedistributionOperatorInfer : public UT::Common {
   TestRedistributionOperatorInfer() {}
 
   void SetUp() {
-    RankList dev_list;
+    std::vector<int32_t> dev_list;
 
     for (int32_t i = 0; i < 1050; i++) {
       dev_list.push_back(i);
     }
 
-    RankList stage_map;
+    std::vector<int32_t> stage_map;
     stage_map.push_back(1024);
     stage_map.push_back(26);
 

tests/ut/cpp/parallel/tensor_layout/reshape_layout_transfer_test.cc

@@ -21,11 +21,14 @@
 #include "frontend/parallel/tensor_layout/reshape_layout_transfer.h"
 #include "util_layout_gen_test.h"
 #include "utils/log_adapter.h"
-#include "frontend/parallel/step_parallel.h"
 
 namespace mindspore {
 namespace parallel {
 
+using DeviceArrangement = std::vector<int32_t>;
+using TensorMap = std::vector<int32_t>;
+using TensorShape = std::vector<int32_t>;
+
 class TestReshapeLayoutTransfer : public UT::Common {
  public:
   TestReshapeLayoutTransfer() {}
@@ -257,13 +260,13 @@ TEST_F(TestReshapeLayoutTransfer, ValidInferUnifiedLayoutCheck11) {
   ValidUnifiedLayoutCheck(device_arrangement, in_tensor_map, in_tensor_shape, out_tensor_map, out_tensor_shape);
 }
 
-void ValidInferUnifiedLayoutCheckAll(int64_t device_pow_size, int64_t tensor_pow_size,
-                                     int64_t max_device_dim, int64_t max_shape_dim) {
+void ValidInferUnifiedLayoutCheckAll(int32_t device_pow_size, int32_t tensor_pow_size,
+                                     int32_t max_device_dim, int32_t max_shape_dim) {
   std::vector<std::tuple<DeviceArrangement, TensorMap, TensorShape>> layout_list;
   GenerateValidLayoutByDeviceSizeAndTensorSize(device_pow_size, tensor_pow_size, max_device_dim, max_shape_dim,
                                                &layout_list);
-  for (size_t in = 0; in < layout_list.size(); in++) {
-    for (size_t out = 0; out < layout_list.size(); out++) {
+  for (uint32_t in = 0; in < layout_list.size(); in++) {
+    for (uint32_t out = 0; out < layout_list.size(); out++) {
       DeviceArrangement in_device_arrangement = std::get<0>(layout_list[in]);
       TensorMap in_tensor_map = std::get<1>(layout_list[in]);
       TensorShape in_tensor_shape = std::get<2>(layout_list[in]);
@@ -284,15 +287,15 @@ void ValidInferUnifiedLayoutCheckAll(int64_t device_pow_size, int64_t tensor_pow
 }
 
 TEST_F(TestReshapeLayoutTransfer, ValidInferUnifiedLayoutCheckAll) {
-  int64_t device_pow_size_max = 4;
-  int64_t tensor_pow_size_max = 4;
-  int64_t device_pow_size_min = 1;
-  int64_t tensor_pow_size_min = 1;
-  const int64_t max_device_dim = 5;
-  const int64_t max_shape_dim = 5;
-  int64_t device_pow_size = device_pow_size_min;
+  int32_t device_pow_size_max = 4;
+  int32_t tensor_pow_size_max = 4;
+  int32_t device_pow_size_min = 1;
+  int32_t tensor_pow_size_min = 1;
+  const int32_t max_device_dim = 5;
+  const int32_t max_shape_dim = 5;
+  int32_t device_pow_size = device_pow_size_min;
   while (device_pow_size <= device_pow_size_max) {
-    int64_t tensor_pow_size = tensor_pow_size_min;
+    int32_t tensor_pow_size = tensor_pow_size_min;
     while (tensor_pow_size <= tensor_pow_size_max) {
       ValidInferUnifiedLayoutCheckAll(device_pow_size, tensor_pow_size, max_device_dim, max_shape_dim);
       tensor_pow_size++;
@@ -302,15 +305,15 @@ TEST_F(TestReshapeLayoutTransfer, ValidInferUnifiedLayoutCheckAll) {
 }
 
 TEST_F(TestReshapeLayoutTransfer, ValidInferUnifiedLayoutCheckAll2) {
-  int64_t device_pow_size_max = 1;
-  int64_t tensor_pow_size_max = 2;
-  int64_t device_pow_size_min = 1;
-  int64_t tensor_pow_size_min = 2;
-  const int64_t max_device_dim = 5;
-  const int64_t max_shape_dim = 5;
-  int64_t device_pow_size = device_pow_size_min;
+  int32_t device_pow_size_max = 1;
+  int32_t tensor_pow_size_max = 2;
+  int32_t device_pow_size_min = 1;
+  int32_t tensor_pow_size_min = 2;
+  const int32_t max_device_dim = 5;
+  const int32_t max_shape_dim = 5;
+  int32_t device_pow_size = device_pow_size_min;
   while (device_pow_size <= device_pow_size_max) {
-    int64_t tensor_pow_size = tensor_pow_size_min;
+    int32_t tensor_pow_size = tensor_pow_size_min;
     while (tensor_pow_size <= tensor_pow_size_max) {
       ValidInferUnifiedLayoutCheckAll(device_pow_size, tensor_pow_size, max_device_dim, max_shape_dim);
       tensor_pow_size++;

tests/ut/cpp/parallel/tensor_layout/shape_util_test.cc

@@ -26,7 +26,7 @@ namespace parallel {
  * shape_accum = [2, 2 * 8, 2 * 8 * 32]
  */
 TEST(ShapeUtilTest, ShapeToAccumulateProduct) {
-  Shape shape = {2, 8, 32};
+  std::vector<int32_t> shape = {2, 8, 32};
   std::vector<int64_t> shape_accum;
   Status status = ShapeToAccumulateProduct(shape, &shape_accum);
   ASSERT_EQ(Status::SUCCESS, status);
@@ -39,7 +39,7 @@ TEST(ShapeUtilTest, ShapeToAccumulateProduct) {
  * shape_accum = [2 * 8 * 32, 8 * 32, 32]
  */
 TEST(ShapeUtilTest, ShapeToAccumulateProductReverse) {
-  Shape shape = {2, 8, 32};
+  std::vector<int32_t> shape = {2, 8, 32};
   std::vector<int64_t> shape_accum;
   Status status = ShapeToAccumulateProductReverse(shape, &shape_accum);
   ASSERT_EQ(Status::SUCCESS, status);
@@ -53,10 +53,10 @@ TEST(ShapeUtilTest, ShapeToAccumulateProductReverse) {
  */
 TEST(ShapeUtilTest, AccumulateProductToShape) {
   std::vector<int64_t> shape_accum = {2, 2 * 8, 2 * 8 * 32};
-  Shape shape;
+  std::vector<int32_t> shape;
   Status status = AccumulateProductToShape(shape_accum, &shape);
   ASSERT_EQ(Status::SUCCESS, status);
-  Shape shape_expect = {2, 8, 32};
+  std::vector<int32_t> shape_expect = {2, 8, 32};
   ASSERT_EQ(shape_expect, shape);
 }
 
@@ -66,10 +66,10 @@ TEST(ShapeUtilTest, AccumulateProductToShape) {
  */
 TEST(ShapeUtilTest, AccumulateProductReverseToShape) {
   std::vector<int64_t> shape_accum = {2 * 8 * 32, 8 * 32, 32};
-  Shape shape;
+  std::vector<int32_t> shape;
   Status status = AccumulateProductReverseToShape(shape_accum, &shape);
   ASSERT_EQ(Status::SUCCESS, status);
-  Shape shape_expect = {2, 8, 32};
+  std::vector<int32_t> shape_expect = {2, 8, 32};
   ASSERT_EQ(shape_expect, shape);
 }
 
@@ -94,12 +94,12 @@ TEST(ShapeUtilTest, UnifyAccumulateProduct) {
  * out = [2, 2, 2]
  */
 TEST(ShapeUtilTest, UnifyShape1) {
-  Shape in1 = {2, 4};
-  Shape in2 = {4, 2};
-  Shape out;
+  std::vector<int32_t> in1 = {2, 4};
+  std::vector<int32_t> in2 = {4, 2};
+  std::vector<int32_t> out;
   Status status = UnifyShape(in1, in2, &out);
   ASSERT_EQ(Status::SUCCESS, status);
-  Shape out_expect = {2, 2, 2};
+  std::vector<int32_t> out_expect = {2, 2, 2};
   ASSERT_EQ(out_expect, out);
 }
 
@@ -109,12 +109,12 @@ TEST(ShapeUtilTest, UnifyShape1) {
  * out = [2, 4, 4]
  */
 TEST(ShapeUtilTest, UnifyShape2) {
-  Shape in1 = {8, 4};
-  Shape in2 = {2, 16};
-  Shape out;
+  std::vector<int32_t> in1 = {8, 4};
+  std::vector<int32_t> in2 = {2, 16};
+  std::vector<int32_t> out;
   Status status = UnifyShape(in1, in2, &out);
   ASSERT_EQ(Status::SUCCESS, status);
-  Shape out_expect = {2, 4, 4};
+  std::vector<int32_t> out_expect = {2, 4, 4};
   ASSERT_EQ(out_expect, out);
 }
 
@@ -184,12 +184,12 @@ TEST(ShapeUtilTest, ExpandAccumulateProduct4) {
  * out = [2, 8, 4, 8]
  */
 TEST(ShapeUtilTest, ExpandShape1) {
-  Shape in = {2, 8, 32};
-  Shape expand = {16, 4, 8};
-  Shape out;
+  std::vector<int32_t> in = {2, 8, 32};
+  std::vector<int32_t> expand = {16, 4, 8};
+  std::vector<int32_t> out;
   Status status = ExpandShape(in, expand, &out);
   ASSERT_EQ(Status::SUCCESS, status);
-  Shape out_expect = {2, 8, 4, 8};
+  std::vector<int32_t> out_expect = {2, 8, 4, 8};
   ASSERT_EQ(out_expect, out);
 }
 
@@ -199,12 +199,12 @@ TEST(ShapeUtilTest, ExpandShape1) {
  * out = [2, 8, 4, 8]
  */
 TEST(ShapeUtilTest, ExpandShape2) {
-  Shape in = {2, 8, 32};
-  Shape expand = {2, 4, 8};
-  Shape out;
+  std::vector<int32_t> in = {2, 8, 32};
+  std::vector<int32_t> expand = {2, 4, 8};
+  std::vector<int32_t> out;
   Status status = ExpandShape(in, expand, &out);
   ASSERT_EQ(Status::SUCCESS, status);
-  Shape out_expect = {2, 4, 2, 4, 8};
+  std::vector<int32_t> out_expect = {2, 4, 2, 4, 8};
   ASSERT_EQ(out_expect, out);
 }
 

tests/ut/cpp/parallel/tensor_layout/tensor_layout_test.cc

@@ -18,7 +18,6 @@
 #include "common/common_test.h"
 #include "common/py_func_graph_fetcher.h"
 #include "frontend/parallel/tensor_layout/tensor_layout.h"
-#include "frontend/parallel/step_parallel.h"
 
 namespace mindspore {
 namespace parallel {
@@ -32,12 +31,12 @@ class TestTensorLayout : public UT::Common {
   virtual void TearDown() {}
 };
 
-void ReshapeExpandDeviceArrangementTestFunction(const DeviceArrangement& in_device_arrangement_shape,
-                                                const TensorMap& in_tensor_map_shape,
-                                                const TensorShape& in_tensor_shape_shape,
-                                                const DeviceArrangement& out_device_arrangement_shape,
-                                                const TensorMap& out_tensor_map_shape,
-                                                const TensorShape& out_tensor_shape_shape) {
+void ReshapeExpandDeviceArrangementTestFunction(const std::vector<int32_t>& in_device_arrangement_shape,
+                                                const std::vector<int32_t>& in_tensor_map_shape,
+                                                const std::vector<int32_t>& in_tensor_shape_shape,
+                                                const std::vector<int32_t>& out_device_arrangement_shape,
+                                                const std::vector<int32_t>& out_tensor_map_shape,
+                                                const std::vector<int32_t>& out_tensor_shape_shape) {
   Arrangement device_arrangement;
   Status status = device_arrangement.Init(in_device_arrangement_shape);
   ASSERT_EQ(Status::SUCCESS, status);
@@ -71,12 +70,12 @@ void ReshapeExpandDeviceArrangementTestFunction(const DeviceArrangement& in_devi
  *
  */
 TEST_F(TestTensorLayout, ReshapeExpandDeviceArrangement1) {
-  DeviceArrangement device_arrangement = {8, 4};
-  TensorMap tensor_map = {1, 0};
-  TensorShape tensor_shape = {512, 1024};
-  DeviceArrangement device_arrangement_new = {4, 2, 2, 2};
-  TensorMap tensor_map_expect = {3, 2, 1, 0};
-  TensorShape tensor_shape_expect = {4, 128, 2, 512};
+  std::vector<int32_t> device_arrangement = {8, 4};
+  std::vector<int32_t> tensor_map = {1, 0};
+  std::vector<int32_t> tensor_shape = {512, 1024};
+  std::vector<int32_t> device_arrangement_new = {4, 2, 2, 2};
+  std::vector<int32_t> tensor_map_expect = {3, 2, 1, 0};
+  std::vector<int32_t> tensor_shape_expect = {4, 128, 2, 512};
   ReshapeExpandDeviceArrangementTestFunction(device_arrangement, tensor_map, tensor_shape, device_arrangement_new,
                                              tensor_map_expect, tensor_shape_expect);
 }
@@ -92,12 +91,12 @@ TEST_F(TestTensorLayout, ReshapeExpandDeviceArrangement1) {
  *    out_tensor_shape = [2, 256, 4, 256]
  */
 TEST_F(TestTensorLayout, ReshapeExpandDeviceArrangement2) {
-  DeviceArrangement device_arrangement = {8, 4};
-  TensorMap tensor_map = {0, 1};
-  TensorShape tensor_shape = {512, 1024};
-  DeviceArrangement device_arrangement_new = {4, 2, 2, 2};
-  TensorMap tensor_map_expect = {1, 0, 3, 2};
-  TensorShape tensor_shape_expect = {2, 256, 4, 256};
+  std::vector<int32_t> device_arrangement = {8, 4};
+  std::vector<int32_t> tensor_map = {0, 1};
+  std::vector<int32_t> tensor_shape = {512, 1024};
+  std::vector<int32_t> device_arrangement_new = {4, 2, 2, 2};
+  std::vector<int32_t> tensor_map_expect = {1, 0, 3, 2};
+  std::vector<int32_t> tensor_shape_expect = {2, 256, 4, 256};
   ReshapeExpandDeviceArrangementTestFunction(device_arrangement, tensor_map, tensor_shape, device_arrangement_new,
                                              tensor_map_expect, tensor_shape_expect);
 }
@@ -112,12 +111,12 @@ TEST_F(TestTensorLayout, ReshapeExpandDeviceArrangement2) {
  *    out_tensor_shape = [4, 128, 1024]
  */
 TEST_F(TestTensorLayout, ReshapeExpandDeviceArrangement3) {
-  DeviceArrangement device_arrangement = {8, 4};
-  TensorMap tensor_map = {1, -1};
-  TensorShape tensor_shape = {512, 1024};
-  DeviceArrangement device_arrangement_new = {4, 2, 2, 2};
-  TensorMap tensor_map_expect = {3, 2, -1};
-  TensorShape tensor_shape_expect = {4, 128, 1024};
+  std::vector<int32_t> device_arrangement = {8, 4};
+  std::vector<int32_t> tensor_map = {1, -1};
+  std::vector<int32_t> tensor_shape = {512, 1024};
+  std::vector<int32_t> device_arrangement_new = {4, 2, 2, 2};
+  std::vector<int32_t> tensor_map_expect = {3, 2, -1};
+  std::vector<int32_t> tensor_shape_expect = {4, 128, 1024};
   ReshapeExpandDeviceArrangementTestFunction(device_arrangement, tensor_map, tensor_shape, device_arrangement_new,
                                              tensor_map_expect, tensor_shape_expect);
 }
@@ -133,33 +132,33 @@ TEST_F(TestTensorLayout, ReshapeExpandDeviceArrangement3) {
  *    out_tensor_shape = [512, 4, 256]
  */
 TEST_F(TestTensorLayout, ReshapeExpandDeviceArrangement4) {
-  DeviceArrangement device_arrangement = {8, 4};
-  TensorMap tensor_map = {0, 1};
-  TensorShape tensor_shape = {512, 1024};
-  DeviceArrangement device_arrangement_new = {4, 2, 4};
-  TensorMap tensor_map_expect = {0, 2, 1};
-  TensorShape tensor_shape_expect = {512, 4, 256};
+  std::vector<int32_t> device_arrangement = {8, 4};
+  std::vector<int32_t> tensor_map = {0, 1};
+  std::vector<int32_t> tensor_shape = {512, 1024};
+  std::vector<int32_t> device_arrangement_new = {4, 2, 4};
+  std::vector<int32_t> tensor_map_expect = {0, 2, 1};
+  std::vector<int32_t> tensor_shape_expect = {512, 4, 256};
   ReshapeExpandDeviceArrangementTestFunction(device_arrangement, tensor_map, tensor_shape, device_arrangement_new,
                                              tensor_map_expect, tensor_shape_expect);
 }
 
 TEST_F(TestTensorLayout, ReshapeExpandDeviceArrangement5) {
-  DeviceArrangement device_arrangement = {8, 4};
-  TensorMap tensor_map = {1, -1, 0};
-  TensorShape tensor_shape = {128, 4, 1024};
-  DeviceArrangement device_arrangement_new = {8, 4};
-  TensorMap tensor_map_expect = {1, -1, 0};
-  TensorShape tensor_shape_expect = {128, 4, 1024};
+  std::vector<int32_t> device_arrangement = {8, 4};
+  std::vector<int32_t> tensor_map = {1, -1, 0};
+  std::vector<int32_t> tensor_shape = {128, 4, 1024};
+  std::vector<int32_t> device_arrangement_new = {8, 4};
+  std::vector<int32_t> tensor_map_expect = {1, -1, 0};
+  std::vector<int32_t> tensor_shape_expect = {128, 4, 1024};
   ReshapeExpandDeviceArrangementTestFunction(device_arrangement, tensor_map, tensor_shape, device_arrangement_new,
                                              tensor_map_expect, tensor_shape_expect);
 }
 
-void ExpandTensorShapeTestFunction(const DeviceArrangement& in_device_arrangement_shape,
-                                   const TensorMap& in_tensor_map_shape,
-                                   const TensorShape& in_tensor_shape_shape,
-                                   const DeviceArrangement& out_device_arrangement_shape,
-                                   const TensorMap& out_tensor_map_shape,
-                                   const TensorShape& out_tensor_shape_shape) {
+void ExpandTensorShapeTestFunction(const std::vector<int32_t>& in_device_arrangement_shape,
+                                   const std::vector<int32_t>& in_tensor_map_shape,
+                                   const std::vector<int32_t>& in_tensor_shape_shape,
+                                   const std::vector<int32_t>& out_device_arrangement_shape,
+                                   const std::vector<int32_t>& out_tensor_map_shape,
+                                   const std::vector<int32_t>& out_tensor_shape_shape) {
   Arrangement device_arrangement;
   Status status = device_arrangement.Init(in_device_arrangement_shape);
   ASSERT_EQ(Status::SUCCESS, status);
@@ -194,31 +193,31 @@ void ExpandTensorShapeTestFunction(const DeviceArrangement& in_device_arrangemen
  *    out_tensor_map = [2, 1, 0],
  */
 TEST_F(TestTensorLayout, ExpandTensorShape1) {
-  DeviceArrangement device_arrangement = {8, 4};
-  TensorMap tensor_map = {1, 0};
-  TensorShape tensor_shape = {512, 1024};
-  DeviceArrangement device_arrangement_expect = {4, 2, 4};
-  TensorMap tensor_map_expect = {2, 1, 0};
-  TensorShape tensor_shape_new = {4, 128, 1024};
+  std::vector<int32_t> device_arrangement = {8, 4};
+  std::vector<int32_t> tensor_map = {1, 0};
+  std::vector<int32_t> tensor_shape = {512, 1024};
+  std::vector<int32_t> device_arrangement_expect = {4, 2, 4};
+  std::vector<int32_t> tensor_map_expect = {2, 1, 0};
+  std::vector<int32_t> tensor_shape_new = {4, 128, 1024};
   ExpandTensorShapeTestFunction(device_arrangement, tensor_map, tensor_shape, device_arrangement_expect,
                                 tensor_map_expect, tensor_shape_new);
 }
 
 TEST_F(TestTensorLayout, ExpandTensorShape2) {
-  DeviceArrangement device_arrangement = {8, 4};
-  TensorMap tensor_map = {1, 0};
-  TensorShape tensor_shape = {128, 4096};
-  DeviceArrangement device_arrangement_expect = {8, 4};
-  TensorMap tensor_map_expect = {1, 0, -1};
-  TensorShape tensor_shape_new = {128, 4, 1024};
+  std::vector<int32_t> device_arrangement = {8, 4};
+  std::vector<int32_t> tensor_map = {1, 0};
+  std::vector<int32_t> tensor_shape = {128, 4096};
+  std::vector<int32_t> device_arrangement_expect = {8, 4};
+  std::vector<int32_t> tensor_map_expect = {1, 0, -1};
+  std::vector<int32_t> tensor_shape_new = {128, 4, 1024};
   ExpandTensorShapeTestFunction(device_arrangement, tensor_map, tensor_shape, device_arrangement_expect,
                                 tensor_map_expect, tensor_shape_new);
 }
 
 TEST_F(TestTensorLayout, GetSliceShape) {
-  DeviceArrangement in_device_arrangement = {8, 4};
-  TensorMap in_tensor_map = {1, -1};
-  TensorShape in_tensor_shape = {512, 1024};
+  std::vector<int32_t> in_device_arrangement = {8, 4};
+  std::vector<int32_t> in_tensor_map = {1, -1};
+  std::vector<int32_t> in_tensor_shape = {512, 1024};
   Arrangement device_arrangement;
   device_arrangement.Init(in_device_arrangement);
   Map tensor_map;
@@ -234,9 +233,9 @@ TEST_F(TestTensorLayout, GetSliceShape) {
 }
 
 TEST_F(TestTensorLayout, UpdateTensorMap) {
-  DeviceArrangement in_device_arrangement = {8, 4};
-  TensorMap in_tensor_map = {1, -1};
-  TensorShape in_tensor_shape = {512, 1024};
+  std::vector<int32_t> in_device_arrangement = {8, 4};
+  std::vector<int32_t> in_tensor_map = {1, -1};
+  std::vector<int32_t> in_tensor_shape = {512, 1024};
   Arrangement device_arrangement;
   device_arrangement.Init(in_device_arrangement);
   Map tensor_map;
@@ -251,12 +250,12 @@ TEST_F(TestTensorLayout, UpdateTensorMap) {
   ASSERT_EQ(in_tensor_map, new_tensor_map);
 }
 
-void RemoveElementEqualToOneInDeviceArrangementTestFunction(const DeviceArrangement& in_device_arrangement_shape,
-                                                            const TensorMap& in_tensor_map_shape,
-                                                            const TensorShape& in_tensor_shape_shape,
-                                                            const DeviceArrangement& out_device_arrangement_shape,
-                                                            const TensorMap& out_tensor_map_shape,
-                                                            const TensorShape& out_tensor_shape_shape) {
+void RemoveElementEqualToOneInDeviceArrangementTestFunction(const std::vector<int32_t>& in_device_arrangement_shape,
+                                                            const std::vector<int32_t>& in_tensor_map_shape,
+                                                            const std::vector<int32_t>& in_tensor_shape_shape,
+                                                            const std::vector<int32_t>& out_device_arrangement_shape,
+                                                            const std::vector<int32_t>& out_tensor_map_shape,
+                                                            const std::vector<int32_t>& out_tensor_shape_shape) {
   Arrangement device_arrangement;
   Status status = device_arrangement.Init(in_device_arrangement_shape);
   ASSERT_EQ(Status::SUCCESS, status);
@@ -278,45 +277,45 @@ void RemoveElementEqualToOneInDeviceArrangementTestFunction(const DeviceArrangem
 }
 
 TEST_F(TestTensorLayout, RemoveElementEqualToOneInDeviceArrangement1) {
-  DeviceArrangement device_arrangement = {2, 2, 1};
-  TensorMap tensor_map = {2, 1};
-  TensorShape tensor_shape = {128, 4096};
-  DeviceArrangement device_arrangement_expect = {2, 2};
-  TensorMap tensor_map_expect = {1, 0};
-  TensorShape tensor_shape_new = {128, 4096};
+  std::vector<int32_t> device_arrangement = {2, 2, 1};
+  std::vector<int32_t> tensor_map = {2, 1};
+  std::vector<int32_t> tensor_shape = {128, 4096};
+  std::vector<int32_t> device_arrangement_expect = {2, 2};
+  std::vector<int32_t> tensor_map_expect = {1, 0};
+  std::vector<int32_t> tensor_shape_new = {128, 4096};
   RemoveElementEqualToOneInDeviceArrangementTestFunction(
     device_arrangement, tensor_map, tensor_shape, device_arrangement_expect, tensor_map_expect, tensor_shape_new);
 }
 
 TEST_F(TestTensorLayout, RemoveElementEqualToOneInDeviceArrangement2) {
-  DeviceArrangement device_arrangement = {16, 1, 1};
-  TensorMap tensor_map = {2, 0};
-  TensorShape tensor_shape = {128, 4096};
-  DeviceArrangement device_arrangement_expect = {16};
-  TensorMap tensor_map_expect = {0, -1};
-  TensorShape tensor_shape_new = {128, 4096};
+  std::vector<int32_t> device_arrangement = {16, 1, 1};
+  std::vector<int32_t> tensor_map = {2, 0};
+  std::vector<int32_t> tensor_shape = {128, 4096};
+  std::vector<int32_t> device_arrangement_expect = {16};
+  std::vector<int32_t> tensor_map_expect = {0, -1};
+  std::vector<int32_t> tensor_shape_new = {128, 4096};
   RemoveElementEqualToOneInDeviceArrangementTestFunction(
     device_arrangement, tensor_map, tensor_shape, device_arrangement_expect, tensor_map_expect, tensor_shape_new);
 }
 
 TEST_F(TestTensorLayout, RemoveElementEqualToOneInDeviceArrangement3) {
-  DeviceArrangement device_arrangement = {1, 16, 1};
-  TensorMap tensor_map = {2, 1};
-  TensorShape tensor_shape = {128, 4096};
-  DeviceArrangement device_arrangement_expect = {16};
-  TensorMap tensor_map_expect = {-1, 0};
-  TensorShape tensor_shape_new = {128, 4096};
+  std::vector<int32_t> device_arrangement = {1, 16, 1};
+  std::vector<int32_t> tensor_map = {2, 1};
+  std::vector<int32_t> tensor_shape = {128, 4096};
+  std::vector<int32_t> device_arrangement_expect = {16};
+  std::vector<int32_t> tensor_map_expect = {-1, 0};
+  std::vector<int32_t> tensor_shape_new = {128, 4096};
   RemoveElementEqualToOneInDeviceArrangementTestFunction(
     device_arrangement, tensor_map, tensor_shape, device_arrangement_expect, tensor_map_expect, tensor_shape_new);
 }
 
 TEST_F(TestTensorLayout, RemoveElementEqualToOneInDeviceArrangement4) {
-  DeviceArrangement device_arrangement = {1, 1, 1};
-  TensorMap tensor_map = {2, 1};
-  TensorShape tensor_shape = {128, 4096};
-  DeviceArrangement device_arrangement_expect = {};
-  TensorMap tensor_map_expect = {-1, -1};
-  TensorShape tensor_shape_new = {128, 4096};
+  std::vector<int32_t> device_arrangement = {1, 1, 1};
+  std::vector<int32_t> tensor_map = {2, 1};
+  std::vector<int32_t> tensor_shape = {128, 4096};
+  std::vector<int32_t> device_arrangement_expect = {};
+  std::vector<int32_t> tensor_map_expect = {-1, -1};
+  std::vector<int32_t> tensor_shape_new = {128, 4096};
   RemoveElementEqualToOneInDeviceArrangementTestFunction(
     device_arrangement, tensor_map, tensor_shape, device_arrangement_expect, tensor_map_expect, tensor_shape_new);
 }

tests/ut/cpp/parallel/tensor_layout/tensor_redistribution_test.cc

@@ -18,7 +18,6 @@
 #include "common/common_test.h"
 #include "common/py_func_graph_fetcher.h"
 #include "frontend/parallel/tensor_layout/tensor_redistribution.h"
-#include "frontend/parallel/step_parallel.h"
 
 namespace mindspore {
 namespace parallel {
@@ -34,7 +33,7 @@ class TestTensorRedistribution : public UT::Common {
       dev_list.push_back(i);
     }
 
-    RankList stage_map;
+    std::vector<int32_t> stage_map;
     stage_map.push_back(16);
     stage_map.push_back(4);
 
@@ -50,9 +49,9 @@ class TestTensorRedistribution : public UT::Common {
 
 // Redistribution: Reshape -> SplitByAxis -> ConcatByAxis -> SplitByAxis -> Reshape
 TEST_F(TestTensorRedistribution, TestInferRedistribution1) {
-  DeviceArrangement device_arrangement = {2, 4, 2};
-  TensorMap tensor_map = {2, 0};
-  TensorShape tensor_shape = {512, 1024};
+  std::vector<int32_t> device_arrangement = {2, 4, 2};
+  std::vector<int32_t> tensor_map = {2, 0};
+  std::vector<int32_t> tensor_shape = {512, 1024};
 
   Arrangement in_device_arrangement;
   Status status = in_device_arrangement.Init(device_arrangement);
@@ -103,9 +102,9 @@ TEST_F(TestTensorRedistribution, TestInferRedistribution1) {
 
 // Redistribution: AlltoAll
 TEST_F(TestTensorRedistribution, TestInferRedistribution2) {
-  DeviceArrangement device_arrangement = {16, 1, 1};
-  TensorMap tensor_map = {2, 0};
-  TensorShape tensor_shape = {512, 1024};
+  std::vector<int32_t> device_arrangement = {16, 1, 1};
+  std::vector<int32_t> tensor_map = {2, 0};
+  std::vector<int32_t> tensor_shape = {512, 1024};
 
   Arrangement in_device_arrangement;
   Status status = in_device_arrangement.Init(device_arrangement);
@@ -155,9 +154,9 @@ TEST_F(TestTensorRedistribution, TestInferRedistribution2) {
 
 // Redistribution: Reshape
 TEST_F(TestTensorRedistribution, TestInferRedistribution3) {
-  DeviceArrangement device_arrangement = {8};
-  TensorMap tensor_map = {0, -1, -1, -1};
-  TensorShape tensor_shape = {128, 64, 1, 1};
+  std::vector<int32_t> device_arrangement = {8};
+  std::vector<int32_t> tensor_map = {0, -1, -1, -1};
+  std::vector<int32_t> tensor_shape = {128, 64, 1, 1};
 
   Arrangement in_device_arrangement;
   Status status = in_device_arrangement.Init(device_arrangement);

tests/ut/cpp/parallel/tensor_layout/util_layout_gen_test.cc

@@ -28,21 +28,21 @@ using std::pow;
 
 namespace mindspore {
 namespace parallel {
-std::vector<Shape> combine(const Shape& in, int64_t target) {
-  std::vector<Shape> output;
-  for (int64_t i = 0; i < pow(2, in.size()); i++) {
-    size_t temp = 0;
-    size_t count = 0;
-    Shape left;
-    for (size_t j = 0; j < in.size(); j++) {
+std::vector<std::vector<int32_t>> combine(const std::vector<int32_t>& in, int32_t target) {
+  std::vector<std::vector<int32_t>> output;
+  for (int32_t i = 0; i < pow(2, in.size()); i++) {
+    int32_t temp = 0;
+    int32_t count = 0;
+    std::vector<int32_t> left;
+    for (int32_t j = 0; j < in.size(); j++) {
       if ((i & (1 << j)) != 0) {
         left.push_back(j);
         count++;
       }
     }
     if (count == target) {
-      Shape one_case;
-      for (size_t j = 0; j < count; j++) {
+      std::vector<int32_t> one_case;
+      for (int32_t j = 0; j < count; j++) {
         temp = in.size() - 1 - left[j];
         one_case.push_back(in[temp]);
       }
@@ -54,23 +54,24 @@ std::vector<Shape> combine(const Shape& in, int64_t target) {
   return output;
 }
 
-void GenerateValidShapeBySizeAndDim(int64_t pow_size, int64_t dim, std::vector<Shape>* out) {
+void GenerateValidShapeBySizeAndDim(int32_t pow_size, int32_t dim,
+                                    std::vector<std::vector<int32_t>>* out) {
   out->clear();
-  Shape in;
-  for (int64_t i = 1; i < pow_size; i++) {
+  std::vector<int32_t> in;
+  for (int32_t i = 1; i < pow_size; i++) {
     in.push_back(i);
   }
-  std::vector<Shape> combine_result;
+  std::vector<std::vector<int32_t>> combine_result;
   combine_result = combine(in, dim - 1);
   if (combine_result.size() == 0) {
-    int64_t size = exp2(pow_size);
-    Shape item = {size};
+    int32_t size = exp2(pow_size);
+    std::vector<int32_t> item = {size};
     out->push_back(item);
   }
-  for (size_t i = 0; i < combine_result.size(); i++) {
-    Shape item;
-    int64_t prev = 0;
-    for (int64_t j = combine_result[i].size() - 1; j >= 0; j--) {
+  for (uint32_t i = 0; i < combine_result.size(); i++) {
+    std::vector<int32_t> item;
+    int32_t prev = 0;
+    for (int32_t j = combine_result[i].size() - 1; j >= 0; j--) {
       item.push_back(exp2(combine_result[i][j] - prev));
       prev = combine_result[i][j];
     }
@@ -80,21 +81,22 @@ void GenerateValidShapeBySizeAndDim(int64_t pow_size, int64_t dim, std::vector<S
   return;
 }
 
-void GenerateValidShapeBySize(int64_t pow_size, std::vector<Shape>* out) {
+void GenerateValidShapeBySize(int32_t pow_size, std::vector<std::vector<int32_t>>* out) {
   out->clear();
-  for (int64_t dim = 1; dim <= pow_size; dim++) {
-    std::vector<Shape> combine_result;
+  for (int32_t dim = 1; dim <= pow_size; dim++) {
+    std::vector<std::vector<int32_t>> combine_result;
     GenerateValidShapeBySizeAndDim(pow_size, dim, &combine_result);
-    for (size_t i = 0; i < combine_result.size(); i++) {
+    for (uint32_t i = 0; i < combine_result.size(); i++) {
       out->push_back(combine_result[i]);
     }
   }
   return;
 }
 
-TensorMap GenerateTensorMap(const int64_t& map_size, const Shape& pos_index, const Shape& pos_value) {
-  TensorMap tensor_map(map_size, -1);
-  for (size_t i = 0; i < pos_index.size() && i < pos_value.size(); i++) {
+std::vector<int32_t> GenerateTensorMap(const uint32_t& map_size, const std::vector<int32_t>& pos_index,
+                                       const std::vector<int32_t>& pos_value) {
+  std::vector<int32_t> tensor_map(map_size, -1);
+  for (uint32_t i = 0; i < pos_index.size() && i < pos_value.size(); i++) {
     if (pos_index[i] >= map_size) {
       continue;
     }
@@ -103,43 +105,43 @@ TensorMap GenerateTensorMap(const int64_t& map_size, const Shape& pos_index, con
   return tensor_map;
 }
 
-void GenerateValidTensorMap(const DeviceArrangement& device_arrangement, const TensorShape& tensor_shape,
-                            std::vector<TensorMap>* tensor_map_list) {
+void GenerateValidTensorMap(const std::vector<int32_t>& device_arrangement, const std::vector<int32_t>& tensor_shape,
+                            std::vector<std::vector<int32_t>>* tensor_map_list) {
   tensor_map_list->clear();
-  int64_t device_size = device_arrangement.size();
-  int64_t shape_size = tensor_shape.size();
-  Shape pos_ind_combine_in;
-  for (int64_t i = 0; i < shape_size; i++) {
+  int32_t device_size = device_arrangement.size();
+  int32_t shape_size = tensor_shape.size();
+  std::vector<int32_t> pos_ind_combine_in;
+  for (int32_t i = 0; i < shape_size; i++) {
     pos_ind_combine_in.push_back(i);
   }
-  Shape dev_ind_combine_in;
-  for (int64_t i = 0; i < device_size; i++) {
+  std::vector<int32_t> dev_ind_combine_in;
+  for (int32_t i = 0; i < device_size; i++) {
     dev_ind_combine_in.push_back(i);
   }
-  TensorMap none_map(tensor_shape.size(), -1);
+  std::vector<int32_t> none_map(tensor_shape.size(), -1);
   tensor_map_list->push_back(none_map);
-  for (int64_t pos_num = 1; (pos_num <= shape_size) && (pos_num <= device_size); pos_num++) {
-    std::vector<Shape> pos_index;
+  for (uint32_t pos_num = 1; (pos_num <= shape_size) && (pos_num <= device_size); pos_num++) {
+    std::vector<std::vector<int32_t>> pos_index;
     pos_index = combine(pos_ind_combine_in, pos_num);
-    std::vector<Shape> dev_index;
+    std::vector<std::vector<int32_t>> dev_index;
     dev_index = combine(dev_ind_combine_in, pos_num);
-    for (size_t l = 0; l < dev_index.size(); l++) {
-      Shape pos_value_combine_in;
+    for (int l = 0; l < dev_index.size(); l++) {
+      std::vector<int32_t> pos_value_combine_in;
       for (int32_t i = dev_index[l].size() - 1; i >= 0; i--) {
         pos_value_combine_in.push_back(dev_index[l][i]);
       }
-      std::vector<Shape> pos_value;
-      Shape::iterator it = pos_value_combine_in.begin();
+      std::vector<std::vector<int32_t>> pos_value;
+      std::vector<int32_t>::iterator it = pos_value_combine_in.begin();
       do {
-        Shape pos_value_item;
-        for (size_t m = 0; m < pos_num; m++) {
+        std::vector<int32_t> pos_value_item;
+        for (uint32_t m = 0; m < pos_num; m++) {
           pos_value_item.push_back(pos_value_combine_in[m]);
         }
         pos_value.push_back(pos_value_item);
       } while (next_permutation(it, it + pos_num));
-      for (size_t j = 0; j < pos_index.size(); j++) {
-        for (size_t k = 0; k < pos_value.size(); k++) {
-          TensorMap tensor_map = GenerateTensorMap(shape_size, pos_index[j], pos_value[k]);
+      for (uint32_t j = 0; j < pos_index.size(); j++) {
+        for (uint32_t k = 0; k < pos_value.size(); k++) {
+          std::vector<int32_t> tensor_map = GenerateTensorMap(shape_size, pos_index[j], pos_value[k]);
           tensor_map_list->push_back(tensor_map);
         }
       }
@@ -149,19 +151,19 @@ void GenerateValidTensorMap(const DeviceArrangement& device_arrangement, const T
 }
 
 void GenerateValidLayoutByDeviceSizeAndTensorSize(
-  int64_t device_pow_size, int64_t tensor_pow_size, int64_t max_device_dim,
-  int64_t max_shape_dim,
-  std::vector<std::tuple<DeviceArrangement, TensorMap, TensorShape>>* layout_list) {
+  int32_t device_pow_size, int32_t tensor_pow_size, int32_t max_device_dim,
+  int32_t max_shape_dim,
+  std::vector<std::tuple<std::vector<int32_t>, std::vector<int32_t>, std::vector<int32_t>>>* layout_list) {
   layout_list->clear();
-  std::vector<DeviceArrangement> device_arrangement_list;
+  std::vector<std::vector<int32_t>> device_arrangement_list;
   GenerateValidShapeBySize(device_pow_size, &device_arrangement_list);
-  std::vector<TensorShape> tensor_shape_list;
+  std::vector<std::vector<int32_t>> tensor_shape_list;
   GenerateValidShapeBySize(tensor_pow_size, &tensor_shape_list);
-  for (size_t device_idx = 0; device_idx < device_arrangement_list.size(); device_idx++) {
-    for (size_t shape_idx = 0; shape_idx < tensor_shape_list.size(); shape_idx++) {
-      std::vector<TensorMap> tensor_map_list;
+  for (uint32_t device_idx = 0; device_idx < device_arrangement_list.size(); device_idx++) {
+    for (uint32_t shape_idx = 0; shape_idx < tensor_shape_list.size(); shape_idx++) {
+      std::vector<std::vector<int32_t>> tensor_map_list;
       GenerateValidTensorMap(device_arrangement_list[device_idx], tensor_shape_list[shape_idx], &tensor_map_list);
-      for (size_t map_idx = 0; map_idx < tensor_map_list.size(); map_idx++) {
+      for (uint32_t map_idx = 0; map_idx < tensor_map_list.size(); map_idx++) {
         if (!CheckLayoutValid(device_arrangement_list[device_idx], tensor_map_list[map_idx],
                               tensor_shape_list[shape_idx])) {
           continue;
@@ -174,8 +176,8 @@ void GenerateValidLayoutByDeviceSizeAndTensorSize(
   return;
 }
 
-bool CheckLayoutValid(const DeviceArrangement& device_arrangement, const TensorMap& tensor_map,
-                      const TensorShape& tensor_shape) {
+bool CheckLayoutValid(const std::vector<int32_t>& device_arrangement, const std::vector<int32_t>& tensor_map,
+                      const std::vector<int32_t>& tensor_shape) {
   bool flag = false;
   if ((tensor_map.size() - ComputeNoneNumber(tensor_map)) > device_arrangement.size()) {
     return flag;
@@ -186,9 +188,9 @@ bool CheckLayoutValid(const DeviceArrangement& device_arrangement, const TensorM
   return true;
 }
 
-size_t ComputeNoneNumber(const TensorMap& tensor_map) {
-  size_t num = 0;
-  for (size_t i = 0; i < tensor_map.size(); i++) {
+uint32_t ComputeNoneNumber(const std::vector<int32_t>& tensor_map) {
+  uint32_t num = 0;
+  for (uint32_t i = 0; i < tensor_map.size(); i++) {
     if (tensor_map[i] == -1) {
       num++;
     }
@@ -196,14 +198,14 @@ size_t ComputeNoneNumber(const TensorMap& tensor_map) {
   return num;
 }
 
-bool ShapeIsDividedByDevice(const DeviceArrangement& device_arrangement, const TensorMap& tensor_map,
-                            const TensorShape& tensor_shape) {
+bool ShapeIsDividedByDevice(const std::vector<int32_t>& device_arrangement, const std::vector<int32_t>& tensor_map,
+                            const std::vector<int32_t>& tensor_shape) {
   bool flag = false;
   for (uint32_t i = 0; i < tensor_map.size() && i < tensor_shape.size(); i++) {
     if (tensor_map[i] == -1) {
       continue;
     }
-    int64_t dim = device_arrangement[device_arrangement.size() - 1 - tensor_map[i]];
+    int32_t dim = device_arrangement[device_arrangement.size() - 1 - tensor_map[i]];
     if (tensor_shape[i] % dim != 0) {
       return flag;
     }
@@ -211,8 +213,8 @@ bool ShapeIsDividedByDevice(const DeviceArrangement& device_arrangement, const T
   return true;
 }
 
-bool IsExpended(const Shape& in1, const Shape& in2) {
-  int64_t size = 1;
+bool IsExpended(const std::vector<int32_t>& in1, const std::vector<int32_t>& in2) {
+  int32_t size = 1;
   uint32_t ind = 0;
   for (uint32_t i = 0; i < in1.size(); i++) {
     size *= in1[i];
@@ -234,9 +236,9 @@ bool IsExpended(const Shape& in1, const Shape& in2) {
   return true;
 }
 
-void ComputeAccumDeviceTOAccumShapeMap(const DeviceArrangement& device_arrangement,
-                                       const TensorMap& tensor_map, const TensorShape& tensor_shape,
-                                       std::map<int64_t, int64_t>* accum_device_to_accum_shape_map) {
+void ComputeAccumDeviceTOAccumShapeMap(const std::vector<int32_t>& device_arrangement,
+                                       const std::vector<int32_t>& tensor_map, const std::vector<int32_t>& tensor_shape,
+                                       std::map<int32_t, int32_t>* accum_device_to_accum_shape_map) {
   accum_device_to_accum_shape_map->clear();
   std::vector<int64_t> shape_accum_reverse;
   Status status = ShapeToAccumulateProductReverse(tensor_shape, &shape_accum_reverse);
@@ -256,42 +258,42 @@ void ComputeAccumDeviceTOAccumShapeMap(const DeviceArrangement& device_arrangeme
   return;
 }
 
-void IsLinearValue(int64_t small, int64_t big, int64_t small_value, int64_t big_value, int64_t middle,
-                   int64_t middle_value) {
+void IsLinearValue(int32_t small, int32_t big, int32_t small_value, int32_t big_value, int32_t middle,
+                   int32_t middle_value) {
   ASSERT_NE(big, small);
-  int64_t value = (middle - small) * (big_value - small_value) / (big - small) + small_value;
+  int32_t value = (middle - small) * (big_value - small_value) / (big - small) + small_value;
   ASSERT_EQ(middle_value, value);
 }
 
-void LayoutTransferValidLayoutChangeCheck(const DeviceArrangement& in_device_arrangement,
-                                          const TensorMap& in_tensor_map,
-                                          const TensorShape& in_tensor_shape,
-                                          const DeviceArrangement& out_device_arrangement,
-                                          const TensorMap& out_tensor_map,
-                                          const TensorShape& out_tensor_shape) {
+void LayoutTransferValidLayoutChangeCheck(const std::vector<int32_t>& in_device_arrangement,
+                                          const std::vector<int32_t>& in_tensor_map,
+                                          const std::vector<int32_t>& in_tensor_shape,
+                                          const std::vector<int32_t>& out_device_arrangement,
+                                          const std::vector<int32_t>& out_tensor_map,
+                                          const std::vector<int32_t>& out_tensor_shape) {
   bool is_expended = IsExpended(out_device_arrangement, in_device_arrangement);
   ASSERT_EQ(true, is_expended);
   is_expended = IsExpended(out_tensor_shape, in_tensor_shape);
   ASSERT_EQ(true, is_expended);
-  std::map<int64_t, int64_t> out_accum_device_to_accum_shape_map;
+  std::map<int32_t, int32_t> out_accum_device_to_accum_shape_map;
   ComputeAccumDeviceTOAccumShapeMap(out_device_arrangement, out_tensor_map, out_tensor_shape,
                                     &out_accum_device_to_accum_shape_map);
-  std::map<int64_t, int64_t> in_accum_device_to_accum_shape_map;
+  std::map<int32_t, int32_t> in_accum_device_to_accum_shape_map;
   ComputeAccumDeviceTOAccumShapeMap(in_device_arrangement, in_tensor_map, in_tensor_shape,
                                     &in_accum_device_to_accum_shape_map);
-  std::map<int64_t, int64_t>::iterator in_iter = in_accum_device_to_accum_shape_map.begin();
+  std::map<int32_t, int32_t>::iterator in_iter = in_accum_device_to_accum_shape_map.begin();
   while (in_iter != in_accum_device_to_accum_shape_map.end()) {
     if (in_iter->second != out_accum_device_to_accum_shape_map[in_iter->first]) {
       continue;
     }
     in_iter++;
   }
-  std::map<int64_t, int64_t>::iterator out_iter = out_accum_device_to_accum_shape_map.begin();
+  std::map<int32_t, int32_t>::iterator out_iter = out_accum_device_to_accum_shape_map.begin();
   while (out_iter != out_accum_device_to_accum_shape_map.end()) {
     if (out_accum_device_to_accum_shape_map.find(out_iter->first) == out_accum_device_to_accum_shape_map.end()) {
       in_iter = in_accum_device_to_accum_shape_map.begin();
-      int64_t small = 1;
-      int64_t big = 1;
+      int32_t small = 1;
+      int32_t big = 1;
       while (in_iter != in_accum_device_to_accum_shape_map.end()) {
         if (in_iter->first < out_iter->first) {
           small = in_iter->second;
@@ -309,18 +311,18 @@ void LayoutTransferValidLayoutChangeCheck(const DeviceArrangement& in_device_arr
       if (big == 1) {
         ASSERT_EQ(true, false);
       }
-      int64_t small_value = in_accum_device_to_accum_shape_map[small];
-      int64_t big_value = in_accum_device_to_accum_shape_map[big];
+      int32_t small_value = in_accum_device_to_accum_shape_map[small];
+      int32_t big_value = in_accum_device_to_accum_shape_map[big];
       IsLinearValue(small, big, small_value, big_value, out_iter->first, out_iter->second);
     }
     out_iter++;
   }
 }
 
-void ValidLayoutChangeCheck(const DeviceArrangement& in_device_arrangement,
-                            const TensorMap& in_tensor_map, const TensorShape& in_tensor_shape,
-                            const DeviceArrangement& out_device_arrangement,
-                            const TensorMap& out_tensor_map, const TensorShape& out_tensor_shape) {
+void ValidLayoutChangeCheck(const std::vector<int32_t>& in_device_arrangement,
+                            const std::vector<int32_t>& in_tensor_map, const std::vector<int32_t>& in_tensor_shape,
+                            const std::vector<int32_t>& out_device_arrangement,
+                            const std::vector<int32_t>& out_tensor_map, const std::vector<int32_t>& out_tensor_shape) {
   LayoutTransferValidLayoutChangeCheck(in_device_arrangement, in_tensor_map, in_tensor_shape, out_device_arrangement,
                                        out_tensor_map, out_tensor_shape);
 }

tests/ut/cpp/parallel/tensor_layout/util_layout_gen_test.h

@@ -21,50 +21,51 @@
 #include <vector>
 
 #include "frontend/parallel/tensor_layout/tensor_layout.h"
-#include "frontend/parallel/step_parallel.h"
 
 namespace mindspore {
 namespace parallel {
 
-std::vector<Shape> combine(const Shape& in, int64_t target);
+std::vector<std::vector<int32_t>> combine(const std::vector<int32_t>& in, int32_t target);
 
-void GenerateValidShapeBySizeAndDim(int64_t pow_size, int64_t dim, std::vector<Shape>* out);
+void GenerateValidShapeBySizeAndDim(int32_t pow_size, int32_t dim,
+                                    std::vector<std::vector<int32_t>>* out);
 
-void GenerateValidShapeBySize(int64_t pow_size, std::vector<Shape>* out);
+void GenerateValidShapeBySize(int32_t pow_size, std::vector<std::vector<int32_t>>* out);
 
-TensorMap GenerateTensorMap(const int64_t& map_size, const Shape& pos_index, const Shape& pos_value);
+std::vector<int32_t> GenerateTensorMap(const uint32_t& map_size, const std::vector<int32_t>& pos_index,
+                                       const std::vector<int32_t>& pos_value);
 
-void GenerateValidTensorMap(const DeviceArrangement& device_arrangement, const TensorMap& tensor_shape,
-                            std::vector<TensorMap>* tensor_map_list);
+void GenerateValidTensorMap(const std::vector<int32_t>& device_arrangement, const std::vector<int32_t>& tensor_shape,
+                            std::vector<std::vector<int32_t>>* tensor_map_list);
 
 void GenerateValidLayoutByDeviceSizeAndTensorSize(
-  int64_t device_pow_size, int64_t tensor_pow_size, int64_t max_device_dim,
-  int64_t max_shape_dim,
-  std::vector<std::tuple<DeviceArrangement, TensorMap, TensorShape>>* layout_list);
+  int32_t device_pow_size, int32_t tensor_pow_size, int32_t max_device_dim,
+  int32_t max_shape_dim,
+  std::vector<std::tuple<std::vector<int32_t>, std::vector<int32_t>, std::vector<int32_t>>>* layout_list);
 
-size_t ComputeNoneNumber(const TensorMap& tensor_map);
+uint32_t ComputeNoneNumber(const std::vector<int32_t>& tensor_map);
 
-bool ShapeIsDividedByDevice(const DeviceArrangement& device_arrangement, const TensorMap& tensor_map,
-                            const TensorShape& tensor_shape);
+bool ShapeIsDividedByDevice(const std::vector<int32_t>& device_arrangement, const std::vector<int32_t>& tensor_map,
+                            const std::vector<int32_t>& tensor_shape);
 
-bool CheckLayoutValid(const DeviceArrangement& device_arrangement, const TensorMap& tensor_map,
-                      const TensorShape& tensor_shape);
+bool CheckLayoutValid(const std::vector<int32_t>& device_arrangement, const std::vector<int32_t>& tensor_map,
+                      const std::vector<int32_t>& tensor_shape);
 
-void ComputeAccumDeviceTOAccumShapeMap(const DeviceArrangement& device_arrangement,
-                                       const TensorMap& tensor_map, const TensorShape& tensor_shape,
-                                       std::map<int64_t, int64_t>* accum_device_to_accum_shape_map);
+void ComputeAccumDeviceTOAccumShapeMap(const std::vector<int32_t>& device_arrangement,
+                                       const std::vector<int32_t>& tensor_map, const std::vector<int32_t>& tensor_shape,
+                                       std::map<int32_t, int32_t>* accum_device_to_accum_shape_map);
 
-void LayoutTransferValidLayoutChangeCheck(const DeviceArrangement& in_device_arrangement,
-                                          const TensorMap& in_tensor_map,
-                                          const TensorShape& in_tensor_shape,
-                                          const DeviceArrangement& out_device_arrangement,
-                                          const TensorMap& out_tensor_map,
-                                          const TensorShape& out_tensor_shape);
+void LayoutTransferValidLayoutChangeCheck(const std::vector<int32_t>& in_device_arrangement,
+                                          const std::vector<int32_t>& in_tensor_map,
+                                          const std::vector<int32_t>& in_tensor_shape,
+                                          const std::vector<int32_t>& out_device_arrangement,
+                                          const std::vector<int32_t>& out_tensor_map,
+                                          const std::vector<int32_t>& out_tensor_shape);
 
-void ValidLayoutChangeCheck(const DeviceArrangement& in_device_arrangement,
-                            const TensorMap& in_tensor_map, const TensorShape& in_tensor_shape,
-                            const DeviceArrangement& out_device_arrangement,
-                            const TensorMap& out_tensor_map, const TensorShape& out_tensor_shape);
+void ValidLayoutChangeCheck(const std::vector<int32_t>& in_device_arrangement,
+                            const std::vector<int32_t>& in_tensor_map, const std::vector<int32_t>& in_tensor_shape,
+                            const std::vector<int32_t>& out_device_arrangement,
+                            const std::vector<int32_t>& out_tensor_map, const std::vector<int32_t>& out_tensor_shape);
 
 }  // namespace parallel
 }  // namespace mindspore

tests/ut/cpp/parallel/virtual_dataset_test.cc

@@ -37,13 +37,13 @@ class TestVirtualDatasetInfo : public UT::Common {
 };
 
 void TestVirtualDatasetInfo::SetUp() {
-  RankList dev_list;
+  std::vector<int32_t> dev_list;
 
   for (int32_t i = 0; i < 130; i++) {
     dev_list.push_back(i);
   }
 
-  RankList stage_map;
+  std::vector<int32_t> stage_map;
   stage_map.push_back(16);
   stage_map.push_back(114);
 
@@ -62,27 +62,27 @@ void TestVirtualDatasetInfo::SetUp() {
 }
 
 TEST_F(TestVirtualDatasetInfo, InferDevMatrixShape1) {
-  Strategys inputs = {{16, 1}, {16, 1}, {16, 1}};
+  std::vector<Dimensions> inputs = {{16, 1}, {16, 1}, {16, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
   virtual_dataset->Init(strategy);
-  Shape dev_matrix_shape = virtual_dataset->dev_matrix_shape();
+  std::vector<int32_t> dev_matrix_shape = virtual_dataset->dev_matrix_shape();
 
-  Shape expect = {16};
+  std::vector<int32_t> expect = {16};
   ASSERT_EQ(dev_matrix_shape, expect);
 }
 
 TEST_F(TestVirtualDatasetInfo, InferDevMatrixShape2) {
-  Strategys inputs = {{8, 1}, {8, 1}, {8, 1}};
+  std::vector<Dimensions> inputs = {{8, 1}, {8, 1}, {8, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
   virtual_dataset->Init(strategy);
-  Shape dev_matrix_shape = virtual_dataset->dev_matrix_shape();
+  std::vector<int32_t> dev_matrix_shape = virtual_dataset->dev_matrix_shape();
 
-  Shape expect = {8, 2};
+  std::vector<int32_t> expect = {8, 2};
   ASSERT_EQ(dev_matrix_shape, expect);
 }
 
 TEST_F(TestVirtualDatasetInfo, InferSliceShape1) {
-  Strategys str = {{8, 1}, {8, 1}, {8, 1}};
+  std::vector<Dimensions> str = {{8, 1}, {8, 1}, {8, 1}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   virtual_dataset->Init(strategy);
@@ -127,7 +127,7 @@ TEST_F(TestVirtualDatasetInfo, InferSliceShape1) {
 }
 
 TEST_F(TestVirtualDatasetInfo, GetTensorLayout1) {
-  Strategys str = {{8, 1}, {8, 1}, {8, 1}};
+  std::vector<Dimensions> str = {{8, 1}, {8, 1}, {8, 1}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   virtual_dataset->Init(strategy);
@@ -148,7 +148,7 @@ TEST_F(TestVirtualDatasetInfo, GetTensorLayout1) {
 }
 
 TEST_F(TestVirtualDatasetInfo, GetForwardOp1) {
-  Strategys inputs = {{8, 1}, {8, 1}, {8, 1}};
+  std::vector<Dimensions> inputs = {{8, 1}, {8, 1}, {8, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   virtual_dataset->Init(strategy);
@@ -159,7 +159,7 @@ TEST_F(TestVirtualDatasetInfo, GetForwardOp1) {
 }
 
 TEST_F(TestVirtualDatasetInfo, GetMirrorOPs1) {
-  Strategys inputs = {{8, 1}, {8, 1}, {8, 1}};
+  std::vector<Dimensions> inputs = {{8, 1}, {8, 1}, {8, 1}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   virtual_dataset->Init(strategy);