Pre Merge pull request !1763 from 王强/master

ge/CMakeLists.txt

@@ -297,7 +297,9 @@ set(TRAIN_SRC_LIST
     "graph/passes/hccl_continuous_memcpy_pass.cc"
     "graph/passes/identity_pass.cc"
     "graph/passes/ref_identity_delete_op_pass.cc"
+    "graph/passes/infer_base_pass.cc"
     "graph/passes/infershape_pass.cc"
+    "graph/passes/infer_value_range_pass.cc"
     "graph/passes/iterator_op_pass.cc"
     "graph/passes/link_gen_mask_nodes_pass.cc"
     "graph/passes/merge_pass.cc"
@@ -546,7 +548,9 @@ set(INFER_SRC_LIST
     "graph/passes/shape_operate_op_remove_pass.cc"
     "graph/passes/assert_pass.cc"
     "graph/passes/dropout_pass.cc"
+    "graph/passes/infer_base_pass.cc"
     "graph/passes/infershape_pass.cc"
+    "graph/passes/infer_value_range_pass.cc"
     "graph/passes/unused_const_pass.cc"
     "graph/passes/permute_pass.cc"
     "graph/passes/ctrl_edge_transfer_pass.cc"

ge/graph/passes/constant_folding_pass.cc

@@ -20,35 +20,9 @@
 #include "graph/operator_factory.h"
 #include "graph/utils/node_utils.h"
 #include "graph/utils/type_utils.h"
-#include "init/gelib.h"
 
 namespace ge {
 const int64_t kStartCallNum = 1;
-const std::string kKernelLibName = "aicpu_tf_kernel";
-// tf_kernel.json opsFlag config
-const std::string kOpsFlagClose = "0";
-
-Status RunOpKernelWithCheck(NodePtr &node,
-                            const vector<ConstGeTensorPtr> &inputs,
-                            std::vector<GeTensorPtr> &outputs) {
-  std::shared_ptr<GELib> instance_ptr = ge::GELib::GetInstance();
-  if ((instance_ptr == nullptr) || (!instance_ptr->InitFlag())) {
-    GELOGE(GE_CLI_GE_NOT_INITIALIZED, "[Check][Param] GE is not initialized or is finalized.");
-    return UNSUPPORTED;
-  }
-  OpsKernelInfoStorePtr kernel_info = instance_ptr->OpsKernelManagerObj().GetOpsKernelInfoStore(kKernelLibName);
-  if (kernel_info == nullptr) {
-    GELOGE(FAILED, "[Get][OpsKernelInfoStore] %s failed", kKernelLibName.c_str());
-    return UNSUPPORTED;
-  }
-
-  std::string ops_flag;
-  kernel_info->opsFlagCheck(*node, ops_flag);
-  if (ops_flag == kOpsFlagClose) {
-    return UNSUPPORTED;
-  }
-  return FoldingPass::RunOpKernel(node, inputs, outputs);
-}
 
 const map<string, pair<uint64_t, uint64_t>> &ConstantFoldingPass::GetGeConstantFoldingPerfStatistic() const {
   return statistic_of_ge_constant_folding_;
@@ -81,7 +55,7 @@ Status ConstantFoldingPass::Run(ge::NodePtr &node) {
   vector<GeTensorPtr> outputs;
   // Statistic of ge constant folding kernel
   uint64_t start_time = GetCurrentTimestamp();
-  auto ret = RunOpKernelWithCheck(node, inputs, outputs);
+  auto ret = FoldingPass::RunOpKernelWithCheck(node, inputs, outputs);
   if (ret != SUCCESS) {
     auto op_kernel = folding_pass::GetKernelByType(node);
     if (op_kernel == nullptr) {

ge/graph/passes/folding_pass.cc

@@ -29,7 +29,7 @@
 #include "inc/kernel_factory.h"
 #include "graph/debug/ge_attr_define.h"
 #include "ge_local_engine/engine/host_cpu_engine.h"
-
+#include "init/gelib.h"
 
 namespace ge {
 namespace folding_pass {
@@ -59,6 +59,9 @@ bool IsNoNeedConstantFolding(const NodePtr &node) {
 }  // namespace folding_pass
 
 namespace {
+const std::string kKernelLibName = "aicpu_tf_kernel";
+const std::string kOpsFlagClose = "0";
+
 IndexsToAnchors GetIndexAndPeerInDataAnchors(NodePtr &node) {
   IndexsToAnchors indexes_to_anchors;
   for (auto &out_anchor : node->GetAllOutDataAnchors()) {
@@ -129,6 +132,27 @@ Status FoldingPass::RunOpKernel(NodePtr &node,
   return HostCpuEngine::GetInstance().Run(node, inputs, outputs);
 }
 
+Status FoldingPass::RunOpKernelWithCheck(NodePtr &node, const vector<ConstGeTensorPtr> &inputs,
+                                         std::vector<GeTensorPtr> &outputs) {
+  std::shared_ptr<GELib> instance_ptr = ge::GELib::GetInstance();
+  if ((instance_ptr == nullptr) || (!instance_ptr->InitFlag())) {
+    GELOGE(GE_CLI_GE_NOT_INITIALIZED, "[Check][Param] GE is not initialized or is finalized.");
+    return UNSUPPORTED;
+  }
+  OpsKernelInfoStorePtr kernel_info = instance_ptr->OpsKernelManagerObj().GetOpsKernelInfoStore(kKernelLibName);
+  if (kernel_info == nullptr) {
+    GELOGE(FAILED, "[Get][OpsKernelInfoStore] %s failed", kKernelLibName.c_str());
+    return UNSUPPORTED;
+  }
+
+  std::string ops_flag;
+  kernel_info->opsFlagCheck(*node, ops_flag);
+  if (ops_flag == kOpsFlagClose) {
+    return UNSUPPORTED;
+  }
+  return FoldingPass::RunOpKernel(node, inputs, outputs);
+}
+
 Status FoldingPass::Folding(NodePtr &node, vector<GeTensorPtr> &outputs) {
   GE_CHECK_NOTNULL(node);
   GELOGD("begin folding node:%s", node->GetName().c_str());

ge/graph/passes/folding_pass.h

@@ -36,6 +36,9 @@ using IndexsToAnchors = std::map<int, std::vector<InDataAnchorPtr>>;
 class FoldingPass : public BaseNodePass {
  public:
   static Status RunOpKernel(NodePtr &node, const vector<ConstGeTensorPtr> &inputs, vector<GeTensorPtr> &outputs);
+  static Status RunOpKernelWithCheck(NodePtr &node, const vector<ConstGeTensorPtr> &inputs,
+                                     std::vector<GeTensorPtr> &outputs);
+
  protected:
   Status Folding(NodePtr &node, vector<GeTensorPtr> &outputs);
  private:

ge/graph/passes/infer_base_pass.cc

@@ -0,0 +1,676 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "infer_base_pass.h"
+#include "common/ge/ge_util.h"
+#include "common/util/error_manager/error_manager.h"
+#include "framework/common/debug/ge_log.h"
+#include "framework/common/util.h"
+#include "graph/debug/ge_attr_define.h"
+#include "graph/debug/ge_util.h"
+#include "graph/utils/graph_utils.h"
+#include "graph/utils/node_utils.h"
+#include "graph/utils/tensor_utils.h"
+#include "graph/utils/type_utils.h"
+
+namespace ge {
+namespace {
+string Serial(const vector<int64_t> &dims) {
+  string serial_string;
+  serial_string += "[";
+  for (int64_t dim : dims) {
+    serial_string += std::to_string(dim) + " ";
+  }
+  serial_string += "]";
+  return serial_string;
+}
+void SerialShapeRange(const GeTensorDescPtr &desc, std::string &desc_str) {
+  desc_str += "[";
+  std::vector<std::pair<int64_t, int64_t>> shape_range;
+  (void)desc->GetShapeRange(shape_range);
+  for (const auto &pair : shape_range) {
+    desc_str += "{";
+    desc_str += std::to_string(pair.first) + "," + std::to_string(pair.second);
+    desc_str += "},";
+  }
+  desc_str += "]";
+  shape_range.clear();
+  (void)desc->GetOriginShapeRange(shape_range);
+  for (const auto &pair : shape_range) {
+    desc_str += ",{";
+    desc_str += std::to_string(pair.first) + "," + std::to_string(pair.second);
+    desc_str += "},";
+  }
+}
+void SerialValueRange(const GeTensorDescPtr &desc, std::string &desc_str) {
+  desc_str += "[";
+  std::vector<std::pair<int64_t, int64_t>> value_range;
+  (void)desc->GetValueRange(value_range);
+  for (const auto &pair : value_range) {
+    desc_str += "{";
+    desc_str += std::to_string(pair.first) + "," + std::to_string(pair.second);
+    desc_str += "},";
+  }
+  desc_str += "]";
+}
+graphStatus FindSubgraphDataAndNetoutput(const ComputeGraphPtr &sub_graph, NodePtr &netoutput, const ConstNodePtr &node,
+                                         std::vector<std::vector<GeTensorDesc>> &ref_data_tensors) {
+  auto sub_nodes = sub_graph->GetDirectNode();
+  for (size_t i = sub_nodes.size(); i > 0; --i) {
+    auto sub_node = sub_nodes.at(i - 1);
+    if (sub_node->GetType() == NETOUTPUT) {
+      netoutput = sub_node;
+    }
+    if (sub_node->GetType() == DATA) {
+      if (sub_node->GetOpDesc() == nullptr) {
+        return GRAPH_FAILED;
+      }
+
+      int ref_i;
+      if (!AttrUtils::GetInt(sub_node->GetOpDesc(), ATTR_NAME_PARENT_NODE_INDEX, ref_i)) {
+        REPORT_INNER_ERROR("E19999", "subgraph data node[%s] has no parent node!", sub_node->GetName().c_str());
+        GELOGE(GRAPH_FAILED, "[Get][Int] subgraph data node[%s] has no parent node!", sub_node->GetName().c_str());
+        return GRAPH_FAILED;
+      }
+      if (ref_i < 0 || static_cast<uint32_t>(ref_i) >= node->GetAllInDataAnchorsSize()) {
+        REPORT_INNER_ERROR("E19999", "data node[%s]'s ref index[%d] is not in range [0, %u)!",
+                           sub_node->GetName().c_str(), ref_i, node->GetAllInDataAnchorsSize());
+        GELOGE(GRAPH_FAILED, "[Check][Param] data node[%s]'s ref index[%d] is not in range [0, %u)!",
+               sub_node->GetName().c_str(), ref_i, node->GetAllInDataAnchorsSize());
+        return GRAPH_FAILED;
+      }
+      ref_data_tensors[ref_i].emplace_back(sub_node->GetOpDesc()->GetOutputDesc(0));
+    }
+  }
+  return GRAPH_SUCCESS;
+}
+}  // namespace
+
+Status InferBasePass::Run(NodePtr &node) {
+  GE_CHECK_NOTNULL(node);
+  GE_CHECK_NOTNULL(node->GetOpDesc());
+
+  bool need_infer = NeedInfer(node);
+  if (!need_infer) {
+    GELOGD("Node %s does not need to infer.", node->GetName().c_str());
+    return SUCCESS;
+  }
+
+  std::set<NodePtr> changed_nodes;
+  auto ret = InferAndUpdate(node, !OptionExists(kOptimizeAfterSubGraph), changed_nodes);
+  if (ret != GRAPH_SUCCESS) {
+    (void)AnalyzeFailedInfo(node);
+    return GE_GRAPH_INFERSHAPE_FAILED;
+  }
+
+  /*
+   * we will use changed nodes to do repass for control_ops.
+   * AddChangedNodesImmediateRepass(changed_nodes);
+   */
+  auto status = DoRepassForLoopNode(node);
+  if (status != SUCCESS) {
+    GELOGE(GE_GRAPH_INFERSHAPE_FAILED, "repass failed. node: %s", node->GetName().c_str());
+    return GE_GRAPH_INFERSHAPE_FAILED;
+  }
+  return SUCCESS;
+}
+
+bool InferBasePass::NeedInfer(const NodePtr &node) { return true; }
+void InferBasePass::AnalyzeFailedInfo(const NodePtr &node) { /* Analyze and select failed info*/ }
+Status InferBasePass::DoRepassForLoopNode(NodePtr &node) { return SUCCESS; }
+graphStatus InferBasePass::UpdatePeerInputs(NodePtr &node) { return GRAPH_SUCCESS; }
+void InferBasePass::AddChangedNodesImmediateRepass(std::set<NodePtr> &changed_nodes) {
+  for (const auto &node_ele : changed_nodes) {
+    AddImmediateRePassNode(node_ele);
+  }
+}
+
+graphStatus InferBasePass::InferAndUpdate(NodePtr &node, bool before_subgraph, std::set<NodePtr> &changed_nodes) {
+  auto ret = GRAPH_SUCCESS;
+  bool is_unknown_graph = node->GetOwnerComputeGraph()->GetGraphUnknownFlag();
+  auto opdesc = node->GetOpDesc();
+  // some op can not infershape twice such as aipp
+  bool need_update_input = !is_unknown_graph && !opdesc->HasAttr("has_infered_verified");
+  if (need_update_input) {
+    ret = UpdateCurOpInputDesc(node);
+    if (ret != GRAPH_SUCCESS) {
+      REPORT_CALL_ERROR("E19999", "update op input_desc failed! ret:%d, node:%s", ret, node->GetName().c_str());
+      GELOGE(GRAPH_FAILED, "[Update][OpInputDesc] failed! ret:%d", ret);
+      return ret;
+    }
+  }
+
+  bool contain_subgraph = ContainsSubgraph(node);
+  if (contain_subgraph && before_subgraph) {
+    ret = UpdateTensorDescToSubgraphData(node, changed_nodes);
+    if (ret != GRAPH_SUCCESS) {
+      return ret;
+    }
+  }
+  ret = Infer(node);
+  if (ret != GRAPH_SUCCESS) {
+    return ret;
+  }
+  if (contain_subgraph && !before_subgraph) {
+    ret = UpdateTensorDescToParentNode(node, changed_nodes);
+    if (ret != GRAPH_SUCCESS) {
+      return ret;
+    }
+  }
+
+  ret = UpdatePeerInputs(node);
+  return ret;
+}
+
+graphStatus InferBasePass::UpdateCurOpInputDesc(const NodePtr &node_ptr) {
+  for (const auto &in_anchor : node_ptr->GetAllInDataAnchors()) {
+    auto in_idx = in_anchor->GetIdx();
+    auto peer_out_data_anchor = in_anchor->GetPeerOutAnchor();
+    if (peer_out_data_anchor == nullptr) {
+      continue;
+    }
+    auto peer_out_data_node = peer_out_data_anchor->GetOwnerNode();
+    if (peer_out_data_node == nullptr || peer_out_data_node->GetOpDesc() == nullptr) {
+      continue;
+    }
+    int peer_out_idx = peer_out_data_anchor->GetIdx();
+    auto peer_out_desc = peer_out_data_node->GetOpDesc()->MutableOutputDesc(static_cast<uint32_t>(peer_out_idx));
+
+    // check shape and dtype continuity. do not stop process
+    auto in_desc = node_ptr->GetOpDesc()->MutableInputDesc(static_cast<uint32_t>(in_idx));
+    if (in_desc == nullptr) {
+      continue;
+    }
+    auto in_shape = in_desc->MutableShape().GetDims();
+    auto in_dtype = in_desc->GetDataType();
+    auto peer_out_shape = peer_out_desc->MutableShape().GetDims();
+    auto peer_out_dtype = peer_out_desc->GetDataType();
+    if (peer_out_dtype != in_dtype) {
+      GELOGW(
+        "current node [%s] [%d]\'th in_dtype is [%s].peer output node [%s] [%d]\'th "
+        "output_dtype is [%s].The two dtype should be same! Please check graph and fix it",
+        node_ptr->GetName().c_str(), in_idx, TypeUtils::DataTypeToSerialString(in_dtype).c_str(),
+        peer_out_data_node->GetName().c_str(), peer_out_idx, TypeUtils::DataTypeToSerialString(peer_out_dtype).c_str());
+    } else if ((!in_shape.empty()) && (in_shape != peer_out_shape)) {
+      string in_shape_str = Serial(in_shape);
+      string peer_out_shape_str = Serial(peer_out_shape);
+      GELOGW(
+        "current node [%s] [%d]\'th in_shape is [%s].peer output node [%s] [%d]\'th "
+        "output_shape is [%s].The two shape should be same! Please check graph and fix it",
+        node_ptr->GetName().c_str(), in_idx, in_shape_str.c_str(), peer_out_data_node->GetName().c_str(), peer_out_idx,
+        peer_out_shape_str.c_str());
+    }
+    // refresh current node input desc
+    bool output_changed = false;
+    (void)UpdateInputDescAttr(peer_out_desc, in_desc, output_changed);
+  }
+  return GRAPH_SUCCESS;
+}
+
+graphStatus InferBasePass::UpdateInputDescAttr(const GeTensorDescPtr &src, GeTensorDescPtr &dst, bool &changed) {
+  changed = false;
+  return GRAPH_SUCCESS;
+}
+
+bool InferBasePass::ContainsSubgraph(const NodePtr &node) {
+  auto op_desc = node->GetOpDesc();
+  auto sub_graph_names = op_desc->GetSubgraphInstanceNames();
+  if (sub_graph_names.empty()) {
+    return false;
+  }
+
+  auto root_graph = GraphUtils::FindRootGraph(node->GetOwnerComputeGraph());
+  if (root_graph == nullptr) {
+    return false;
+  }
+  for (const auto &name : sub_graph_names) {
+    if (name.empty()) {
+      continue;
+    }
+    auto sub_graph = root_graph->GetSubgraph(name);
+    if (sub_graph != nullptr) {
+      return true;
+    }
+  }
+  return false;
+}
+
+std::vector<ComputeGraphPtr> InferBasePass::GetCurNodeSubgraphs(const NodePtr &node) {
+  std::vector<ComputeGraphPtr> cur_node_subgraph;
+  auto op_desc = node->GetOpDesc();
+  auto sub_graph_names = op_desc->GetSubgraphInstanceNames();
+  if (sub_graph_names.empty()) {
+    return cur_node_subgraph;
+  }
+
+  auto root_graph = GraphUtils::FindRootGraph(node->GetOwnerComputeGraph());
+  for (const auto &name : sub_graph_names) {
+    if (name.empty()) {
+      GELOGW("The node %s contains empty subgraph instance name", node->GetName().c_str());
+      continue;
+    }
+    auto sub_graph = root_graph->GetSubgraph(name);
+    if (sub_graph == nullptr) {
+      REPORT_INNER_ERROR("E19999", "Can not find the subgrpah %s for node %s", name.c_str(), node->GetName().c_str());
+      GE_LOGE("[Get][Graph] can not find the subgrpah %s for node %s", name.c_str(), node->GetName().c_str());
+      continue;
+    }
+    cur_node_subgraph.emplace_back(sub_graph);
+  }
+  return cur_node_subgraph;
+}
+
+graphStatus InferBasePass::UpdateTensorDescToSubgraphData(NodePtr &node, std::set<NodePtr> &changed_nodes) {
+  // if infer again, update output of while into subgraph data node
+  auto op_desc = node->GetOpDesc();
+  for (const auto &sub_graph : GetCurNodeSubgraphs(node)) {
+    for (const auto &node_sub : sub_graph->GetDirectNode()) {
+      if (node_sub->GetType() != DATA) {
+        continue;
+      }
+      auto name = sub_graph->GetName();
+      int ref_i;
+      auto data_opdesc = node_sub->GetOpDesc();
+      if (data_opdesc == nullptr) {
+        REPORT_INNER_ERROR("E19999", "Invalid data node on the sub graph %s parent node %s, no OpDesc", name.c_str(),
+                           node->GetName().c_str());
+        GE_LOGE("[Get][OpDesc] Invalid data node on the sub graph %s parent node %s, no OpDesc", name.c_str(),
+                node->GetName().c_str());
+        return GRAPH_FAILED;
+      }
+      if (!AttrUtils::GetInt(data_opdesc, ATTR_NAME_PARENT_NODE_INDEX, ref_i)) {
+        REPORT_INNER_ERROR("E19999", "Invalid data node on the sub graph %s parent node %s, no ref-index attribute",
+                           name.c_str(), node->GetName().c_str());
+        GE_LOGE("[Get][Int] Invalid data node on the sub graph %s parent node %s, no ref-index attribute", name.c_str(),
+                node->GetName().c_str());
+        return GRAPH_FAILED;
+      }
+      if (data_opdesc->HasAttr(ATTR_MBATCH_ORIGIN_INPUT_DIMS)) {
+        continue;
+      }
+      auto input_desc = op_desc->MutableInputDesc(ref_i);
+      if (input_desc == nullptr) {
+        REPORT_INNER_ERROR("E19999",
+                           "The ref index(%d) on the data %s on the sub graph %s "
+                           "parent node %s are incompatible, inputs num %u",
+                           ref_i, node_sub->GetName().c_str(), name.c_str(), node->GetName().c_str(),
+                           node->GetAllInDataAnchorsSize());
+        GE_LOGE(
+          "[Call][MutableInputDesc] The ref index(%d) on the data %s on the sub graph %s "
+          "parent node %s are incompatible, inputs num %u",
+          ref_i, node_sub->GetName().c_str(), name.c_str(), node->GetName().c_str(), node->GetAllInDataAnchorsSize());
+        return GRAPH_FAILED;
+      }
+      GELOGI("Ref index is %d, input_desc dtype is %d, node name is %s", ref_i, input_desc->GetDataType(),
+             node->GetName().c_str());
+
+      // if need infer again, refresh subgraph input with output
+      bool is_infer_again = false;
+      AttrUtils::GetBool(node->GetOpDesc(), ATTR_NAME_NEED_INFER_AGAIN, is_infer_again);
+      if (is_infer_again) {
+        input_desc = op_desc->MutableOutputDesc(ref_i);
+        if (input_desc == nullptr) {
+          REPORT_INNER_ERROR("E19999",
+                             "The ref index(%d) on the data %s on the subgraph %s "
+                             "parent node %s are incompatible, outputs num %u.",
+                             ref_i, node_sub->GetName().c_str(), name.c_str(), node->GetName().c_str(),
+                             node->GetAllOutDataAnchorsSize());
+          GELOGE(PARAM_INVALID,
+                 "[Call][MutableOutputDesc] The ref index(%d) on the data %s on the subgraph %s "
+                 "parent node %s are incompatible, outputs num %u.",
+                 ref_i, node_sub->GetName().c_str(), name.c_str(), node->GetName().c_str(),
+                 node->GetAllOutDataAnchorsSize());
+        }
+        GELOGD("Update input desc of data %s on the sub graph %s of node %s,output idx: %d from [%s] to [%s]",
+               node_sub->GetName().c_str(), name.c_str(), node->GetName().c_str(), ref_i,
+               data_opdesc->GetInputDescPtr(0)->GetShape().ToString().c_str(),
+               input_desc->GetShape().ToString().c_str());
+      }
+
+      auto data_input_desc = data_opdesc->MutableInputDesc(0);
+      auto ret = data_opdesc->UpdateInputDesc(0, *input_desc);
+      if (ret != GRAPH_SUCCESS) {
+        REPORT_CALL_ERROR("E19999", "Failed to update input desc of data %s on the sub graph %s parent node %s",
+                          node_sub->GetName().c_str(), name.c_str(), node->GetName().c_str());
+        GE_LOGE("[Update][InputDesc] of data %s on the sub graph %s parent node %s failed", node_sub->GetName().c_str(),
+                name.c_str(), node->GetName().c_str());
+        return ret;
+      }
+      bool input_changed = TensorDescChanged(input_desc, data_input_desc);
+
+      auto data_output_desc = data_opdesc->MutableOutputDesc(0);
+      ret = data_opdesc->UpdateOutputDesc(0, *input_desc);
+      if (ret != GRAPH_SUCCESS) {
+        REPORT_CALL_ERROR("E19999", "Failed to update output desc of data %s on the sub graph %s parent node %s",
+                          node_sub->GetName().c_str(), name.c_str(), node->GetName().c_str());
+        GE_LOGE("[Update][OutputDesc] of data %s on the sub graph %s parent node %s failed",
+                node_sub->GetName().c_str(), name.c_str(), node->GetName().c_str());
+        return ret;
+      }
+      bool output_changed = TensorDescChanged(input_desc, data_output_desc);
+
+      if (input_changed || output_changed) {
+        changed_nodes.insert(node_sub);
+      }
+    }
+  }
+  return GRAPH_SUCCESS;
+}
+
+graphStatus InferBasePass::UpdateTensorDescToParentNode(NodePtr &node, std::set<NodePtr> &changed_nodes) {
+  std::vector<std::vector<GeTensorDesc>> ref_data_tensors(node->GetAllInDataAnchorsSize());
+  std::vector<std::vector<GeTensorDesc>> ref_out_tensors(node->GetAllOutDataAnchorsSize());
+
+  for (const auto &sub_graph : GetCurNodeSubgraphs(node)) {
+    auto name = sub_graph->GetName();
+    NodePtr netoutput = nullptr;
+    auto ret = FindSubgraphDataAndNetoutput(sub_graph, netoutput, node, ref_data_tensors);
+    if (ret != GRAPH_SUCCESS) {
+      return ret;
+    }
+    if (netoutput == nullptr) {
+      REPORT_INNER_ERROR("E19999", "No NetOutput node on sub graph %s, parent node %s", name.c_str(),
+                         node->GetName().c_str());
+      GE_LOGE("[Check][Param] No NetOutput node on sub graph %s, parent node %s", name.c_str(),
+              node->GetName().c_str());
+      return GRAPH_FAILED;
+    }
+    auto netoutput_opdesc = netoutput->GetOpDesc();
+    if (netoutput_opdesc == nullptr) {
+      REPORT_INNER_ERROR("E19999", "Invalid NetOutput node on sub graph %s, parent node %s, no OpDesc on it",
+                         name.c_str(), node->GetName().c_str());
+      GE_LOGE("[Get][OpDesc] Invalid NetOutput node on sub graph %s, parent node %s, no OpDesc on it", name.c_str(),
+              node->GetName().c_str());
+      return GRAPH_FAILED;
+    }
+    for (auto &edge_anchor : netoutput->GetAllInDataAnchors()) {
+      auto edge_desc = netoutput_opdesc->MutableInputDesc(edge_anchor->GetIdx());
+      if (edge_desc == nullptr) {
+        REPORT_INNER_ERROR("E19999",
+                           "Invalid NetOutput node on sub graph %s, parent node %s, "
+                           "can not find input tensor %d",
+                           name.c_str(), node->GetName().c_str(), edge_anchor->GetIdx());
+        GE_LOGE("[Get][Tensor] Invalid NetOutput node on sub graph %s, parent node %s, can not find input tensor %d",
+                name.c_str(), node->GetName().c_str(), edge_anchor->GetIdx());
+        return GRAPH_FAILED;
+      }
+      GELOGI("Netoutput in anchor index is %d, input tensor dim is %zu", edge_anchor->GetIdx(),
+             edge_desc->GetShape().GetDimNum());
+      int ref_i;
+      if (!AttrUtils::GetInt(edge_desc, ATTR_NAME_PARENT_NODE_INDEX, ref_i)) {
+        // if there is no ref index on the TensorDesc, it means the output data will be ignored outer.
+        continue;
+      }
+      GELOGI("Parent node index of edge desc is %d", ref_i);
+      if (ref_i < 0 || static_cast<uint32_t>(ref_i) >= node->GetAllOutDataAnchorsSize()) {
+        return GRAPH_FAILED;
+      }
+      ref_out_tensors[ref_i].emplace_back(*edge_desc);
+    }
+  }
+
+  if (node->GetType() == WHILE) {
+    return UpdateParentNodeForWhile(node, ref_data_tensors, ref_out_tensors, changed_nodes);
+  }
+  return UpdateParentNodeForBranch(node, ref_out_tensors, changed_nodes);
+}
+
+graphStatus InferBasePass::UpdateParentNodeForWhile(NodePtr &node,
+                                                    std::vector<std::vector<GeTensorDesc>> &ref_data_tensors,
+                                                    std::vector<std::vector<GeTensorDesc>> &ref_out_tensors,
+                                                    std::set<NodePtr> &changed_nodes) {
+  GELOGD("Enter update parent node shape for class while op process");
+  if (ref_data_tensors.size() != ref_out_tensors.size()) {
+    REPORT_INNER_ERROR("E19999", "op:%s(%s) input number[%zu] and output number[%zu] is not same!",
+                       node->GetName().c_str(), node->GetType().c_str(), ref_data_tensors.size(),
+                       ref_out_tensors.size());
+    GELOGE(GRAPH_FAILED, "[Check][Param] while op [%s] input number[%zu] and output number[%zu] is not same!",
+           node->GetName().c_str(), ref_data_tensors.size(), ref_out_tensors.size());
+    return GRAPH_FAILED;
+  }
+  for (size_t i = 0; i < ref_data_tensors.size(); i++) {
+    if (ref_out_tensors[i].size() != 1) {
+      REPORT_INNER_ERROR("E19999", "while op, every output should only find one output tensor in all graph!");
+      GELOGE(GRAPH_FAILED, "[Check][Param] while op, every output should only find one output tensor in all graph!");
+      return GRAPH_FAILED;
+    }
+  }
+  bool need_infer_again = false;
+  // check input and output
+  for (size_t i = 0; i < ref_out_tensors.size(); i++) {
+    if (ref_out_tensors[i].empty()) {
+      continue;
+    }
+    auto ref_out_tensor = ref_out_tensors[i].at(0);
+    auto out_shape = ref_out_tensor.MutableShape();
+    vector<std::pair<int64_t, int64_t>> data_shape_range;
+    // ref_i's data and output tensor shape should be same
+    for (auto &tensor : ref_data_tensors[i]) {
+      if (ref_out_tensor.GetDataType() != tensor.GetDataType()) {
+        REPORT_INNER_ERROR("E19999", "node[%s] does not support diff dtype or format among all ref output",
+                           node->GetName().c_str());
+        GELOGE(GRAPH_FAILED, "[Check][Param] node[%s] does not support diff dtype or format output.",
+               node->GetName().c_str());
+        return GRAPH_FAILED;
+      }
+      auto data_shape = tensor.MutableShape();
+      // input is dynamic, here use dim_num
+      if (data_shape.GetDims() != out_shape.GetDims()) {
+        GELOGI("After infer, While %s %zu output shape [%s] is not match with input shape [%s].Need infer again.",
+               node->GetName().c_str(), i, out_shape.ToString().c_str(), data_shape.ToString().c_str());
+        if (data_shape.GetDimNum() != out_shape.GetDimNum()) {
+          ref_out_tensor.SetUnknownDimNumShape();
+        } else {
+          for (size_t j = 0; j < data_shape.GetDimNum(); ++j) {
+            if (data_shape.GetDim(j) != out_shape.GetDim(j)) {
+              if (data_shape.GetDim(j) != UNKNOWN_DIM) {
+                // if input data is fix shape, output is different, need_infer_again
+                need_infer_again = true;
+              }
+              data_shape.SetDim(j, UNKNOWN_DIM);
+            }
+            // set shape rang of while, if dim is unknown ,set shape range as {1,-1}
+            if (data_shape.GetDim(j) == UNKNOWN_DIM) {
+              data_shape_range.emplace_back(std::make_pair(1, UNKNOWN_DIM));
+            } else {
+              data_shape_range.emplace_back(std::make_pair(data_shape.GetDim(j), data_shape.GetDim(j)));
+            }
+          }
+          ref_out_tensor.SetShape(data_shape);
+          ref_out_tensor.SetShapeRange(data_shape_range);
+        }
+      }
+    }
+
+    auto output_desc = node->GetOpDesc()->MutableOutputDesc(i);
+    (void)node->GetOpDesc()->UpdateOutputDesc(i, ref_out_tensor);
+    bool output_changed = TensorDescChanged(ComGraphMakeShared<GeTensorDesc>(ref_out_tensor), output_desc);
+    if (output_changed) {
+      changed_nodes.insert(node);
+    }
+  }
+  AttrUtils::SetBool(node->GetOpDesc(), ATTR_NAME_NEED_INFER_AGAIN, need_infer_again);
+  return GRAPH_SUCCESS;
+}
+
+graphStatus InferBasePass::UpdateOutputForMultiBatch(NodePtr &node,
+                                                     std::vector<std::vector<GeTensorDesc>> &ref_out_tensors,
+                                                     std::set<NodePtr> &changed_nodes) {
+  // check sub_graph shape. Get max for update.
+  for (size_t i = 0; i < ref_out_tensors.size(); ++i) {
+    if (ref_out_tensors[i].empty()) {
+      continue;
+    }
+
+    int64_t max_size = 0;
+    size_t max_shape_index = 0;
+    auto &ref_out_tensor = ref_out_tensors[i].at(0);
+    for (size_t j = 0; j < ref_out_tensors[i].size(); ++j) {
+      auto &tensor = ref_out_tensors[i].at(j);
+      if (ref_out_tensor.GetDataType() != tensor.GetDataType()) {
+        REPORT_INNER_ERROR("E19999", "node[%s] does not support diff dtype among all ref output",
+                           node->GetName().c_str());
+        GELOGE(GRAPH_FAILED, "[Check][Param] node[%s] does not support diff dtype among all ref output",
+               node->GetName().c_str());
+        return GRAPH_FAILED;
+      }
+
+      auto shape = tensor.MutableShape();
+      int64_t size = 1;
+      for (auto dim : shape.GetDims()) {
+        if (dim != 0 && INT64_MAX / dim < size) {
+          REPORT_INNER_ERROR("E19999", "The shape:%s size overflow, node:%s", shape.ToString().c_str(),
+                             node->GetName().c_str());
+          GELOGE(PARAM_INVALID, "[Check][Overflow] The shape size overflow");
+          return PARAM_INVALID;
+        }
+        size *= dim;
+      }
+
+      if (size > max_size) {
+        max_size = size;
+        max_shape_index = j;
+      }
+    }
+
+    auto output_desc = node->GetOpDesc()->MutableOutputDesc(i);
+    (void)node->GetOpDesc()->UpdateOutputDesc(i, ref_out_tensors[i].at(max_shape_index));
+    bool output_changed =
+      TensorDescChanged(ComGraphMakeShared<GeTensorDesc>(ref_out_tensors[i].at(max_shape_index)), output_desc);
+    if (output_changed) {
+      changed_nodes.insert(node);
+    }
+  }
+
+  return GRAPH_SUCCESS;
+}
+
+graphStatus InferBasePass::UpdateParentNodeForBranch(NodePtr &node,
+                                                     std::vector<std::vector<GeTensorDesc>> &ref_out_tensors,
+                                                     std::set<NodePtr> &changed_nodes) {
+  GELOGD("Enter update parent node shape for class branch op process");
+  if (node->GetOpDesc()->HasAttr(ATTR_NAME_BATCH_NUM)) {
+    return UpdateOutputForMultiBatch(node, ref_out_tensors, changed_nodes);
+  }
+
+  // check sub_graph shape.If not same ,do unknown shape process
+  for (size_t i = 0; i < ref_out_tensors.size(); i++) {
+    if (ref_out_tensors[i].empty()) {
+      continue;
+    }
+    auto ref_out_tensor = ref_out_tensors[i].at(0);
+    ge::GeShape &ref_out_tensor_shape = ref_out_tensor.MutableShape();
+    for (auto &tensor : ref_out_tensors[i]) {
+      if (ref_out_tensor.GetDataType() != tensor.GetDataType()) {
+        REPORT_INNER_ERROR("E19999", "node[%s] does not support diff dtype among all ref output, shape:%s",
+                           node->GetName().c_str(), ref_out_tensor_shape.ToString().c_str());
+        GELOGE(GRAPH_FAILED, "[Check][Param] node[%s] does not support diff dtype output", node->GetName().c_str());
+        return GRAPH_FAILED;
+      }
+      auto shape = tensor.MutableShape();
+      if (shape.GetDims().size() != ref_out_tensor_shape.GetDims().size()) {
+        GELOGD("node is %s, i : %zu, shape size: %lu, ref_out_tensor_shape size: %lu", node->GetName().c_str(), i,
+               shape.GetShapeSize(), ref_out_tensor_shape.GetShapeSize());
+        ref_out_tensor_shape = GeShape(UNKNOWN_RANK);
+        break;
+      }
+      for (size_t j = 0; j < ref_out_tensor_shape.GetDims().size(); j++) {
+        if (ref_out_tensor_shape.GetDim(j) == shape.GetDim(j)) {
+          continue;
+        }
+        GELOGD("node is %s, i : %zu, j: %zu ,shape size: %lu, ref_out_tensor_shape size: %lu", node->GetName().c_str(),
+               i, j, shape.GetShapeSize(), ref_out_tensor_shape.GetShapeSize());
+        (void)ref_out_tensor_shape.SetDim(j, UNKNOWN_DIM);
+      }
+    }
+
+    auto output_desc = node->GetOpDesc()->MutableOutputDesc(i);
+    (void)node->GetOpDesc()->UpdateOutputDesc(i, ref_out_tensor);
+    bool output_changed =
+      TensorDescChanged(ComGraphMakeShared<GeTensorDesc>(ref_out_tensor), output_desc);
+    if (output_changed) {
+      changed_nodes.insert(node);
+    }
+  }
+  return GRAPH_SUCCESS;
+}
+
+void InferBasePass::PrintInOutTensorShape(const NodePtr &node, const std::string &phase) {
+  if (!IsLogEnable(GE, DLOG_DEBUG)) {
+    return;
+  }
+  if (node == nullptr) {
+    REPORT_INNER_ERROR("E19999", "param node is nullprt, check invalid");
+    GELOGE(GRAPH_FAILED, "[Check][Param] node is null");
+    return;
+  }
+  ge::OpDescPtr op_desc = node->GetOpDesc();
+  GE_IF_BOOL_EXEC(op_desc == nullptr, REPORT_INNER_ERROR("E19999", "node has no opdesc, check invalid");
+                  GELOGE(GRAPH_FAILED, "[Get][OpDesc] op_desc is null."); return );
+  std::stringstream ss;
+  ss << "{";
+  int32_t in_idx = 0;
+  int32_t out_idx = 0;
+  for (const auto &input_desc : op_desc->GetAllInputsDescPtr()) {
+    if (input_desc == nullptr) {
+      in_idx++;
+      continue;
+    }
+    if (in_idx > 0) {
+      ss << "    ";
+    }
+    ss << "input_" << in_idx << " "
+       << "tensor: [";
+    ss << "(shape:[" << input_desc->MutableShape().ToString() << "]),";
+    ss << "(format:" << TypeUtils::FormatToSerialString(input_desc->GetFormat()) << "),";
+    ss << "(dtype:" << TypeUtils::DataTypeToSerialString(input_desc->GetDataType()) << "),";
+    ss << "(origin_shape:" << input_desc->GetOriginShape().ToString() << "),";
+    ss << "(origin_format:" << TypeUtils::FormatToSerialString(input_desc->GetOriginFormat()) << "),";
+    ss << "(origin_dtype:" << TypeUtils::DataTypeToSerialString(input_desc->GetOriginDataType()) << "),";
+    string range_str;
+    SerialShapeRange(input_desc, range_str);
+    ss << "(shape_range:" << range_str << "),";
+    string value_range_str;
+    SerialValueRange(input_desc, value_range_str);
+    ss << "(value_range:" << value_range_str << ")]";
+    in_idx++;
+  }
+  for (const auto &output_desc : op_desc->GetAllOutputsDescPtr()) {
+    if (output_desc == nullptr) {
+      out_idx++;
+      continue;
+    }
+    ss << "    ";
+    ss << "output_" << out_idx << " "
+       << "tensor: [";
+    ss << "(shape:[" << output_desc->MutableShape().ToString() << "]),";
+    ss << "(format:" << TypeUtils::FormatToSerialString(output_desc->GetFormat()) << "),";
+    ss << "(dtype:" << TypeUtils::DataTypeToSerialString(output_desc->GetDataType()) << "),";
+    ss << "(origin_shape:" << output_desc->GetOriginShape().ToString() << "),";
+    ss << "(origin_format:" << TypeUtils::FormatToSerialString(output_desc->GetOriginFormat()) << "),";
+    ss << "(origin_dtype:" << TypeUtils::DataTypeToSerialString(output_desc->GetOriginDataType()) << "),";
+    string range_str;
+    SerialShapeRange(output_desc, range_str);
+    ss << "(shape_range:" << range_str << "),";
+    string value_range_str;
+    SerialValueRange(output_desc, value_range_str);
+    ss << "(value_range:" << value_range_str << ")]";
+    out_idx++;
+  }
+  ss << "}";
+  GELOGD("Shape dump [%s], Node name: [%s]. %s", phase.c_str(), node->GetName().c_str(), ss.str().c_str());
+}
+}  // namespace ge

ge/graph/passes/infer_base_pass.h

@@ -0,0 +1,53 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef GE_GRAPH_PASSES_INFER_BASE_PASS_H_
+#define GE_GRAPH_PASSES_INFER_BASE_PASS_H_
+
+#include "graph/passes/base_pass.h"
+
+namespace ge {
+class InferBasePass : public BaseNodePass {
+ public:
+  Status Run(NodePtr &node) override;
+  graphStatus InferAndUpdate(NodePtr &node, bool before_subgraph, std::set<NodePtr> &changed_nodes);
+  void PrintInOutTensorShape(const NodePtr &node, const std::string &phase);
+
+ protected:
+  virtual graphStatus Infer(NodePtr &node) = 0;
+  virtual bool TensorDescChanged(const GeTensorDescPtr &src, const GeTensorDescPtr &dst) = 0;
+  virtual graphStatus UpdateInputDescAttr(const GeTensorDescPtr &src, GeTensorDescPtr &dst, bool &changed);
+  virtual bool NeedInfer(const NodePtr &node);
+  virtual void AnalyzeFailedInfo(const NodePtr &node);
+  virtual Status DoRepassForLoopNode(NodePtr &node);   // only for infershape, will be deleted
+  virtual graphStatus UpdatePeerInputs(NodePtr &node); // only for infershape, will be deleted
+
+ private:
+  void AddChangedNodesImmediateRepass(std::set<NodePtr> &changed_nodes);
+  graphStatus UpdateCurOpInputDesc(const NodePtr &node_ptr);
+  bool ContainsSubgraph(const NodePtr &node);
+  std::vector<ComputeGraphPtr> GetCurNodeSubgraphs(const NodePtr &node);
+  graphStatus UpdateTensorDescToSubgraphData(NodePtr &node, std::set<NodePtr> &changed_nodes);
+  graphStatus UpdateTensorDescToParentNode(NodePtr &node, std::set<NodePtr> &changed_nodes);
+  graphStatus UpdateParentNodeForWhile(NodePtr &node, std::vector<std::vector<GeTensorDesc>> &ref_data_tensors,
+                                       std::vector<std::vector<GeTensorDesc>> &ref_out_tensors,
+                                       std::set<NodePtr> &changed_nodes);
+  graphStatus UpdateParentNodeForBranch(NodePtr &node, std::vector<std::vector<GeTensorDesc>> &ref_out_tensors,
+                                        std::set<NodePtr> &changed_nodes);
+  graphStatus UpdateOutputForMultiBatch(NodePtr &node, std::vector<std::vector<GeTensorDesc>> &ref_out_tensors,
+                                        std::set<NodePtr> &changed_nodes);
+};
+}  // namespace ge
+#endif  // GE_GRAPH_PASSES_INFER_BASE_PASS_H_

ge/graph/passes/infer_value_range_pass.cc

@@ -0,0 +1,388 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "graph/passes/infer_value_range_pass.h"
+#include "common/util/error_manager/error_manager.h"
+#include "framework/common/debug/ge_log.h"
+#include "graph/debug/ge_attr_define.h"
+
+#include "graph/operator_factory_impl.h"
+#include "graph/passes/folding_pass.h"
+#include "common/ge/ge_util.h"
+#include "init/gelib.h"
+
+using std::unique_ptr;
+namespace ge {
+namespace {
+#define GET_DATA_BY_DTYPE(DTYPE, TYPE)                                                 \
+  case (DTYPE):                                                                        \
+    ConstructValueRange<TYPE>(lower_tensor, higher_tensor, output_tensor_value_range); \
+    break;
+
+Status RunCpuKernelForValueRange(NodePtr &node, const vector<ConstGeTensorPtr> &inputs,
+                                 std::vector<GeTensorPtr> &outputs) {
+  // should use RunOpKernelWithCheck, RunOpKernel for ut test
+  auto ret = FoldingPass::RunOpKernel(node, inputs, outputs);
+  if (ret != SUCCESS) {
+    auto op_kernel = folding_pass::GetKernelByType(node);
+    if (op_kernel == nullptr) {
+      GELOGE(PARAM_INVALID, "Calculate value range failed, no op kernel for node %s type %s", node->GetName().c_str(),
+             node->GetType().c_str());
+      return PARAM_INVALID;
+    }
+
+    ret = op_kernel->Compute(node->GetOpDesc(), inputs, outputs);
+    if (ret != SUCCESS) {
+      REPORT_INNER_ERROR("E19999", "Calculate for node %s(%s) failed", node->GetName().c_str(),
+                         node->GetType().c_str());
+      GELOGE(INTERNAL_ERROR, "Calculate for node %s failed in constant folding", node->GetName().c_str());
+      return ret;
+    }
+  }
+  GELOGI("Node %s type %s, run cpu kernel success.", node->GetName().c_str(), node->GetType().c_str());
+  return SUCCESS;
+}
+}  // namespace
+
+graphStatus InferValueRangePass::Infer(NodePtr &node) {
+  PrintInOutTensorShape(node, "before_infer_value_range");
+
+  auto infer_value_range_param = OperatorFactoryImpl::GetInferValueRangePara(node->GetType());
+  // Use registered func to calculate value range
+  if (!infer_value_range_param.use_cpu_kernel) {
+    if (infer_value_range_param.infer_value_func == nullptr) {
+      GELOGE(GRAPH_PARAM_INVALID, "The registered func to infer value range is nullptr.");
+      return GRAPH_PARAM_INVALID;
+    }
+    Operator op = OpDescUtils::CreateOperatorFromNode(node);
+    auto ret = node->GetOpDesc()->CallInferValueRangeFunc(op);
+    if (ret != GRAPH_SUCCESS) {
+      REPORT_CALL_ERROR("E19999", "Node %s call infer value range function failed.", node->GetName().c_str());
+      GELOGE(GRAPH_FAILED, "[Call][InferFunction] failed, node: %s.", node->GetName().c_str());
+      return GRAPH_FAILED;
+    }
+    return GRAPH_SUCCESS;
+  }
+
+  // Use CPU kernel func to calculate value range
+  return ConstructInputAndInferValueRange(node);
+}
+
+bool InferValueRangePass::NeedInfer(const NodePtr &node) {
+  auto infer_value_range_param = OperatorFactoryImpl::GetInferValueRangePara(node->GetType());
+  if (!infer_value_range_param.is_initialized) {
+    GELOGD("Node %s does not register func to infer value range, skip infer_value_range_pass.",
+           node->GetName().c_str());
+    return false;
+  }
+
+  if (infer_value_range_param.when_call == INPUT_IS_DYNAMIC) {
+    // Only do infer for node that all inputs are dynamic, such as shape
+    if (InputIsDynamic(node)) {
+      return true;
+    }
+    GELOGD("Node %s register func to infer value range and when_call is INPUT_IS_DYNAMIC, but check input failed.",
+           node->GetName().c_str());
+  } else if (infer_value_range_param.when_call == INPUT_HAS_VALUE_RANGE) {
+    // Only do infer for node that all inputs have value_range or node type of inputs is constant/const
+    if (InputIsConstOrHasValueRange(node)) {
+      return true;
+    }
+    GELOGD("Node %s register func to infer value range and when_call is INPUT_HAS_VALUE_RANGE, but check input failed.",
+           node->GetName().c_str());
+  }
+  GELOGD("Node %s does not need to infer value range, skip infer_value_range_pass.", node->GetName().c_str());
+  return false;
+}
+
+bool InferValueRangePass::TensorDescChanged(const GeTensorDescPtr &src, const GeTensorDescPtr &dst) {
+  bool changed = false;
+  std::vector<std::pair<int64_t, int64_t>> src_value_range;
+  std::vector<std::pair<int64_t, int64_t>> dst_value_range;
+  (void)src->GetValueRange(src_value_range);
+  (void)dst->GetValueRange(dst_value_range);
+  if (src_value_range != dst_value_range) {
+    changed = true;
+  }
+  return changed;
+}
+
+graphStatus InferValueRangePass::UpdateInputDescAttr(const GeTensorDescPtr &src, GeTensorDescPtr &dst, bool &changed) {
+  changed = false;
+  std::vector<std::pair<int64_t, int64_t>> src_value_range;
+  std::vector<std::pair<int64_t, int64_t>> dst_value_range;
+  (void)src->GetValueRange(src_value_range);
+  (void)dst->GetValueRange(dst_value_range);
+  if (src_value_range != dst_value_range) {
+    changed = true;
+  }
+
+  dst->SetValueRange(src_value_range);
+  return GRAPH_SUCCESS;
+}
+
+void InferValueRangePass::AnalyzeFailedInfo(const NodePtr &node) {
+  REPORT_CALL_ERROR("E19999", "Infer value range for node:%s(%s) failed.", node->GetName().c_str(),
+                    node->GetType().c_str());
+  GELOGE(GE_GRAPH_INFERSHAPE_FAILED, "infer value range failed. node: %s", node->GetName().c_str());
+}
+
+bool InferValueRangePass::InputIsDynamic(const NodePtr &node) {
+  bool input_is_dynamic = false;
+  auto cur_op_desc = node->GetOpDesc();
+  for (const auto &input_desc : cur_op_desc->GetAllInputsDescPtr()) {
+    auto dims = input_desc->GetShape().GetDims();
+    for (auto dim : dims) {
+      if (dim == UNKNOWN_DIM || dim == UNKNOWN_DIM_NUM) {
+        input_is_dynamic = true;
+        break;
+      }
+    }
+  }
+  return input_is_dynamic;
+}
+
+bool InferValueRangePass::InputIsConstOrHasValueRange(const NodePtr &node) {
+  bool input_is_const_or_has_value_range = true;
+  auto cur_op_desc = node->GetOpDesc();
+  auto in_data_anchors = node->GetAllInDataAnchors();
+  for (auto i = 0; i < in_data_anchors.size(); ++i) {
+    auto peer_out_anchor = in_data_anchors.at(i)->GetPeerOutAnchor();
+    if (peer_out_anchor == nullptr) {
+      continue;
+    }
+    auto peer_node = peer_out_anchor->GetOwnerNode();
+    if (peer_node == nullptr || peer_node->GetOpDesc() == nullptr) {
+      continue;
+    }
+    if ((peer_node->GetType() == CONSTANT) || (peer_node->GetType() == CONSTANTOP)) {
+      continue;
+    }
+
+    const auto &input_desc = cur_op_desc->GetInputDesc(i);
+    std::vector<std::pair<int64_t, int64_t>> value_range;
+    (void)input_desc.GetValueRange(value_range);
+    if (value_range.empty()) {
+      int peer_out_idx = peer_out_anchor->GetIdx();
+      auto peer_out_desc = peer_node->GetOpDesc()->MutableOutputDesc(static_cast<uint32_t>(peer_out_idx));
+      (void)peer_out_desc->GetValueRange(value_range);
+      if (value_range.empty()) {
+        input_is_const_or_has_value_range = false;
+        break;
+      }
+    }
+  }
+  return input_is_const_or_has_value_range;
+}
+
+template <typename T>
+graphStatus InferValueRangePass::ConstructData(const GeTensorDesc &tensor_desc, bool use_floor_value, GeTensorPtr &output_ptr) {
+  std::vector<std::pair<int64_t, int64_t>> value_range;
+  (void)tensor_desc.GetValueRange(value_range);
+  if (value_range.size() != tensor_desc.GetShape().GetShapeSize()) {
+    REPORT_INNER_ERROR("E19999", "Value range of input %s is invalid.", tensor_desc.GetName().c_str());
+    GELOGE(GRAPH_PARAM_INVALID, "Value range of input %s is invalid.", tensor_desc.GetName().c_str());
+    return GRAPH_PARAM_INVALID;
+  }
+
+  auto value_range_data_num = value_range.size();
+  unique_ptr<T[]> buf(new (std::nothrow) T[value_range_data_num]());
+  if (buf == nullptr) {
+    REPORT_INNER_ERROR("E19999", "New buf failed");
+    GELOGE(MEMALLOC_FAILED, "new buf failed");
+    return GRAPH_FAILED;
+  }
+  for (auto j = 0; j < value_range_data_num; ++j) {
+    auto value_range_j = use_floor_value ? value_range[j].first : value_range[j].second;
+    buf[j] = static_cast<T>(value_range_j);
+  }
+
+  if (output_ptr->SetData(reinterpret_cast<uint8_t *>(buf.get()), value_range_data_num * sizeof(T)) != GRAPH_SUCCESS) {
+    GELOGE(GRAPH_FAILED, "set data failed");
+    return GRAPH_FAILED;
+  }
+  return GRAPH_SUCCESS;
+}
+
+graphStatus InferValueRangePass::ConstructDataByType(const GeTensorDesc &tensor_desc, bool use_floor_value, GeTensorPtr &output_ptr) {
+  graphStatus ret = GRAPH_SUCCESS;
+  auto data_type = tensor_desc.GetDataType();
+  output_ptr->MutableTensorDesc().SetDataType(data_type);
+  switch (data_type) {
+    case DT_FLOAT:
+      ret = ConstructData<float>(tensor_desc, use_floor_value, output_ptr);
+      break;
+    case DT_DOUBLE:
+      ret = ConstructData<double>(tensor_desc, use_floor_value, output_ptr);
+      break;
+    case DT_UINT8:
+      ret = ConstructData<uint8_t>(tensor_desc, use_floor_value, output_ptr);
+      break;
+    case DT_INT8:
+      ret = ConstructData<int8_t>(tensor_desc, use_floor_value, output_ptr);
+      break;
+    case DT_UINT16:
+      ret = ConstructData<uint16_t>(tensor_desc, use_floor_value, output_ptr);
+      break;
+    case DT_INT16:
+      ret = ConstructData<int16_t>(tensor_desc, use_floor_value, output_ptr);
+      break;
+    case DT_INT32:
+      ret = ConstructData<int32_t>(tensor_desc, use_floor_value, output_ptr);
+      break;
+    case DT_INT64:
+      ret = ConstructData<int64_t>(tensor_desc, use_floor_value, output_ptr);
+      break;
+    default:
+      GELOGW("Data type:%s is not supported.", TypeUtils::DataTypeToSerialString(data_type).c_str());
+      ret = GRAPH_FAILED;
+  }
+  return ret;
+}
+
+vector<ConstGeTensorPtr> InferValueRangePass::ConstructInputTensors(const NodePtr &node, bool use_floor_value) {
+  vector<ConstGeTensorPtr> input_tensors;
+  auto cur_op_desc = node->GetOpDesc();
+  auto in_data_anchors = node->GetAllInDataAnchors();
+  for (auto i = 0; i < in_data_anchors.size(); ++i) {
+    auto peer_out_anchor = in_data_anchors.at(i)->GetPeerOutAnchor();
+    if (peer_out_anchor == nullptr) {
+      continue;
+    }
+    auto peer_node = peer_out_anchor->GetOwnerNode();
+    if (peer_node == nullptr) {
+      continue;
+    }
+
+    // construct input tensor by constant node
+    if ((peer_node->GetType() == CONSTANT) || (peer_node->GetType() == CONSTANTOP)) {
+      vector<GeTensorPtr> const_weight = OpDescUtils::MutableWeights(peer_node);
+      if (const_weight.empty()) {
+        REPORT_INNER_ERROR("E19999", "MutableWeights failed, weight is empty, node: %s(%s)",
+                           peer_node->GetName().c_str(), peer_node->GetType().c_str());
+        GELOGE(INTERNAL_ERROR, "MutableWeights failed, weight is empty, node: %s(%s)", peer_node->GetName().c_str(),
+               peer_node->GetType().c_str());
+        return vector<ConstGeTensorPtr>();
+      }
+      // const/constant op has only one weight
+      if (const_weight.at(0) == nullptr) {
+        REPORT_INNER_ERROR("E19999", "MutableWeights failed, weight of constant is null, node: %s(%s)",
+                           peer_node->GetName().c_str(), peer_node->GetType().c_str());
+        GELOGE(INTERNAL_ERROR, "MutableWeights failed, weight of constant is null, node name: %s(%s)",
+               peer_node->GetName().c_str(), peer_node->GetType().c_str());
+        return vector<ConstGeTensorPtr>();
+      }
+      input_tensors.push_back(const_weight.at(0));
+      continue;
+    }
+
+    // construct input tensor by boundary of value range
+    const auto &input_tensor_desc = cur_op_desc->GetInputDesc(i);
+    GeTensorPtr tmp_tensor_ptr = MakeShared<GeTensor>(input_tensor_desc);
+    if (tmp_tensor_ptr == nullptr) {
+      REPORT_INNER_ERROR("E19999", "Make shared failed");
+      GELOGE(MEMALLOC_FAILED, "Make shared failed");
+      return vector<ConstGeTensorPtr>();
+    }
+
+    auto ret = ConstructDataByType(input_tensor_desc, use_floor_value, tmp_tensor_ptr);
+    if (ret != GRAPH_SUCCESS) {
+      REPORT_INNER_ERROR("E19999", "Input %s construct input tensor by boundary of value range failed.",
+                         input_tensor_desc.GetName().c_str());
+      GELOGE(GRAPH_PARAM_INVALID, "Input %s construct input tensor by boundary of value range failed.",
+             input_tensor_desc.GetName().c_str());
+      return vector<ConstGeTensorPtr>();
+    }
+    input_tensors.push_back(tmp_tensor_ptr);
+  }
+
+  return input_tensors;
+}
+
+graphStatus InferValueRangePass::ConstructInputAndInferValueRange(NodePtr &node) {
+  auto inputs = ConstructInputTensors(node, true);
+  if (inputs.empty()) {
+    return GRAPH_PARAM_INVALID;
+  }
+  vector<GeTensorPtr> outputs_lower;
+  auto ret = RunCpuKernelForValueRange(node, inputs, outputs_lower);
+  if (ret != SUCCESS) {
+    REPORT_INNER_ERROR("E19999", "Calculate for node %s(%s) failed", node->GetName().c_str(), node->GetType().c_str());
+    GELOGE(GRAPH_FAILED, "Calculate for node %s failed in constant folding", node->GetName().c_str());
+    return GRAPH_FAILED;
+  }
+
+  inputs = ConstructInputTensors(node, false);
+  if (inputs.empty()) {
+    return GRAPH_PARAM_INVALID;
+  }
+  vector<GeTensorPtr> outputs_higher;
+  ret = RunCpuKernelForValueRange(node, inputs, outputs_higher);
+  if (ret != SUCCESS) {
+    REPORT_INNER_ERROR("E19999", "Calculate for node %s(%s) failed", node->GetName().c_str(), node->GetType().c_str());
+    GELOGE(GRAPH_FAILED, "Calculate for node %s failed in constant folding", node->GetName().c_str());
+    return GRAPH_FAILED;
+  }
+
+  // construct value range from output tensor
+  OpDescPtr node_desc = node->GetOpDesc();
+  std::vector<std::pair<int64_t, int64_t>> output_tensor_value_range;
+  size_t node_output_desc_size = node_desc->GetOutputsSize();
+  for (size_t i = 0; i < node_output_desc_size; ++i) {
+    output_tensor_value_range.clear();
+    auto lower_tensor = outputs_lower[i];
+    auto lower_tensor_shape_size = lower_tensor->GetTensorDesc().GetShape().GetShapeSize();
+    auto higher_tensor = outputs_higher[i];
+    auto higher_tensor_shape_size = higher_tensor->GetTensorDesc().GetShape().GetShapeSize();
+    auto output_tensor_desc = node_desc->MutableOutputDesc(i);
+    auto output_tensor_shape_size = output_tensor_desc->GetShape().GetShapeSize();
+    if (output_tensor_shape_size != lower_tensor_shape_size || output_tensor_shape_size != higher_tensor_shape_size) {
+      GELOGE(GRAPH_PARAM_INVALID, "Value range of output %s is invalid.", output_tensor_desc->GetName().c_str());
+    }
+
+    auto data_type = output_tensor_desc->GetDataType();
+    switch (data_type) {
+      GET_DATA_BY_DTYPE(DT_INT8, int8_t)
+      GET_DATA_BY_DTYPE(DT_INT16, int16_t)
+      GET_DATA_BY_DTYPE(DT_INT32, int32_t)
+      GET_DATA_BY_DTYPE(DT_INT64, int64_t)
+      GET_DATA_BY_DTYPE(DT_UINT8, uint8_t)
+      GET_DATA_BY_DTYPE(DT_UINT16, uint16_t)
+      GET_DATA_BY_DTYPE(DT_UINT32, uint32_t)
+      GET_DATA_BY_DTYPE(DT_UINT64, uint64_t)
+      GET_DATA_BY_DTYPE(DT_FLOAT, float)
+      GET_DATA_BY_DTYPE(DT_DOUBLE, double)
+      default:
+        GELOGW("Data type:%s is not supported.", TypeUtils::DataTypeToSerialString(data_type).c_str());
+        return GRAPH_FAILED;
+    }
+    output_tensor_desc->SetValueRange(output_tensor_value_range);
+  }
+  return GRAPH_SUCCESS;
+}
+
+template <typename T>
+void InferValueRangePass::ConstructValueRange(const GeTensorPtr &left_tensor, const GeTensorPtr &right_tensor,
+                                                     std::vector<std::pair<int64_t, int64_t>> &value_range) {
+  auto x = reinterpret_cast<const T *>(left_tensor->GetData().GetData());
+  auto y = reinterpret_cast<const T *>(right_tensor->GetData().GetData());
+  for (auto j = 0; j < left_tensor->GetTensorDesc().GetShape().GetShapeSize(); ++j) {
+    auto left = static_cast<int64_t>(*(x + j));
+    auto right = static_cast<int64_t>(*(y + j));
+    value_range.emplace_back(std::make_pair(left, right));
+  }
+}
+}  // namespace ge

ge/graph/passes/infer_value_range_pass.h

@@ -0,0 +1,44 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef GE_GRAPH_PASSES_INFER_VALUE_RANGE_PASS_H_
+#define GE_GRAPH_PASSES_INFER_VALUE_RANGE_PASS_H_
+
+#include "graph/passes/infer_base_pass.h"
+
+namespace ge {
+class InferValueRangePass : public InferBasePass {
+ public:
+  graphStatus Infer(NodePtr &node) override;
+  bool TensorDescChanged(const GeTensorDescPtr &src, const GeTensorDescPtr &dst) override;
+  graphStatus UpdateInputDescAttr(const GeTensorDescPtr &src, GeTensorDescPtr &dst, bool &changed) override;
+  bool NeedInfer(const NodePtr &node) override;
+  void AnalyzeFailedInfo(const NodePtr &node) override;
+
+ private:
+  bool InputIsDynamic(const NodePtr &node);
+  bool InputIsConstOrHasValueRange(const NodePtr &node);
+  template <typename T>
+  graphStatus ConstructData(const GeTensorDesc &tensor_desc, bool use_floor_value, GeTensorPtr &output_ptr);
+  graphStatus ConstructDataByType(const GeTensorDesc &tensor_desc, bool use_floor_value, GeTensorPtr &output_ptr);
+  vector<ConstGeTensorPtr> ConstructInputTensors(const NodePtr &node, bool use_floor_value);
+  template <typename T>
+  void ConstructValueRange(const GeTensorPtr &left_tensor, const GeTensorPtr &right_tensor,
+                           std::vector<std::pair<int64_t, int64_t>> &value_range);
+  graphStatus ConstructInputAndInferValueRange(NodePtr &node);
+};
+}  // namespace ge
+#endif  // GE_GRAPH_PASSES_INFER_VALUE_RANGE_PASS_H_

ge/graph/passes/infershape_pass.cc

@@ -19,15 +19,84 @@
 #include "framework/common/debug/ge_log.h"
 #include "analyzer/analyzer.h"
 #include "framework/common/util.h"
-#include "graph/shape_refiner.h"
-#include "graph/utils/graph_utils.h"
-#include "graph/utils/node_utils.h"
 #include "graph/common/omg_util.h"
 #include "graph/debug/ge_attr_define.h"
-#include "utils/tensor_utils.h"
-#include "utils/type_utils.h"
+#include "graph/debug/ge_util.h"
+#include "graph/operator_factory_impl.h"
+#include "graph/utils/graph_utils.h"
+#include "graph/utils/node_utils.h"
+#include "graph/utils/tensor_utils.h"
+#include "graph/utils/type_utils.h"
 
 namespace ge {
+namespace {
+const char *const kPreOpInputShapeRange = "_pre_op_in_range";
+thread_local std::unordered_map<NodePtr, InferenceContextPtr> context_map;
+}
+
+GE_FUNC_DEV_VISIBILITY GE_FUNC_HOST_VISIBILITY void InferShapePass::ClearContextMap() { context_map.clear(); }
+
+InferenceContextPtr CreateInferenceContextPtr(const std::unordered_map<NodePtr, InferenceContextPtr> &context_map,
+                                              const NodePtr &node) {
+  if (node == nullptr) {
+    GELOGE(GRAPH_FAILED, "node is null");
+    return nullptr;
+  }
+  InferenceContextPtr inference_context = std::shared_ptr<InferenceContext>(InferenceContext::Create());
+  if (inference_context == nullptr) {
+    REPORT_CALL_ERROR("E19999", "Failed to alloc InferenceContext, node:%s", node->GetName().c_str());
+    GELOGE(GRAPH_FAILED, "[Alloc][InferenceContext] failed.");
+    return nullptr;
+  }
+
+  auto all_in_data_anchors = node->GetAllInDataAnchors();
+  std::vector<std::vector<ShapeAndType>> input_shapes_and_types(all_in_data_anchors.size());
+  std::vector<std::string> marks;
+
+  bool has_input_shapes_and_types = false;
+  for (const auto &in_anchor : all_in_data_anchors) {
+    const auto &out_anchor = in_anchor->GetPeerOutAnchor();
+    if (out_anchor == nullptr) {
+      continue;
+    }
+
+    auto input_node = out_anchor->GetOwnerNode();
+    if (input_node == nullptr) {
+      continue;
+    }
+
+    auto iter = context_map.find(input_node);
+    if (iter != context_map.end()) {
+      const auto &src_context = iter->second;
+      GE_IF_BOOL_EXEC(src_context == nullptr, REPORT_INNER_ERROR("E19999", "src_context is null.");
+                      GELOGE(GRAPH_FAILED, "[Check][Param] src_context is null."); return nullptr);
+      GELOGD("node:%s get %ld marks from node:%s", node->GetName().c_str(), src_context->GetMarks().size(),
+             input_node->GetName().c_str());
+      for (auto mark : src_context->GetMarks()) {
+        marks.push_back(mark);
+      }
+      auto output_idx = out_anchor->GetIdx();
+      auto input_idx = in_anchor->GetIdx();
+      auto output_shape_and_type = src_context->GetOutputHandleShapesAndTypes();
+      if (output_idx < static_cast<int>(output_shape_and_type.size())) {
+        GELOGI("Add shape and type from %s:%d to %s:%d", input_node->GetName().c_str(), output_idx,
+               node->GetName().c_str(), input_idx);
+        input_shapes_and_types[input_idx] = output_shape_and_type[output_idx];
+        has_input_shapes_and_types = true;
+      } else {
+        GELOGI("[%s] Output out of range. index = %d, size = %zu", node->GetName().c_str(), output_idx,
+               output_shape_and_type.size());
+      }
+    }
+  }
+
+  if (has_input_shapes_and_types) {
+    inference_context->SetInputHandleShapesAndTypes(std::move(input_shapes_and_types));
+  }
+  inference_context->SetMarks(marks);
+
+  return inference_context;
+}
 
 void SerialShapeRange(const GeTensorDescPtr &desc, std::string &desc_str) {
   desc_str += "[";
@@ -61,7 +130,8 @@ std::string GetInTensorInfoWithString(const ge::NodePtr &node) {
     if (in_idx > 0) {
       ss << "    ";
     }
-    ss << "input_" << in_idx << " " << "tensor: [";
+    ss << "input_" << in_idx << " "
+       << "tensor: [";
     ss << "(shape:[" << input_desc->MutableShape().ToString() << "]),";
     ss << "(format:" << TypeUtils::FormatToSerialString(input_desc->GetFormat()) << "),";
     ss << "(dtype:" << TypeUtils::DataTypeToSerialString(input_desc->GetDataType()) << "),";
@@ -76,28 +146,180 @@ std::string GetInTensorInfoWithString(const ge::NodePtr &node) {
   return ss.str();
 }
 
-Status InferShapePass::Run(NodePtr &node) {
-  // kOptimizeAfterSubGraph exist means after subgraph
-  auto ret = ShapeRefiner::InferShapeAndType(node, !OptionExists(kOptimizeAfterSubGraph));
-  if (ret != GRAPH_SUCCESS) {
-    // select INFERSHAPE failed info
-    auto graph = node->GetOwnerComputeGraph();
-    GE_CHECK_NOTNULL(graph);
-    auto root_graph = ge::GraphUtils::FindRootGraph(graph);
-    GE_CHECK_NOTNULL(root_graph);
-    analyzer::DataInfo analyze_info{root_graph->GetSessionID(), root_graph->GetGraphID(),
-                                    analyzer::INFER_SHAPE, node, "InferShapeFailed!"};
-    (void)Analyzer::GetInstance()->DoAnalyze(analyze_info);
-    (void)Analyzer::GetInstance()->SaveAnalyzerDataToFile(root_graph->GetSessionID(),
-                                                          root_graph->GetGraphID());
-
-    REPORT_CALL_ERROR("E19999", "Call InferShapeAndType for node:%s(%s) failed, input_tensor:%s",
-                      node->GetName().c_str(), node->GetType().c_str(), GetInTensorInfoWithString(node).c_str());
-    GELOGE(GE_GRAPH_INFERSHAPE_FAILED, "[Call][InferShapeAndType] for node:%s(%s) failed, input_tensor:%s",
-           node->GetName().c_str(), node->GetType().c_str(), GetInTensorInfoWithString(node).c_str());
-    return GE_GRAPH_INFERSHAPE_FAILED;
+void InferShapePass::AnalyzeFailedInfo(const NodePtr &node) {
+  auto graph = node->GetOwnerComputeGraph();
+  if (graph == nullptr) {
+    GELOGW("Owner compute graph of node %s is nullptr", node->GetName().c_str());
+  }
+  auto root_graph = ge::GraphUtils::FindRootGraph(graph);
+  if (root_graph == nullptr) {
+    GELOGW("Root compute graph of node %s is nullptr", node->GetName().c_str());
+  }
+  analyzer::DataInfo analyze_info{root_graph->GetSessionID(), root_graph->GetGraphID(), analyzer::INFER_SHAPE, node,
+                                  "InferShapeFailed!"};
+  (void)Analyzer::GetInstance()->DoAnalyze(analyze_info);
+  (void)Analyzer::GetInstance()->SaveAnalyzerDataToFile(root_graph->GetSessionID(), root_graph->GetGraphID());
+  REPORT_CALL_ERROR("E19999", "Call InferShapeAndType for node:%s(%s) failed, input_tensor:%s", node->GetName().c_str(),
+                    node->GetType().c_str(), GetInTensorInfoWithString(node).c_str());
+  GELOGE(GE_GRAPH_INFERSHAPE_FAILED, "[Call][InferShapeAndType] for node:%s(%s) failed, input_tensor:%s",
+         node->GetName().c_str(), node->GetType().c_str(), GetInTensorInfoWithString(node).c_str());
+}
+
+bool InferShapePass::TensorDescChanged(const GeTensorDescPtr &src, const GeTensorDescPtr &dst) {
+  bool changed = false;
+  const auto &dst_dims = dst->GetShape().GetDims();
+  const auto &src_dims = src->GetShape().GetDims();
+  if (dst_dims != src_dims) {
+    changed = true;
+  }
+  return changed;
+}
+
+graphStatus InferShapePass::UpdateInputDescAttr(const GeTensorDescPtr &src, GeTensorDescPtr &dst, bool &changed) {
+  dst->SetOriginShape(src->GetOriginShape());
+  dst->SetShape(src->MutableShape());
+  dst->SetDataType(src->GetDataType());
+  dst->SetOriginDataType(src->GetOriginDataType());
+  if (src->MutableShape().GetDims() != UNKNOWN_RANK) {
+    std::vector<std::pair<int64_t, int64_t>> shape_range;
+    (void)src->GetShapeRange(shape_range);
+    dst->SetShapeRange(shape_range);
+  }
+  std::vector<int64_t> pre_op_in_range;
+  if (ge::AttrUtils::GetListInt(*src, kPreOpInputShapeRange, pre_op_in_range)) {
+    (void)ge::AttrUtils::SetListInt(*dst, kPreOpInputShapeRange, pre_op_in_range);
+  }
+  ge::TensorUtils::SetRealDimCnt(*dst, static_cast<uint32_t>(src->MutableShape().GetDims().size()));
+  return GRAPH_SUCCESS;
+}
+
+graphStatus InferShapePass::Infer(NodePtr &node) {
+  bool is_unknown_graph = node->GetOwnerComputeGraph()->GetGraphUnknownFlag();
+  auto opdesc = node->GetOpDesc();
+  if (node->Verify() != GRAPH_SUCCESS) {
+    REPORT_CALL_ERROR("E19999", "Verifying %s failed.", node->GetName().c_str());
+    GELOGE(GRAPH_FAILED, "[Call][Verify] Verifying %s failed.", node->GetName().c_str());
+    return GRAPH_FAILED;
+  }
+  PrintInOutTensorShape(node, "before_infershape");
+  Operator op = OpDescUtils::CreateOperatorFromNode(node);
+
+  if (!is_unknown_graph) {
+    auto inference_context = CreateInferenceContextPtr(context_map, node);
+    GE_CHECK_NOTNULL(inference_context);
+    GELOGD("create context for node:%s, marks %zu", node->GetName().c_str(), inference_context->GetMarks().size());
+    op.SetInferenceContext(inference_context);
+  }
+
+  graphStatus status = CallInferShapeFunc(node, op);
+  if (status != GRAPH_PARAM_INVALID && status != GRAPH_SUCCESS) {
+    REPORT_CALL_ERROR("E19999", "%s call infer function failed.", node->GetName().c_str());
+    GELOGE(GRAPH_FAILED, "[Call][InferFunction] failed, node:%s.", node->GetName().c_str());
+    return GRAPH_FAILED;
   }
+  if (!is_unknown_graph) {
+    auto ctx_after_infer = op.GetInferenceContext();
+    if (ctx_after_infer != nullptr) {
+      GELOGD("[%s] after infershape. mark:%zu", node->GetName().c_str(), ctx_after_infer->GetMarks().size());
+      if (!ctx_after_infer->GetOutputHandleShapesAndTypes().empty() || !ctx_after_infer->GetMarks().empty()) {
+        GELOGD("[%s] set inference context after. mark:%zu", node->GetName().c_str(),
+               ctx_after_infer->GetMarks().size());
+        (void)context_map.emplace(node, ctx_after_infer);
+      }
+    }
+  }
+
+  return GRAPH_SUCCESS;
+}
 
+graphStatus InferShapePass::CallInferShapeFunc(NodePtr &node, Operator &op) {
+  auto op_desc = node->GetOpDesc();
+  const auto &op_type = op_desc->GetType();
+  auto ret = op_desc->CallInferFunc(op);
+  if (ret == GRAPH_PARAM_INVALID) {
+    // Op ir no infer func, try to get infer func from operator factory
+    auto node_op = ge::OperatorFactory::CreateOperator("node_op", op_desc->GetType());
+    if (node_op.IsEmpty()) {
+      GELOGW("get op from OperatorFactory fail. opType: %s", op_type.c_str());
+      return ret;
+    }
+
+    GELOGD("get op from OperatorFactory success. opType: %s", op_type.c_str());
+    auto temp_op_desc = ge::OpDescUtils::GetOpDescFromOperator(node_op);
+    node_op.BreakConnect();
+    if (temp_op_desc == nullptr) {
+      REPORT_CALL_ERROR("E19999", "GetOpDescFromOperator failed, return nullptr.");
+      GELOGE(GRAPH_FAILED, "[Get][OpDesc] temp op desc is null");
+      return GRAPH_FAILED;
+    }
+    if (!op_desc->UpdateInputName(temp_op_desc->GetAllInputName())) {
+      GELOGW("InferShapeAndType UpdateInputName failed");
+      for (const auto &out_desc : op_desc->GetAllOutputsDescPtr()) {
+        if (out_desc != nullptr && out_desc->GetShape().GetDims().empty()) {
+          break;
+        }
+        return GRAPH_SUCCESS;
+      }
+    }
+    if (!op_desc->UpdateOutputName(temp_op_desc->GetAllOutputName())) {
+      GELOGW("InferShapeAndType UpdateOutputName failed");
+    }
+    op_desc->AddInferFunc(temp_op_desc->GetInferFunc());
+    ret = op_desc->CallInferFunc(op);
+    GELOGI("op CallInferFunc second. ret: %u", ret);
+  }
+  return ret;
+}
+
+graphStatus InferShapePass::UpdatePeerInputs(NodePtr &node) {
+  bool is_unknown_graph = node->GetOwnerComputeGraph()->GetGraphUnknownFlag();
+  if (is_unknown_graph) {
+    PrintInOutTensorShape(node, "after_infershape when running");
+    return GRAPH_SUCCESS;
+  }
+  UpdateInputOutputOriginAttr(node);
+  if (NodeUtils::UpdatePeerNodeInputDesc(node) != SUCCESS) {
+    return GRAPH_FAILED;
+  }
+  PrintInOutTensorShape(node, "after_infershape");
+  return GRAPH_SUCCESS;
+}
+
+void InferShapePass::UpdateInputOutputOriginAttr(NodePtr &node) {
+  auto op_desc = node->GetOpDesc();
+  for (const auto &out_anchor : node->GetAllOutDataAnchors()) {
+    auto output_tensor = op_desc->MutableOutputDesc(out_anchor->GetIdx());
+    if (output_tensor == nullptr) {
+      continue;
+    }
+    if (output_tensor->MutableShape().GetDims().empty()) {
+      output_tensor->SetOriginShape(output_tensor->GetShape());
+    }
+    ge::TensorUtils::SetRealDimCnt(*output_tensor,
+                                   static_cast<uint32_t>(output_tensor->GetOriginShape().GetDims().size()));
+    output_tensor->SetOriginDataType(output_tensor->GetDataType());
+    // set output origin shape range
+    std::vector<std::pair<int64_t, int64_t>> range;
+    (void)output_tensor->GetShapeRange(range);
+    output_tensor->SetOriginShapeRange(range);
+    GELOGD("node name is %s, origin shape is %ld, origin format is %s, origin data type is %s", node->GetName().c_str(),
+           output_tensor->GetOriginShape().GetShapeSize(),
+           TypeUtils::FormatToSerialString(output_tensor->GetOriginFormat()).c_str(),
+           TypeUtils::DataTypeToSerialString(output_tensor->GetOriginDataType()).c_str());
+  }
+  for (const auto &in_anchor : node->GetAllInDataAnchors()) {
+    auto input_tensor = op_desc->MutableInputDesc(in_anchor->GetIdx());
+    if (input_tensor == nullptr) {
+      continue;
+    }
+    // set input origin shape range
+    std::vector<std::pair<int64_t, int64_t>> range;
+    (void)input_tensor->GetShapeRange(range);
+    input_tensor->SetOriginShapeRange(range);
+  }
+}
+
+Status InferShapePass::DoRepassForLoopNode(NodePtr &node) {
   GE_CHK_STATUS_RET_NOLOG(RePassLoopNode(node));
   bool need_repass = false;
   auto has_attr = AttrUtils::GetBool(node->GetOpDesc(), ATTR_NAME_NEED_INFER_AGAIN, need_repass);
@@ -150,13 +372,13 @@ Status InferShapePass::RePassLoopNode(const NodePtr &node) {
   GE_CHK_STATUS_RET(GetOriginalType(node, node_type),
                     "[Get][OriginalType] of node:%s failed.", node->GetName().c_str());
   if (kNextIterationOpTypes.count(node_type) > 0) {
-    return RePassNode(kMergeOpTypes); // Re-Pass Merge
+    return RePassNode(kMergeOpTypes);  // Re-Pass Merge
   }
 
   if (kMergeOpTypes.count(node_type) > 0) {
     if (node->GetOpDesc()->HasAttr(ATTR_NAME_NEED_INFER_AGAIN)) {
       node->GetOpDesc()->DelAttr(ATTR_NAME_NEED_INFER_AGAIN);
-      return RePassNode(kSwitchOpTypes); // Re-Pass Switch
+      return RePassNode(kSwitchOpTypes);  // Re-Pass Switch
     }
     return SUCCESS;
   }
@@ -164,12 +386,110 @@ Status InferShapePass::RePassLoopNode(const NodePtr &node) {
   if (kSwitchOpTypes.count(node_type) > 0) {
     if (node->GetOpDesc()->HasAttr(ATTR_NAME_NEED_INFER_AGAIN)) {
       node->GetOpDesc()->DelAttr(ATTR_NAME_NEED_INFER_AGAIN);
-      return ExProcNode(kExitOpTypes, &InferShapePass::AddNodeResume, "need resume"); // Resume Exit
+      return ExProcNode(kExitOpTypes, &InferShapePass::AddNodeResume, "need resume");  // Resume Exit
     } else {
-      return ExProcNode(kExitOpTypes, &InferShapePass::AddNodeSuspend, "need suspend"); // Suspend Exit
+      return ExProcNode(kExitOpTypes, &InferShapePass::AddNodeSuspend, "need suspend");  // Suspend Exit
     }
   }
 
   return SUCCESS;
 }
+
+GE_FUNC_DEV_VISIBILITY GE_FUNC_HOST_VISIBILITY
+graphStatus InferShapePass::InferShapeAndType(NodePtr &node) {
+  GE_CHECK_NOTNULL(node);
+  GE_CHECK_NOTNULL(node->GetOpDesc());
+  InferShapePass pass;
+  std::set<NodePtr> unused_changed_nodes;
+  return pass.InferAndUpdate(node, true, unused_changed_nodes);
+}
+GE_FUNC_DEV_VISIBILITY GE_FUNC_HOST_VISIBILITY
+graphStatus InferShapePass::InferShapeAndType(NodePtr &node, bool before_subgraph) {
+  GE_CHECK_NOTNULL(node);
+  GE_CHECK_NOTNULL(node->GetOpDesc());
+  InferShapePass pass;
+  std::set<NodePtr> unused_changed_nodes;
+  return pass.InferAndUpdate(node, before_subgraph, unused_changed_nodes);
+}
+
+
+graphStatus InferShapeForRunning::Infer(NodePtr &node) {
+  auto opdesc = node->GetOpDesc();
+  vector<ge::DataType> temp_dtype;
+  for (auto &tensor_desc : opdesc->GetAllOutputsDescPtr()) {
+    temp_dtype.emplace_back(tensor_desc->GetDataType());
+  }
+  PrintInOutTensorShape(node, "before_infershape when running");
+
+  Operator op = OpDescUtils::CreateOperatorFromNode(node);
+  graphStatus status = CallInferShapeFuncForRunning(node, op);
+  if (status == GRAPH_PARAM_INVALID || status == GRAPH_SUCCESS) {
+    // ensure the dtype is not changed after infershape in running
+    auto after_opdesc = node->GetOpDesc();
+    GE_IF_BOOL_EXEC(after_opdesc == nullptr, REPORT_INNER_ERROR("E19999", "param node has no opdesc, check invalid.");
+                    GELOGE(GRAPH_FAILED, "[Get][OpDesc]  after_opdesc is null."); return GRAPH_FAILED);
+    auto all_output_tensor = after_opdesc->GetAllOutputsDescPtr();
+    for (size_t i = 0; i < all_output_tensor.size(); ++i) {
+      if (all_output_tensor.at(i)->GetDataType() != temp_dtype[i]) {
+        GELOGD("Op %s output %zu need reset dtype,original dtype is %s, new dtype is %s", node->GetName().c_str(), i,
+               TypeUtils::DataTypeToSerialString(all_output_tensor.at(i)->GetDataType()).c_str(),
+               TypeUtils::DataTypeToSerialString(temp_dtype[i]).c_str());
+        all_output_tensor.at(i)->SetDataType(temp_dtype[i]);
+      }
+    }
+    PrintInOutTensorShape(node, "after_infershape when running");
+    return GRAPH_SUCCESS;
+  } else {
+    REPORT_CALL_ERROR("E19999", "%s call infer function failed.", node->GetName().c_str());
+    GELOGE(GRAPH_FAILED, "[Call][InferFunction] failed, node:%s.", node->GetName().c_str());
+    return GRAPH_FAILED;
+  }
+}
+
+graphStatus InferShapeForRunning::CallInferShapeFuncForRunning(NodePtr &node, Operator &op) {
+  auto op_desc = node->GetOpDesc();
+  const auto &op_type = op_desc->GetType();
+
+  // Create InferenceContext to avoid null pointer access.
+  const static std::set<std::string> force_context_op_types{"Enter", "Switch", "RefSwitch"};
+  if (force_context_op_types.count(op_type) > 0) {
+    GELOGD("Set InferenceContext for node [%s]", op_desc->GetName().c_str());
+    op.SetInferenceContext(std::shared_ptr<InferenceContext>(InferenceContext::Create()));
+  }
+
+  // Get infer func and execute
+  auto ret = op_desc->CallInferFunc(op);
+  if (ret == GRAPH_PARAM_INVALID) {
+    GELOGD("NodeUtils::GetNodeType return value is: [%s]", NodeUtils::GetNodeType(*node).c_str());
+    auto origin_type = NodeUtils::GetNodeType(*node);
+    auto infer_func = ge::OperatorFactoryImpl::GetInferShapeFunc(origin_type);
+    if (infer_func == nullptr) {
+      REPORT_INNER_ERROR("E19999", "Failed to Get InferFunc. type is %s", origin_type.c_str());
+      GELOGE(GRAPH_FAILED, "[Get][InferFunc] failed. type is %s", origin_type.c_str());
+      return GRAPH_FAILED;
+    }
+    op_desc->AddInferFunc(infer_func);
+    ret = op_desc->CallInferFunc(op);
+    GELOGI("op CallInferFunc second. ret: %u", ret);
+  }
+  return ret;
+}
+bool InferShapeForRunning::TensorDescChanged(const GeTensorDescPtr &src, const GeTensorDescPtr &dst) {
+  bool changed = false;
+  const auto &dst_dims = dst->GetShape().GetDims();
+  const auto &src_dims = src->GetShape().GetDims();
+  if (dst_dims != src_dims) {
+    changed = true;
+  }
+  return changed;
+}
+
+GE_FUNC_DEV_VISIBILITY GE_FUNC_HOST_VISIBILITY
+graphStatus InferShapeForRunning::InferShapeAndTypeForRunning(NodePtr &node, bool before_subgraph) {
+  GE_CHECK_NOTNULL(node);
+  GE_CHECK_NOTNULL(node->GetOpDesc());
+  InferShapeForRunning pass;
+  std::set<NodePtr> unused_changed_nodes;
+  return pass.InferAndUpdate(node, before_subgraph, unused_changed_nodes);
+}
 }  // namespace ge

ge/graph/passes/infershape_pass.h

@@ -17,22 +17,38 @@
 #ifndef GE_GRAPH_PASSES_INFERSHAPE_PASS_H_
 #define GE_GRAPH_PASSES_INFERSHAPE_PASS_H_
 
-#include "graph/passes/base_pass.h"
+#include "graph/passes/infer_base_pass.h"
 
 namespace ge {
-class InferShapePass : public BaseNodePass {
+class InferShapePass : public InferBasePass {
  public:
-  ///
-  /// Entry of the InferShapePass optimizer
-  /// @param [in] graph: Input ComputeGraph
-  /// @return SUCCESS: Execution succeed
-  /// @return OTHERS:  Execution failed
-  /// @author
-  ///
-  Status Run(ge::NodePtr &node) override;
+  static void ClearContextMap();
+  graphStatus Infer(NodePtr &node) override;
+  bool TensorDescChanged(const GeTensorDescPtr &src, const GeTensorDescPtr &dst) override;
+  graphStatus UpdateInputDescAttr(const GeTensorDescPtr &src, GeTensorDescPtr &dst, bool &changed) override;
+  void AnalyzeFailedInfo(const NodePtr &node) override;
+
+  static graphStatus InferShapeAndType(NodePtr &node);                        // temp: visible static func
+  static graphStatus InferShapeAndType(NodePtr &node, bool before_subgraph);  // temp: visible static func
+
+ private:
+  graphStatus CallInferShapeFunc(NodePtr &node, Operator &op);
+  graphStatus UpdatePeerInputs(NodePtr &node) override;  // only for infershape, will be deleted
+  void UpdateInputOutputOriginAttr(NodePtr &node);       // only for infershape, will be deleted
+  Status DoRepassForLoopNode(NodePtr &node) override;    // only for infershape, will be deleted
+  Status RePassLoopNode(const NodePtr &node);            // only for infershape, will be deleted
+};
+
+
+class InferShapeForRunning : public InferBasePass {
+ public:
+  graphStatus Infer(NodePtr &node) override;
+  bool TensorDescChanged(const GeTensorDescPtr &src, const GeTensorDescPtr &dst) override;
+
+  static graphStatus InferShapeAndTypeForRunning(NodePtr &node, bool before_subgraph); // temp: visible static func
 
  private:
-  Status RePassLoopNode(const NodePtr &node);
+  graphStatus CallInferShapeFuncForRunning(NodePtr &node, Operator &op);
 };
 }  // namespace ge
 #endif  // GE_GRAPH_PASSES_INFERSHAPE_PASS_H_

ge/graph/preprocess/graph_preprocess.cc

@@ -54,6 +54,7 @@
 #include "graph/passes/hccl_group_pass.h"
 #include "graph/passes/identity_pass.h"
 #include "graph/passes/infershape_pass.h"
+#include "graph/passes/infer_value_range_pass.h"
 #include "graph/passes/merge_pass.h"
 #include "graph/passes/net_output_pass.h"
 #include "graph/passes/no_use_reshape_remove_pass.h"
@@ -1997,6 +1998,8 @@ Status GraphPrepare::InferShapeForPreprocess() {
   names_to_passes.emplace_back("MergePass", &merge_pass);
   InferShapePass infer_shape_pass;
   names_to_passes.emplace_back("InferShapePass", &infer_shape_pass);
+  InferValueRangePass infer_value_pass;
+  names_to_passes.emplace_back("InferValuePass", &infer_value_pass);
   ReplaceWithEmptyConstPass replace_with_empty_const_pass;
   names_to_passes.emplace_back("ReplaceWithEmptyConstPass", &replace_with_empty_const_pass);
   DimensionComputePass dimension_compute_pass;

tests/ut/ge/CMakeLists.txt

@@ -219,7 +219,9 @@ set(COMMON_SRC_FILES
     "${GE_CODE_DIR}/ge/graph/passes/shape_operate_op_remove_pass.cc"
     "${GE_CODE_DIR}/ge/graph/passes/assert_pass.cc"
     "${GE_CODE_DIR}/ge/graph/passes/dropout_pass.cc"
+	"${GE_CODE_DIR}/ge/graph/passes/infer_base_pass.cc"
     "${GE_CODE_DIR}/ge/graph/passes/infershape_pass.cc"
+	"${GE_CODE_DIR}/ge/graph/passes/infer_value_range_pass.cc"
     "${GE_CODE_DIR}/ge/graph/passes/unused_const_pass.cc"
     "${GE_CODE_DIR}/ge/graph/passes/permute_pass.cc"
     "${GE_CODE_DIR}/ge/graph/passes/ctrl_edge_transfer_pass.cc"
@@ -478,7 +480,7 @@ set(GRAPH_BUILD_COMMON_SRC_FILES
 )
 
 set(GRAPH_PASS_COMMON_SRC_FILES
-    "${GE_CODE_DIR}/ge/graph/passes/pass_manager.cc"
+	"${GE_CODE_DIR}/ge/graph/passes/pass_manager.cc"
     "${GE_CODE_DIR}/ge/graph/passes/base_pass.cc"
     "${GE_CODE_DIR}/ge/graph/passes/variable_prepare_op_pass.cc"
     "${GE_CODE_DIR}/ge/graph/passes/variable_ref_delete_op_pass.cc"
@@ -532,7 +534,9 @@ set(GRAPH_PASS_COMMON_SRC_FILES
     "${GE_CODE_DIR}/ge/graph/passes/transpose_transdata_pass.cc"
     "${GE_CODE_DIR}/ge/graph/passes/hccl_memcpy_pass.cc"
     "${GE_CODE_DIR}/ge/graph/passes/no_use_reshape_remove_pass.cc"
+	"${GE_CODE_DIR}/ge/graph/passes/infer_base_pass.cc"
     "${GE_CODE_DIR}/ge/graph/passes/infershape_pass.cc"
+	"${GE_CODE_DIR}/ge/graph/passes/infer_value_range_pass.cc"
     "${GE_CODE_DIR}/ge/ge_local_engine/engine/host_cpu_engine.cc"
     "${GE_CODE_DIR}/ge/analyzer/analyzer.cc"
     "${GE_CODE_DIR}/ge/graph/passes/net_output_pass.cc"
@@ -702,6 +706,7 @@ set(PASS_TEST_FILES
     "graph/passes/net_output_pass_unittest.cc"
     "graph/passes/no_use_reshape_remove_pass_unittest.cc"
     "graph/passes/infershape_pass_unittest.cc"
+    "graph/passes/infer_value_range_pass_unittest.cc"
     "graph/passes/mark_force_unknown_for_cond_pass_unittest.cc"
     "graph/passes/multi_batch_clone_pass_unittest.cc"
     "graph/passes/subgraph_const_migration_pass_unittest.cc"

tests/ut/ge/graph/passes/infer_value_range_pass_unittest.cc

@@ -0,0 +1,281 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <gtest/gtest.h>
+
+#define protected public
+#define private public
+#include "graph/passes/infer_value_range_pass.h"
+#include "graph/utils/tensor_utils.h"
+#include "graph/utils/graph_utils.h"
+#include "graph_builder_utils.h"
+
+#include "inc/external/graph/operator_reg.h"
+#include "inc/external/graph/operator.h"
+#include "inc/external/graph/operator_factory.h"
+#include "inc/graph/operator_factory_impl.h"
+#include "inc/kernel.h"
+#include "inc/kernel_factory.h"
+
+using namespace std;
+using namespace testing;
+namespace ge {
+class UtestGraphInferValueRangePass : public testing::Test {
+ protected:
+  void SetUp() {}
+  void TearDown() {}
+};
+
+static NodePtr CreateNode(ComputeGraph &graph, const string &name, const string &type, int in_num, int out_num) {
+  auto op_desc = std::make_shared<OpDesc>("AddN", "AddN");
+  return graph.AddNode(op_desc);
+}
+
+TEST_F(UtestGraphInferValueRangePass, infer_pass_not_register) {
+  auto graph = std::make_shared<ComputeGraph>("test_graph");
+  GeTensorDesc ge_tensor_desc(GeShape({1, 1, 4, 192}), ge::FORMAT_NCHW, DT_FLOAT16);
+  auto addn_op_desc = std::make_shared<OpDesc>("AddN", "AddN");
+  addn_op_desc->AddInputDesc(ge_tensor_desc);
+  addn_op_desc->AddOutputDesc(ge_tensor_desc);
+  auto addn_op_node = graph->AddNode(addn_op_desc);
+
+  InferValueRangePass infer_pass;
+  EXPECT_EQ(infer_pass.Run(addn_op_node), SUCCESS);
+}
+
+auto ShapeValueInfer = [&](Operator &op) {
+  auto op_desc = OpDescUtils::GetOpDescFromOperator(op);
+  auto output_tensor_desc = op_desc->MutableOutputDesc(0);
+  std::vector<std::pair<int64_t, int64_t>> in_shape_range;
+  op_desc->MutableInputDesc(0)->GetShapeRange(in_shape_range);
+  if (!in_shape_range.empty()) {
+    output_tensor_desc->SetValueRange(in_shape_range);
+  }
+  return SUCCESS;
+};
+
+TEST_F(UtestGraphInferValueRangePass, infer_pass_when_call_1_not_infer) {
+  INFER_VALUE_RANGE_CUSTOM_FUNC_REG(Shape, INPUT_IS_DYNAMIC, ShapeValueInfer);
+  auto graph = std::make_shared<ComputeGraph>("test_graph");
+  GeTensorDesc ge_tensor_desc(GeShape({1, 1, 4, 192}), ge::FORMAT_NCHW, DT_INT32);
+  std::vector<std::pair<int64_t, int64_t>> shape_range = {make_pair(1, 1), make_pair(1, 1),
+                                                          make_pair(4, 4),   make_pair(192, 192)};
+  ge_tensor_desc.SetShapeRange(shape_range);
+  GeTensorDesc output_tensor_desc(GeShape({4}), ge::FORMAT_NCHW, DT_INT32);
+  auto op_desc = std::make_shared<OpDesc>("Shape", "Shape");
+  op_desc->AddInputDesc(ge_tensor_desc);
+  op_desc->AddOutputDesc(output_tensor_desc);
+  auto op_node = graph->AddNode(op_desc);
+
+  InferValueRangePass infer_pass;
+  EXPECT_EQ(infer_pass.Run(op_node), SUCCESS);
+
+  auto output_0_desc = op_node->GetOpDesc()->GetOutputDesc(0);
+  std::vector<std::pair<int64_t, int64_t>> value_range;
+  output_0_desc.GetValueRange(value_range);
+  EXPECT_EQ(value_range.empty(), true);
+}
+
+TEST_F(UtestGraphInferValueRangePass, infer_pass_when_call_1_infer) {
+  // sqrt -> shape -> Output
+  INFER_VALUE_RANGE_CUSTOM_FUNC_REG(Shape, INPUT_IS_DYNAMIC, ShapeValueInfer);
+  auto graph = std::make_shared<ComputeGraph>("test_graph");
+  GeTensorDesc sqrt_tensor_desc(GeShape({-1, -1, 4, 192}), ge::FORMAT_NCHW, DT_INT32);
+  std::vector<std::pair<int64_t, int64_t>> shape_range = {make_pair(1, 100), make_pair(1, 240),
+                                                          make_pair(4, 4),   make_pair(192, 192)};
+  sqrt_tensor_desc.SetShapeRange(shape_range);
+  auto sqrt_op_desc = std::make_shared<OpDesc>("Sqrt", "Sqrt");
+  sqrt_op_desc->AddInputDesc(sqrt_tensor_desc);
+  sqrt_op_desc->AddOutputDesc(sqrt_tensor_desc);
+  auto sqrt_node = graph->AddNode(sqrt_op_desc);
+
+  GeTensorDesc shape_output_desc(GeShape({4}), ge::FORMAT_NCHW, DT_INT32);
+  auto shape_op_desc = std::make_shared<OpDesc>("Shape", "Shape");
+  shape_op_desc->AddInputDesc(sqrt_tensor_desc);
+  shape_op_desc->AddOutputDesc(shape_output_desc);
+  auto shape_node = graph->AddNode(shape_op_desc);
+
+  GeTensorDesc Output_in_tensor_desc(GeShape({4}), ge::FORMAT_NCHW, ge::DT_INT32);
+  auto Output_op_desc = std::make_shared<OpDesc>("Output", "Output");
+  Output_op_desc->AddInputDesc(Output_in_tensor_desc);
+  auto Output_node = graph->AddNode(Output_op_desc);
+
+  ge::GraphUtils::AddEdge(sqrt_node->GetOutDataAnchor(0), shape_node->GetInDataAnchor(0));
+  ge::GraphUtils::AddEdge(shape_node->GetOutDataAnchor(0), Output_node->GetInDataAnchor(0));
+  EXPECT_EQ(graph->TopologicalSorting(), GRAPH_SUCCESS);
+
+
+  InferValueRangePass infer_pass;
+  auto ret = infer_pass.Run(shape_node);
+  EXPECT_EQ(ret, SUCCESS);
+
+  auto output_0_desc = shape_node->GetOpDesc()->GetOutputDesc(0);
+  std::vector<std::pair<int64_t, int64_t>> value_range;
+  output_0_desc.GetValueRange(value_range);
+  EXPECT_EQ(value_range.size(), 4);
+  std::vector<int64_t> target_value_range = {1, 100, 1, 240, 4, 4, 192, 192};
+  std::vector<int64_t> output_value_range;
+  for (auto pair : value_range) {
+    output_value_range.push_back(pair.first);
+    output_value_range.push_back(pair.second);
+  }
+  EXPECT_EQ(target_value_range, output_value_range);
+
+  auto in_0_desc = Output_node->GetOpDesc()->GetInputDesc(0);
+  value_range.clear();
+  in_0_desc.GetValueRange(value_range);
+  EXPECT_EQ(value_range.size(), 0);
+
+  /*
+  INFER_VALUE_RANGE_DEFAULT_REG(Output);
+  ret = infer_pass.Run(Output_node);
+  EXPECT_EQ(ret, FAILED);
+  auto in_0_desc_after_infer = Output_node->GetOpDesc()->GetInputDesc(0);
+  value_range.clear();
+  in_0_desc_after_infer.GetValueRange(value_range);
+  EXPECT_EQ(value_range.size(), 4);
+  output_value_range.clear();
+  for (auto pair : value_range) {
+    output_value_range.push_back(pair.first);
+    output_value_range.push_back(pair.second);
+  }
+  EXPECT_EQ(target_value_range, output_value_range);
+
+  auto out_0_desc = Output_node->GetOpDesc()->GetOutputDesc(0);
+  value_range.clear();
+  out_0_desc.GetValueRange(value_range);
+  EXPECT_EQ(value_range.size(), 0);
+   */
+}
+
+class AddKernel : public Kernel {
+ public:
+  Status Compute(const ge::OpDescPtr op_desc_ptr, const std::vector<ge::ConstGeTensorPtr> &input,
+                 std::vector<ge::GeTensorPtr> &v_output) override {
+    vector<int64_t> data_vec;
+    auto data_num = input[0]->GetTensorDesc().GetShape().GetShapeSize();
+    auto x1_data = reinterpret_cast<const int64_t *>(input[0]->GetData().data());
+    auto x2_data = reinterpret_cast<const int64_t *>(input[1]->GetData().data());
+    for (size_t i = 0; i < data_num; i++) {
+      auto x_index = *(x1_data + i);
+      auto y_index = *(x2_data + i);
+      data_vec.push_back(x_index + y_index);
+    }
+    GeTensorPtr const_tensor = std::make_shared<ge::GeTensor>(input[0]->GetTensorDesc(), (uint8_t *)data_vec.data(),
+                                                              data_num * sizeof(int64_t));
+    v_output.emplace_back(const_tensor);
+    return SUCCESS;
+  }
+};
+REGISTER_KERNEL(ADD, AddKernel);
+
+TEST_F(UtestGraphInferValueRangePass, infer_pass_when_call_2_infer) {
+  // shape --- add --- sqrt
+  // constant /
+  INFER_VALUE_RANGE_DEFAULT_REG(Add);
+  auto graph = std::make_shared<ComputeGraph>("test_graph");
+
+  vector<int64_t> dims_vec = {4};
+  vector<int64_t> data_vec = {1, 1, 1, 1};
+  GeTensorDesc const_tensor_desc(ge::GeShape(dims_vec), ge::FORMAT_NCHW, ge::DT_INT64);
+  GeTensorPtr const_tensor =
+    std::make_shared<ge::GeTensor>(const_tensor_desc, (uint8_t *)data_vec.data(), data_vec.size() * sizeof(int64_t));
+
+  auto const_op_desc = std::make_shared<OpDesc>("Constant", "Constant");
+  const_op_desc->AddOutputDesc(const_tensor_desc);
+  EXPECT_EQ(OpDescUtils::SetWeights(const_op_desc, const_tensor), GRAPH_SUCCESS);
+  auto const_node = graph->AddNode(const_op_desc);
+
+  GeTensorDesc shape_tensor_desc(GeShape({4}), ge::FORMAT_NCHW, ge::DT_INT64);
+  std::vector<std::pair<int64_t, int64_t>> value_range = {make_pair(1, 100), make_pair(1, 240),
+                                                          make_pair(4, 4),   make_pair(192, 192)};
+  shape_tensor_desc.SetValueRange(value_range);
+  auto shape_op_desc = std::make_shared<OpDesc>("Shape", "Shape");
+  shape_op_desc->AddOutputDesc(shape_tensor_desc);
+  auto shape_node = graph->AddNode(shape_op_desc);
+
+  GeTensorDesc add_tensor_desc(GeShape({4}), ge::FORMAT_NCHW, ge::DT_INT64);
+  auto add_op_desc = std::make_shared<OpDesc>("Add", "Add");
+  add_op_desc->AddInputDesc(shape_tensor_desc);
+  add_op_desc->AddInputDesc(const_tensor_desc);
+  add_op_desc->AddOutputDesc(add_tensor_desc);
+  auto add_node = graph->AddNode(add_op_desc);
+
+  ge::GraphUtils::AddEdge(shape_node->GetOutDataAnchor(0), add_node->GetInDataAnchor(0));
+  ge::GraphUtils::AddEdge(const_node->GetOutDataAnchor(0), add_node->GetInDataAnchor(1));
+
+  InferValueRangePass infer_pass;
+  EXPECT_EQ(infer_pass.Run(add_node), SUCCESS);
+
+  auto output_0_desc = add_node->GetOpDesc()->GetOutputDesc(0);
+  std::vector<std::pair<int64_t, int64_t>> out_value_range;
+  output_0_desc.GetValueRange(out_value_range);
+  EXPECT_EQ(out_value_range.size(), 4);
+
+  std::vector<int64_t> target_value_range = {2, 101, 2, 241, 5, 5, 193, 193};
+  std::vector<int64_t> output_value_range;
+  for (auto pair : out_value_range) {
+    output_value_range.push_back(pair.first);
+    output_value_range.push_back(pair.second);
+  }
+  EXPECT_EQ(target_value_range, output_value_range);
+}
+
+TEST_F(UtestGraphInferValueRangePass, test_value_range_infer_and_set_get) {
+  using std::make_pair;
+  std::function<ge::graphStatus(ge::Operator &)> ShapeValueInfer_ = [](ge::Operator &op) -> ge::graphStatus {
+    auto op_desc = OpDescUtils::GetOpDescFromOperator(op);
+    auto output_tensor_desc = op_desc->MutableOutputDesc(0);
+    std::vector<std::pair<int64_t, int64_t>> in_shape_range;
+    op_desc->MutableInputDesc(0)->GetShapeRange(in_shape_range);
+    if (!in_shape_range.empty()) {
+      output_tensor_desc->SetValueRange(in_shape_range);
+    }
+    return GRAPH_SUCCESS;
+  };
+
+  INFER_VALUE_RANGE_CUSTOM_FUNC_REG(Shape, INPUT_IS_DYNAMIC, ShapeValueInfer_);
+  string op_type = "Shape";
+
+  auto graph = std::make_shared<ComputeGraph>("test_graph");
+  auto shape_op_desc = std::make_shared<OpDesc>("node_name", op_type);
+  GeTensorDesc tensor_desc(GeShape({-1, -1, 4, 192}), ge::FORMAT_NCHW, DT_INT32);
+  std::vector<std::pair<int64_t, int64_t>> shape_range = {make_pair(1, 100), make_pair(1, 240),
+                                                          make_pair(4, 4),   make_pair(192, 192)};
+  tensor_desc.SetShapeRange(shape_range);
+  shape_op_desc->AddInputDesc(tensor_desc);
+  GeTensorDesc out_tensor_desc(GeShape({4}), ge::FORMAT_NCHW, DT_INT32);
+  shape_op_desc->AddOutputDesc(out_tensor_desc);
+  auto shape_node = graph->AddNode(shape_op_desc);
+  Operator op = OpDescUtils::CreateOperatorFromNode(shape_node);
+  auto ret = shape_node->GetOpDesc()->CallInferValueRangeFunc(op);
+  ASSERT_EQ(ret, GRAPH_SUCCESS);
+
+  auto output_0_desc = shape_node->GetOpDesc()->GetOutputDesc(0);
+  std::vector<std::pair<int64_t, int64_t>> value_range;
+  output_0_desc.GetValueRange(value_range);
+  EXPECT_EQ(value_range.size(), 4);
+
+  std::vector<int64_t> target_value_range = {1, 100, 1, 240, 4, 4, 192, 192};
+  std::vector<int64_t> output_value_range;
+  for (auto pair : value_range) {
+    output_value_range.push_back(pair.first);
+    output_value_range.push_back(pair.second);
+  }
+  EXPECT_EQ(target_value_range, output_value_range);
+}
+
+}  // namespace ge

tests/ut/ge/graph/passes/infershape_pass_unittest.cc

@@ -15,6 +15,7 @@
  */
 
 #include <gtest/gtest.h>
+#include <operator_factory_impl.h>
 
 #define protected public
 #define private public
@@ -22,9 +23,12 @@
 
 #include "graph/utils/tensor_utils.h"
 #include "graph/utils/graph_utils.h"
-#include "graph/operator_factory.h"
-#include "graph/operator_reg.h"
 #include "graph_builder_utils.h"
+#include "inc/external/graph/operator_reg.h"
+#include "inc/external/graph/operator.h"
+#include "inc/external/graph/operator_factory.h"
+#include "inc/graph/operator_factory_impl.h"
+
 
 using namespace std;
 using namespace testing;
@@ -35,6 +39,113 @@ class UtestGraphInfershapePass : public testing::Test {
   void TearDown() {}
 };
 
+/*
+ *   data1  const1
+ *     \     /
+ *      case1
+ *        |
+ *      relu10
+ *        |
+ *    netoutput
+ */
+ut::GraphBuilder ParentGraphBuilder() {
+  ut::GraphBuilder builder = ut::GraphBuilder("g1");
+  auto data1 = builder.AddNode("data1", "Data", 0, 1);
+  std::vector<int64_t> const_shape = {1};
+  auto const1 = builder.AddNode("const1", "Const", 0, 1, FORMAT_NCHW, DT_INT32, const_shape);
+  auto case1 = builder.AddNode("case1", CASE, 2, 1);
+  auto relu1 = builder.AddNode("relu10", "Relu", 1, 1);
+  auto netoutput = builder.AddNode("netoutput", NETOUTPUT, 1, 0);
+
+  int32_t weight[1] = {1};
+  GeTensorDesc weight_desc(GeShape({1}), FORMAT_NHWC, DT_INT32);
+  GeTensorPtr tensor = std::make_shared<GeTensor>(weight_desc, (uint8_t *)weight, sizeof(weight));
+  OpDescUtils::SetWeights(const1, {tensor});
+
+  builder.AddDataEdge(data1, 0, case1, 0);
+  builder.AddDataEdge(const1, 0, case1, 1);
+  builder.AddDataEdge(case1, 0, relu1, 0);
+  builder.AddDataEdge(relu1, 0, netoutput, 0);
+  return builder;
+}
+
+/*
+ *   data1   data2
+ *     \      /
+ *      switch
+ *     /      \
+ *   relu1   relu2
+ *     \      /
+ *       merge
+ *         |
+ *     netoutput
+ */
+ut::GraphBuilder SwitchSubgraphBuilder(string graph_name, uint32_t num) {
+  ut::GraphBuilder builder = ut::GraphBuilder(graph_name);
+
+  std::vector<int64_t> shape1 = {2,2};
+  string data1_name = "data1_" + std::to_string(num);
+  auto data1 = builder.AddNode(data1_name, "Data", 1, 1, FORMAT_NCHW, DT_INT32, shape1);
+  auto data1_desc = data1->GetOpDesc();
+  EXPECT_NE(data1_desc, nullptr);
+  AttrUtils::SetInt(data1_desc, "_parent_node_index", 0);
+
+  std::vector<int64_t> shape2 = {3,3};
+  string data2_name = "data2_" + std::to_string(num);
+  auto data2 = builder.AddNode(data2_name, "Data", 1, 1, FORMAT_NCHW, DT_INT32, shape2);
+  auto data2_desc = data2->GetOpDesc();
+  EXPECT_NE(data2_desc, nullptr);
+  AttrUtils::SetInt(data2_desc, "_parent_node_index", 1);
+
+  string switch_name = "switch_" + std::to_string(num);
+  auto switch1 = builder.AddNode(switch_name, "Switch", 2, 2);
+
+  string relu1_name = "relu1_" + std::to_string(num);
+  auto relu1 = builder.AddNode(relu1_name, "Relu", 1, 1);
+
+  string relu2_name = "relu2_" + std::to_string(num);
+  auto relu2 = builder.AddNode(relu2_name, "Relu", 1, 1);
+
+  string merge_name = "merge_" + std::to_string(num);
+  auto merge = builder.AddNode(merge_name, "Merge", 2, 1);
+
+  std::vector<int64_t> shape7 = {8,8};
+  string output_name = "output_" + std::to_string(num);
+  auto netoutput = builder.AddNode(output_name, NETOUTPUT, 1, 0, FORMAT_NCHW, DT_INT32, shape7);
+  auto input0_desc = netoutput->GetOpDesc()->MutableInputDesc(0);
+  EXPECT_NE(input0_desc, nullptr);
+  AttrUtils::SetInt(input0_desc, "_parent_node_index", 0);
+
+  builder.AddDataEdge(data1, 0, switch1, 0);
+  builder.AddDataEdge(data2, 0, switch1, 1);
+  builder.AddDataEdge(switch1, 0, relu1, 0);
+  builder.AddDataEdge(switch1, 1, relu2, 0);
+  builder.AddDataEdge(relu1, 0, merge, 0);
+  builder.AddDataEdge(relu2, 0, merge, 1);
+  builder.AddDataEdge(merge, 0, netoutput, 0);
+
+  return builder;
+}
+
+void AddCaseSubgraph(ComputeGraphPtr &parent_graph, uint32_t branch_num) {
+  auto case_node = parent_graph->FindNode("case1");
+  EXPECT_NE(case_node, nullptr);
+
+  for (uint32_t i = 0; i < branch_num; ++i) {
+    string name = "Branch_Graph_" + std::to_string(i);
+
+    auto builder_subgraph = SwitchSubgraphBuilder(name, i);
+    auto switch_subgraph = builder_subgraph.GetGraph();
+
+    case_node->GetOpDesc()->AddSubgraphName(switch_subgraph->GetName());
+    case_node->GetOpDesc()->SetSubgraphInstanceName(i, switch_subgraph->GetName());
+
+    switch_subgraph->SetParentNode(case_node);
+    switch_subgraph->SetParentGraph(parent_graph);
+    EXPECT_EQ(parent_graph->AddSubgraph(switch_subgraph->GetName(), switch_subgraph), GRAPH_SUCCESS);
+  }
+}
+
 static NodePtr CreateNode(ComputeGraph &graph, const string &name, const string &type, int in_num, int out_num) {
   OpDescPtr op_desc = std::make_shared<OpDesc>(name, type);
   op_desc->SetStreamId(0);
@@ -158,4 +269,218 @@ TEST_F(UtestGraphInfershapePass, stop_node_for_while_loop) {
 
   EXPECT_EQ(ge_passes.Run(names_to_passes), SUCCESS);
 }
+
+TEST_F(UtestGraphInfershapePass, infer_with_case_subgraph) {
+  auto builder = ParentGraphBuilder();
+  auto parent_graph = builder.GetGraph();
+  AddCaseSubgraph(parent_graph, 2);
+  auto subgraphs = parent_graph->GetAllSubgraphs();
+  EXPECT_EQ(subgraphs.size(), 2);
+
+  auto case_node = parent_graph->FindNode("case1");
+  EXPECT_NE(case_node, nullptr);
+  InferShapePass infershape_pass;
+  EXPECT_EQ(infershape_pass.Run(case_node), SUCCESS);
+
+  std::vector<int64_t> target_dims_0 = {1, 1, 224, 224};
+  std::vector<int64_t> target_dims_1 = {1};
+  {
+    auto data_node = subgraphs[0]->FindNode("data1_0");
+    auto dims = data_node->GetOpDesc()->GetInputDescPtr(0)->GetShape().GetDims();
+    EXPECT_EQ(dims, target_dims_0);
+    data_node = subgraphs[0]->FindNode("data2_0");
+    dims = data_node->GetOpDesc()->GetInputDescPtr(0)->GetShape().GetDims();
+    EXPECT_EQ(dims, target_dims_1);
+  }
+
+  infershape_pass.options_[kOptimizeAfterSubGraph] = "yes";
+  EXPECT_EQ(infershape_pass.Run(case_node), SUCCESS);
+  {
+    auto dims = case_node->GetOpDesc()->GetOutputDescPtr(0)->GetShape().GetDims();
+    std::vector<int64_t> out_target_dims = {8, 8};
+    EXPECT_EQ(out_target_dims, dims);
+  }
+}
+
+/*
+ *   data1  const1
+ *     \     /
+ *      while
+ *     /    \
+ * relu1  netoutput
+ */
+ut::GraphBuilder ParentWhileGraphBuilder() {
+  ut::GraphBuilder builder = ut::GraphBuilder("g1");
+  auto data1 = builder.AddNode("data1", "Data", 0, 1);
+  std::vector<int64_t> const_shape = {1};
+  auto const1 = builder.AddNode("const1", "Const", 0, 1, FORMAT_NCHW, DT_FLOAT, const_shape);
+  auto case1 = builder.AddNode("case1", WHILE, 2, 2);
+  auto relu1 = builder.AddNode("relu1", "Relu", 1, 1);
+  auto netoutput = builder.AddNode("netoutput", NETOUTPUT, 1, 0);
+
+  int32_t weight[1] = {1};
+  GeTensorDesc weight_desc(GeShape({1}), FORMAT_NHWC, DT_FLOAT);
+  GeTensorPtr tensor = std::make_shared<GeTensor>(weight_desc, (uint8_t *)weight, sizeof(weight));
+  OpDescUtils::SetWeights(const1, {tensor});
+
+  builder.AddDataEdge(data1, 0, case1, 0);
+  builder.AddDataEdge(const1, 0, case1, 1);
+  builder.AddDataEdge(case1, 0, relu1, 0);
+  builder.AddDataEdge(case1, 1, netoutput, 0);
+  return builder;
+}
+
+/*
+ *   data1   data2
+ *     \      /
+ *      switch
+ *      |    |
+ *      \    /
+ *     netoutput
+ */
+ut::GraphBuilder WhileSubgraphBuilder(string graph_name, uint32_t num) {
+  ut::GraphBuilder builder = ut::GraphBuilder(graph_name);
+
+  std::vector<int64_t> shape1 = {2,2};
+  string data1_name = "data1_" + std::to_string(num);
+  auto data1 = builder.AddNode(data1_name, "Data", 1, 1, FORMAT_NCHW, DT_FLOAT, shape1);
+  auto data1_desc = data1->GetOpDesc();
+  EXPECT_NE(data1_desc, nullptr);
+  AttrUtils::SetInt(data1_desc, "_parent_node_index", 0);
+
+  std::vector<int64_t> shape2 = {3,3};
+  string data2_name = "data2_" + std::to_string(num);
+  auto data2 = builder.AddNode(data2_name, "Data", 1, 1, FORMAT_NCHW, DT_FLOAT, shape2);
+  auto data2_desc = data2->GetOpDesc();
+  EXPECT_NE(data2_desc, nullptr);
+  AttrUtils::SetInt(data2_desc, "_parent_node_index", 1);
+
+  string switch_name = "switch_" + std::to_string(num);
+  auto switch1 = builder.AddNode(switch_name, "Switch", 2, 2);
+
+  std::vector<int64_t> shape7 = {8,8,8,8};
+  string output_name = "output_" + std::to_string(num);
+  auto netoutput = builder.AddNode(output_name, NETOUTPUT, 2, 0, FORMAT_NCHW, DT_FLOAT, shape7);
+  auto input0_desc = netoutput->GetOpDesc()->MutableInputDesc(0);
+  EXPECT_NE(input0_desc, nullptr);
+  AttrUtils::SetInt(input0_desc, "_parent_node_index", 0);
+  auto input1_desc = netoutput->GetOpDesc()->MutableInputDesc(1);
+  EXPECT_NE(input1_desc, nullptr);
+  AttrUtils::SetInt(input1_desc, "_parent_node_index", 1);
+
+  builder.AddDataEdge(data1, 0, switch1, 0);
+  builder.AddDataEdge(data2, 0, switch1, 1);
+  builder.AddDataEdge(switch1, 0, netoutput, 0);
+  builder.AddDataEdge(switch1, 1, netoutput, 1);
+  return builder;
+}
+
+void AddWhileSubgraph(ComputeGraphPtr &parent_graph, uint32_t branch_num) {
+  auto case_node = parent_graph->FindNode("case1");
+  EXPECT_NE(case_node, nullptr);
+
+  for (uint32_t i = 0; i < branch_num; ++i) {
+    string name = "Branch_Graph_" + std::to_string(i);
+
+    auto builder_subgraph = WhileSubgraphBuilder(name, i);
+    auto switch_subgraph = builder_subgraph.GetGraph();
+
+    case_node->GetOpDesc()->AddSubgraphName(switch_subgraph->GetName());
+    case_node->GetOpDesc()->SetSubgraphInstanceName(i, switch_subgraph->GetName());
+
+    switch_subgraph->SetParentNode(case_node);
+    switch_subgraph->SetParentGraph(parent_graph);
+    EXPECT_EQ(parent_graph->AddSubgraph(switch_subgraph->GetName(), switch_subgraph), GRAPH_SUCCESS);
+  }
+}
+
+TEST_F(UtestGraphInfershapePass, infer_with_while_subgraph) {
+  auto builder = ParentWhileGraphBuilder();
+  auto parent_graph = builder.GetGraph();
+  AddWhileSubgraph(parent_graph, 1);
+  auto subgraphs = parent_graph->GetAllSubgraphs();
+  EXPECT_EQ(subgraphs.size(), 1);
+
+  auto case_node = parent_graph->FindNode("case1");
+  EXPECT_NE(case_node, nullptr);
+  InferShapePass infershape_pass;
+  EXPECT_EQ(infershape_pass.Run(case_node), SUCCESS);
+
+  std::vector<int64_t> target_dims_0 = {1, 1, 224, 224};
+  std::vector<int64_t> target_dims_1 = {1};
+  {
+    auto data_node = subgraphs[0]->FindNode("data1_0");
+    auto dims = data_node->GetOpDesc()->GetInputDescPtr(0)->GetShape().GetDims();
+    EXPECT_EQ(dims, target_dims_0);
+    data_node = subgraphs[0]->FindNode("data2_0");
+    dims = data_node->GetOpDesc()->GetInputDescPtr(0)->GetShape().GetDims();
+    EXPECT_EQ(dims, target_dims_1);
+  }
+
+  infershape_pass.options_[kOptimizeAfterSubGraph] = "yes";
+  EXPECT_EQ(infershape_pass.Run(case_node), SUCCESS);
+  {
+    auto dims = case_node->GetOpDesc()->GetOutputDescPtr(0)->GetShape().GetDims();
+    std::vector<int64_t> out_target_dims = {-1, -1, -1, -1};
+    EXPECT_EQ(out_target_dims, dims);
+  }
+}
+
+TEST_F(UtestGraphInfershapePass, infer_with_while_subgraph_failed) {
+  auto builder = ParentWhileGraphBuilder();
+  auto parent_graph = builder.GetGraph();
+  AddWhileSubgraph(parent_graph, 2);
+  auto subgraphs = parent_graph->GetAllSubgraphs();
+  EXPECT_EQ(subgraphs.size(), 2);
+
+  auto case_node = parent_graph->FindNode("case1");
+  EXPECT_NE(case_node, nullptr);
+  InferShapePass infershape_pass;
+  infershape_pass.options_[kOptimizeAfterSubGraph] = "yes";
+  EXPECT_EQ(infershape_pass.Run(case_node), GE_GRAPH_INFERSHAPE_FAILED);
+}
+
+auto InferFunc = [&](Operator &op) {
+  return GRAPH_SUCCESS;
+};
+TEST_F(UtestGraphInfershapePass, infer_forrunning_with_while_subgraph) {
+  auto builder = ParentWhileGraphBuilder();
+  auto parent_graph = builder.GetGraph();
+  AddWhileSubgraph(parent_graph, 1);
+  auto subgraphs = parent_graph->GetAllSubgraphs();
+  EXPECT_EQ(subgraphs.size(), 1);
+
+  OperatorFactoryImpl::RegisterInferShapeFunc("Relu", InferFunc);
+
+  auto relu_node = parent_graph->FindNode("relu1");
+  EXPECT_NE(relu_node, nullptr);
+  InferShapeForRunning infershape_for_running;
+  EXPECT_EQ(infershape_for_running.Run(relu_node), SUCCESS);
+}
+
+TEST_F(UtestGraphInfershapePass, infer_static_func) {
+  auto builder = ut::GraphBuilder("test_graph");
+  auto data_1 = builder.AddNode("data_1", DATA, 0, 1);
+  auto data_2 = builder.AddNode("data_2", DATA, 0, 1);
+  auto add = builder.AddNode("Add", "Add", 2, 1);
+
+  builder.AddDataEdge(data_1, 0, add, 0);
+  builder.AddDataEdge(data_2, 0, add, 1);
+  auto test_graph = builder.GetGraph();
+
+//  OperatorFactoryImpl::CreateOperator("Add", "Flatten");
+  auto test_node = test_graph->FindNode("Add");
+  auto ret = InferShapePass::InferShapeAndType(test_node);
+  EXPECT_EQ(ret, GRAPH_SUCCESS);
+
+  OperatorFactoryImpl::RegisterInferShapeFunc("Add", InferFunc);
+  ret = InferShapePass::InferShapeAndType(test_node);
+  EXPECT_EQ(ret, GRAPH_SUCCESS);
+
+  ret = InferShapePass::InferShapeAndType(test_node, true);
+  EXPECT_EQ(ret, GRAPH_SUCCESS);
+
+  ret = InferShapeForRunning::InferShapeAndTypeForRunning(test_node, true);
+  EXPECT_EQ(ret, GRAPH_SUCCESS);
+}
 }  // namespace ge