!3973 enable convert anf fusion pass and optimize

Merge pull request !3973 from zhengjun10/master

mindspore/lite/src/gllo/common/utils.cc → mindspore/lite/src/gllo/common/gllo_utils.cc

@@ -13,83 +13,46 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
+#include "src/gllo/common/gllo_utils.h"
 #include <vector>
-#include <memory>
-#include "src/gllo/common/utils.h"
 #include "src/ir/primitive_t_value.h"
 #include "frontend/operator/ops.h"
 
-using PrimitiveTValuePtr = std::shared_ptr<mindspore::lite::PrimitiveTValue>;
 namespace mindspore {
 namespace opt {
-bool AnfEqual(const BaseRef &a, const BaseRef &b) {
-  if (utils::isa<AnfNodePtr>(a) && utils::isa<AnfNodePtr>(b)) {
-    auto a_node = utils::cast<AnfNodePtr>(a);
-    auto b_node = utils::cast<AnfNodePtr>(b);
-    MS_EXCEPTION_IF_NULL(a_node);
-    MS_EXCEPTION_IF_NULL(b_node);
-    if (IsValueNode<Primitive>(a_node) && IsValueNode<Primitive>(b_node)) {
-      auto a_value_node = a_node->cast<ValueNodePtr>();
-      MS_EXCEPTION_IF_NULL(a_value_node);
-      auto a_value = a_value_node->value();
-      MS_EXCEPTION_IF_NULL(a_value);
-      auto a_prim = a_value->cast<PrimitivePtr>();
-      MS_EXCEPTION_IF_NULL(a_prim);
-
-      auto b_value_node = b_node->cast<ValueNodePtr>();
-      MS_EXCEPTION_IF_NULL(b_value_node);
-      auto b_value = b_value_node->value();
-      MS_EXCEPTION_IF_NULL(b_value);
-      auto b_prim = b_value->cast<PrimitivePtr>();
-      MS_EXCEPTION_IF_NULL(b_prim);
-
-      return a_prim->name() == b_prim->name();
-    } else if (a_node->isa<ValueNode>() && b_node->isa<ValueNode>()) {
-      auto a_value_node_ptr = a_node->cast<ValueNodePtr>();
-      if (a_value_node_ptr == nullptr) {
-        MS_LOG(EXCEPTION) << "cast value node ptr fail";
-      }
-      auto a_value_ptr = a_value_node_ptr->value();
-      if (a_value_ptr == nullptr) {
-        MS_LOG(EXCEPTION) << "value ptr is nullptr";
-      }
-
-      auto b_value_node_ptr = b_node->cast<ValueNodePtr>();
-      if (b_value_node_ptr == nullptr) {
-        MS_LOG(EXCEPTION) << "cast value node ptr fail";
-      }
-      auto b_value_ptr = b_value_node_ptr->value();
-      if (b_value_ptr == nullptr) {
-        MS_LOG(EXCEPTION) << "value ptr is nullptr";
-      }
-
-      if (utils::isa<lite::PrimitiveTValue>(a_value_ptr) && utils::isa<lite::PrimitiveTValue>(b_value_ptr)) {
-        auto a_obj = (lite::PrimitiveTValue *)(a_value_ptr.get());
-        auto b_obj = (lite::PrimitiveTValue *)(b_value_ptr.get());
-        return (*a_obj) == (*b_obj);
-      } else {
-        return (*a_value_ptr) == (*b_value_ptr);
-      }
-    }
-  }
-  if (a.m_ptr->isa<lite::PrimitiveTValue>()) {
-    auto a_value_node_ptr = a.m_ptr->cast<PrimitiveTValuePtr>();
-    auto b_value_node_ptr = b.m_ptr->cast<PrimitiveTValuePtr>();
-    return a_value_node_ptr->GetPrimitiveT()->value.type == b_value_node_ptr->GetPrimitiveT()->value.type;
+namespace {
+constexpr auto kAnfPrimitiveIndex = 0;
+bool CheckPrimitiveType(const AnfNodePtr &node, const PrimitivePtr &primitive_type) {
+  MS_EXCEPTION_IF_NULL(node);
+  if (!node->isa<CNode>()) {
+    return false;
   }
-
-  return a == b;
+  auto cnode = node->cast<CNodePtr>();
+  MS_EXCEPTION_IF_NULL(cnode);
+  return IsPrimitive(cnode->input(kAnfPrimitiveIndex), primitive_type);
 }
 
-bool CNodeTypeEqual(const BaseRef &a, const BaseRef &b) {
-  // To matchCNode and Kernel's type
-  if (utils::isa<CNode>(a) && utils::isa<CNode>(b)) {
+bool IsRealKernel(const AnfNodePtr &node) {
+  MS_EXCEPTION_IF_NULL(node);
+  // parameter and value node is not a real kernel too
+  if (!node->isa<CNode>()) {
     return true;
   }
-  return a.type() == b.type();
+  auto cnode = node->cast<CNodePtr>();
+  MS_EXCEPTION_IF_NULL(cnode);
+  if (cnode->inputs().empty()) {
+    MS_LOG(EXCEPTION) << "Illegal null input of cnode(%s)" << node->DebugString();
+  }
+  auto input = cnode->inputs()[0];
+  bool is_virtual_node = IsPrimitive(input, prim::kPrimImageSummary) || IsPrimitive(input, prim::kPrimScalarSummary) ||
+      IsPrimitive(input, prim::kPrimTensorSummary) ||
+      IsPrimitive(input, prim::kPrimHistogramSummary) || IsPrimitive(input, prim::kPrimMakeTuple) ||
+      IsPrimitive(input, prim::kPrimStateSetItem) || IsPrimitive(input, prim::kPrimDepend) ||
+      IsPrimitive(input, prim::kPrimTupleGetItem) || IsPrimitive(input, prim::kPrimControlDepend) ||
+      IsPrimitive(input, prim::kPrimReturn) || IsPrimitive(input, prim::kPrimPartial);
+  return !is_virtual_node;
 }
 
-namespace {
 ValueNodePtr CreateValueNodeWithSexp(const BaseRef &sexp) {
   if (utils::isa<int>(sexp)) {
     return NewValueNode(utils::cast<int>(sexp));
@@ -118,11 +81,11 @@ CNodePtr CreateCNodeWithGraph(const std::vector<AnfNodePtr> &input_nodes, const
 
 VarNodePtr CreateVarNodeWithSexp(const BaseRef &sexp, const BaseRef &graph) {
   if (utils::isa<VarPtr>(graph)) {
-    // MS_LOG(DEBUG) << "make VarPtr " + graph.ToString();
+    MS_LOG(DEBUG) << "make VarPtr " + graph.ToString();
     return std::make_shared<VarNode>(utils::cast<VarPtr>(sexp), nullptr);
   }
   if (utils::isa<FuncGraphPtr>(graph)) {
-    // MS_LOG(DEBUG) << "VarNode, should input a Var in graph. It's GraphPtr: " + graph.ToString();
+    MS_LOG(DEBUG) << "VarNode, should input a Var in graph. It's GraphPtr: " + graph.ToString();
     return std::make_shared<VarNode>(utils::cast<VarPtr>(sexp), utils::cast<FuncGraphPtr>(graph));
   }
   MS_LOG(ERROR) << "VarNode, should input a Var in graph. It's " + graph.ToString();
@@ -131,7 +94,7 @@ VarNodePtr CreateVarNodeWithSexp(const BaseRef &sexp, const BaseRef &graph) {
 
 AnfNodePtr HandleSexpVector(const BaseRef &sexp, const BaseRef &graph, PrimitiveVarMap *primitive_vars,
                             bool multigraph) {
-  // MS_LOG(DEBUG) << "HandleSexpVector sexp: " + sexp.ToString() + ", graph " + graph.ToString();
+  MS_LOG(DEBUG) << "HandleSexpVector sexp: " + sexp.ToString() + ", graph " + graph.ToString();
   std::vector<AnfNodePtr> input_nodes;
   const auto &tuple = utils::cast<VectorRef>(sexp);
   if (multigraph && utils::isa<VarPtr>(graph)) {
@@ -151,8 +114,75 @@ AnfNodePtr HandleSexpVector(const BaseRef &sexp, const BaseRef &graph, Primitive
 }
 }  // namespace
 
+bool AnfEqual(const BaseRef &a, const BaseRef &b) {
+  if (utils::isa<AnfNodePtr>(a) && utils::isa<AnfNodePtr>(b)) {
+    auto a_node = utils::cast<AnfNodePtr>(a);
+    auto b_node = utils::cast<AnfNodePtr>(b);
+    MS_EXCEPTION_IF_NULL(a_node);
+    MS_EXCEPTION_IF_NULL(b_node);
+    if (IsValueNode<Primitive>(a_node) && IsValueNode<Primitive>(b_node)) {
+      auto a_value_node = a_node->cast<ValueNodePtr>();
+      MS_EXCEPTION_IF_NULL(a_value_node);
+      auto a_value = a_value_node->value();
+      MS_EXCEPTION_IF_NULL(a_value);
+      auto a_prim = a_value->cast<PrimitivePtr>();
+      MS_EXCEPTION_IF_NULL(a_prim);
+
+      auto b_value_node = b_node->cast<ValueNodePtr>();
+      MS_EXCEPTION_IF_NULL(b_value_node);
+      auto b_value = b_value_node->value();
+      MS_EXCEPTION_IF_NULL(b_value);
+      auto b_prim = b_value->cast<PrimitivePtr>();
+      MS_EXCEPTION_IF_NULL(b_prim);
+
+      return a_prim->name() == b_prim->name();
+    } else if (a_node->isa<ValueNode>() && b_node->isa<ValueNode>()) {
+      auto a_value_node_ptr = a_node->cast<ValueNodePtr>();
+      if (a_value_node_ptr == nullptr) {
+        MS_LOG(EXCEPTION) << "cast value node ptr fail";
+      }
+      auto a_value_ptr = a_value_node_ptr->value();
+      if (a_value_ptr == nullptr) {
+        MS_LOG(EXCEPTION) << "value ptr is nullptr";
+      }
+
+      auto b_value_node_ptr = b_node->cast<ValueNodePtr>();
+      if (b_value_node_ptr == nullptr) {
+        MS_LOG(EXCEPTION) << "cast value node ptr fail";
+      }
+      auto b_value_ptr = b_value_node_ptr->value();
+      if (b_value_ptr == nullptr) {
+        MS_LOG(EXCEPTION) << "value ptr is nullptr";
+      }
+
+      if (utils::isa<lite::PrimitiveTValue>(a_value_ptr) && utils::isa<lite::PrimitiveTValue>(b_value_ptr)) {
+        auto a_obj = (lite::PrimitiveTValue *) (a_value_ptr.get());
+        auto b_obj = (lite::PrimitiveTValue *) (b_value_ptr.get());
+        return (*a_obj) == (*b_obj);
+      } else {
+        return (*a_value_ptr) == (*b_value_ptr);
+      }
+    }
+  }
+  if (a.m_ptr->isa<lite::PrimitiveTValue>() && b.m_ptr->isa<lite::PrimitiveTValue>()) {
+    auto a_value_node_ptr = a.m_ptr->cast<PrimitiveTValuePtr>();
+    auto b_value_node_ptr = b.m_ptr->cast<PrimitiveTValuePtr>();
+    return a_value_node_ptr->GetPrimitiveT()->value.type == b_value_node_ptr->GetPrimitiveT()->value.type;
+  }
+
+  return a == b;
+}
+
+bool CNodeTypeEqual(const BaseRef &a, const BaseRef &b) {
+  // To matchCNode and Kernel's type
+  if (utils::isa<CNode>(a) && utils::isa<CNode>(b)) {
+    return true;
+  }
+  return a.type() == b.type();
+}
+
 AnfNodePtr SexpToNode(const BaseRef &sexp, const BaseRef &graph, PrimitiveVarMap *primitive_vars, bool multigraph) {
-  // MS_LOG(DEBUG) << "SexpToNode sexp: " + sexp.ToString() + ", graph " + graph.ToString();
+  MS_LOG(DEBUG) << "SexpToNode sexp: " + sexp.ToString() + ", graph " + graph.ToString();
   MS_EXCEPTION_IF_NULL(primitive_vars);
   if (utils::isa<VectorRef>(sexp)) {
     return HandleSexpVector(sexp, graph, primitive_vars, multigraph);
@@ -176,6 +206,38 @@ AnfNodePtr SexpToNode(const BaseRef &sexp, const BaseRef &graph, PrimitiveVarMap
   return value_node;
 }
 
+bool IsRealCNodeKernel(const AnfNodePtr &node) {
+  MS_EXCEPTION_IF_NULL(node);
+  // parameter and value node is not a real cnode kernel
+  if (!node->isa<CNode>()) {
+    return false;
+  }
+  // return considered as a real node
+  if (CheckPrimitiveType(node, prim::kPrimReturn)) {
+    return true;
+  }
+  return IsRealKernel(node);
+}
+bool IsGraphKernel(const AnfNodePtr &node) {
+  MS_EXCEPTION_IF_NULL(node);
+  // graph kernel should be a real cnode kernel.
+  if (!IsRealCNodeKernel(node)) {
+    return false;
+  }
+
+  auto cnode = node->cast<CNodePtr>();
+  MS_EXCEPTION_IF_NULL(cnode);
+  auto input = cnode->input(kAnfPrimitiveIndex);
+  // graph kernel should has func_graph as first input.
+  if (!IsValueNode<FuncGraph>(input)) {
+    return false;
+  }
+
+  auto func_graph = GetValueNode<FuncGraphPtr>(input);
+  MS_EXCEPTION_IF_NULL(func_graph);
+  return func_graph->has_attr(FUNC_GRAPH_ATTR_GRAPH_KERNEL);
+}
+
 void CheckIfFuncGraphIsNull(const FuncGraphPtr &graph) {
   if (graph == nullptr) {
     MS_LOG(EXCEPTION) << "The graph is null.";

mindspore/lite/src/gllo/common/utils.h → mindspore/lite/src/gllo/common/gllo_utils.h

@@ -14,22 +14,21 @@
  * limitations under the License.
  */
 
-#ifndef MINDSPORE_LITE_SRC_PASS_COMMON_UTILS_H_
-#define MINDSPORE_LITE_SRC_PASS_COMMON_UTILS_H_
+#ifndef MINDSPORE_LITE_SRC_PASS_COMMON_GLLO_UTILS_H_
+#define MINDSPORE_LITE_SRC_PASS_COMMON_GLLO_UTILS_H_
 
-#include <mindspore/lite/src/ir/primitive_t_value.h>
 #include <memory>
+#include "src/ir/primitive_t_value.h"
 #include "ir/anf.h"
 #include "ir/func_graph.h"
 #include "src/common/utils.h"
-#include "src/gllo/common/pattern_engine.h"
+#include "backend/optimizer/common/pattern_engine.h"
 #include "schema/inner/model_generated.h"
 #include "src/param_value_lite.h"
 
 using PrimitiveTValuePtr = std::shared_ptr<mindspore::lite::PrimitiveTValue>;
 namespace mindspore {
 namespace opt {
-
 bool AnfEqual(const BaseRef &a, const BaseRef &b);
 
 bool CNodeTypeEqual(const BaseRef &a, const BaseRef &b);
@@ -37,6 +36,10 @@ bool CNodeTypeEqual(const BaseRef &a, const BaseRef &b);
 AnfNodePtr SexpToNode(const BaseRef &sexp, const BaseRef &graph, PrimitiveVarMap *primitive_vars,
                       bool multigraph = false);
 
+bool IsRealCNodeKernel(const AnfNodePtr &node);
+
+bool IsGraphKernel(const AnfNodePtr &node);
+
 void CheckIfFuncGraphIsNull(const FuncGraphPtr &graph);
 
 void CheckIfAnfNodeIsNull(const AnfNodePtr &node);
@@ -61,4 +64,4 @@ bool IsParamNode(const BaseRef &n);
 bool IsConvNode(const BaseRef &n);
 }  // namespace opt
 }  // namespace mindspore
-#endif  // MINDSPORE_LITE_SRC_PASS_COMMON_UTILS_H_
+#endif  // MINDSPORE_LITE_SRC_PASS_COMMON_GLLO_UTILS_H_

mindspore/lite/src/gllo/common/node_pass.cc

@@ -13,7 +13,7 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#include "src/gllo/common/node_pass.h"
+#include "backend/optimizer/common/node_pass.h"
 
 #include <unordered_set>
 #include <deque>
@@ -22,6 +22,7 @@
 #include "ir/anf.h"
 #include "ir/func_graph.h"
 #include "ir/manager.h"
+#include "src/gllo/common/gllo_utils.h"
 
 namespace mindspore {
 namespace opt {
@@ -54,6 +55,9 @@ bool NodePass::Run(const FuncGraphPtr &func_graph) {
       MS_EXCEPTION_IF_NULL(const_func_graph);
       todo.push_back(const_func_graph->output());
     } else if (new_node && new_node->isa<CNode>()) {
+      if (IsGraphKernel(new_node)) {
+        todo.push_back(new_node);
+      }
       auto cnode = new_node->cast<CNodePtr>();
       MS_EXCEPTION_IF_NULL(cnode);
       auto inputs = cnode->inputs();

mindspore/lite/src/gllo/common/node_pass.h

@@ -1,36 +0,0 @@
-/**
- * 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.
- */
-#ifndef MINDSPORE_LITE_SRC_PASS_COMMON_NODE_PASS_H_
-#define MINDSPORE_LITE_SRC_PASS_COMMON_NODE_PASS_H_
-#include <string>
-#include <memory>
-
-#include "src/gllo/common/pass.h"
-
-namespace mindspore {
-namespace opt {
-// @brief ANF Node level optimization base pass
-class NodePass : public Pass {
- public:
-  explicit NodePass(const std::string &name) : Pass(name) {}
-  ~NodePass() override = default;
-  bool Run(const FuncGraphPtr &func_graph) final;
-  virtual AnfNodePtr Run(const FuncGraphPtr &func_graph, const AnfNodePtr &node) = 0;
-};
-using NodePassPtr = std::shared_ptr<NodePass>;
-}  // namespace opt
-}  // namespace mindspore
-#endif  // MINDSPORE_LITE_SRC_PASS_COMMON_NODE_PASS_H_

mindspore/lite/src/gllo/common/optimizer.cc

@@ -23,8 +23,7 @@
 #include <utility>
 #include <initializer_list>
 
-#include "src/gllo/common/pass_manager.h"
-#include "backend/session/anf_runtime_algorithm.h"
+#include "backend/optimizer/common/pass_manager.h"
 #include "ir/manager.h"
 
 namespace mindspore {

mindspore/lite/src/gllo/common/optimizer.h

@@ -26,9 +26,9 @@
 #include "ir/graph_utils.h"
 #include "src/common/utils.h"
 
-#include "src/gllo/common/pass_manager.h"
-#include "src/gllo/common/pattern_engine.h"
-#include "src/gllo/common/utils.h"
+#include "backend/optimizer/common/pass_manager.h"
+#include "backend/optimizer/common/pattern_engine.h"
+#include "src/gllo/common/gllo_utils.h"
 
 namespace mindspore {
 namespace opt {

mindspore/lite/src/gllo/common/pass_manager.cc

@@ -13,7 +13,7 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#include "src/gllo/common/pass_manager.h"
+#include "backend/optimizer/common/pass_manager.h"
 
 #include <sys/time.h>
 #include <unordered_set>

mindspore/lite/src/gllo/common/pass_manager.h

@@ -1,61 +0,0 @@
-/**
- * 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.
- */
-#ifndef MINDSPORE_LITE_SRC_PASS_COMMON_PASS_MANAGER_H_
-#define MINDSPORE_LITE_SRC_PASS_COMMON_PASS_MANAGER_H_
-
-#include <utility>
-#include <vector>
-#include <string>
-#include <memory>
-
-#include "src/gllo/common/pass.h"
-#include "src/gllo/common/node_pass.h"
-
-namespace mindspore {
-namespace opt {
-// @brief For optimization passes management
-class PassManager {
- public:
-  explicit PassManager(const std::string &name = "pm", bool run_only_once = true)
-      : name_(name), passes_{}, run_only_once_(run_only_once) {}
-  virtual ~PassManager() = default;
-  // Get all the passes added by AddPass
-  const std::vector<PassPtr> &Passes() const;
-  // Add graph pass, the pass object will be freed when pass manager freed.
-  void AddPass(const PassPtr &pass);
-  // Run passes added in pass manager on the input graph
-  // @param [inout] graph The graph to be optimized
-  // @return true, graph changed
-  // @return false, graph not changed
-  bool Run(const FuncGraphPtr &func_graph) const;
-  // Run the given graph passes on the input graph
-  // @param [inout] graph The graph to be optimized
-  // @param [in] passes The given graph passes
-  // @return true, graph changed
-  // @return false, graph not changed
-  bool Run(const FuncGraphPtr &func_graph, const std::vector<PassPtr> &passes) const;
-  std::string name() const { return name_; }
-
- private:
-  const std::string name_;
-  std::vector<PassPtr> passes_;
-  bool run_only_once_;
-};
-using PassManagerPtr = std::shared_ptr<PassManager>;
-}  // namespace opt
-}  // namespace mindspore
-
-#endif  // MINDSPORE_LITE_SRC_PASS_COMMON_PASS_MANAGER_H_

mindspore/lite/src/gllo/common/pattern_engine.cc

@@ -1,365 +0,0 @@
-/**
- * This is the C++ adaptation and derivative work of Myia (https://github.com/mila-iqia/myia/).
- *
- * 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 "src/gllo/common/pattern_engine.h"
-
-#include <exception>
-#include <iostream>
-#include <functional>
-#include <iterator>
-
-#include "ir/func_graph.h"
-#include "mindspore/core/ir/primitive.h"
-#include "utils/info.h"
-#include "ir/anf.h"
-#include "utils/convert_utils_base.h"
-#include "utils/overload.h"
-
-
-namespace mindspore {
-static int GetNextTag() {
-  static int kID = 0;
-  return kID++;
-}
-
-void Var::EnsureTag() {
-  if (tag_.length() == 0) {
-    std::ostringstream buffer;
-    buffer << "_" << GetNextTag();
-    tag_ = buffer.str();
-  }
-}
-
-bool operator==(const VarPtr &lhs, const VarPtr &rhs) {
-  if (lhs->isa<CondVar>() && rhs->isa<CondVar>()) {
-    CondVarPtr v1 = dyn_cast<CondVar>(lhs);
-    CondVarPtr v2 = dyn_cast<CondVar>(rhs);
-    return *v1 == *v2;
-  }
-
-  if (lhs->isa<SeqVar>() && rhs->isa<SeqVar>()) {
-    SVarPtr v1 = dyn_cast<SeqVar>(lhs);
-    SVarPtr v2 = dyn_cast<SeqVar>(rhs);
-    return *v1 == *v2;
-  }
-  return (*lhs == *rhs);
-}
-
-std::string SeqVar::ToString() const {
-  std::ostringstream buffer;
-  buffer << "SeqVar(" << tag() << ", " << subvar_->ToString() << ")";
-  return buffer.str();
-}
-
-std::ostream &operator<<(std::ostream &os, const VarPtr &var) {
-  if (var == nullptr) {
-    os << "";
-  } else {
-    os << var->ToString();
-  }
-  return os;
-}
-
-template <>
-
-std::ostream &operator<<<VarPtr, BaseRef>(std::ostream &os, const Equiv &equiv) {
-  os << "[Equiv]"
-     << "\n";
-  for (auto &equiv_item : equiv) {
-    auto k = equiv_item.first;
-    os << k << ":";
-    BaseRef x = equiv_item.second;
-    if (utils::isa<AnfNodePtr>(x)) {
-      auto node = utils::cast<AnfNodePtr>(x);
-      os << "TypeString[" << node->type_name() << "]";
-      if (IsValueNode<FuncGraph>(node)) {
-        os << "IsValueNodeGraph ";
-      }
-      os << "type " << node->type_name();
-      if (node->isa<ValueNode>()) {
-        os << " value " << GetValueNode(node);
-      }
-      os << " addr: " << node;
-    } else if (utils::isa<Named>(x)) {
-      os << "Named " << x.ToString().c_str();
-    } else if (utils::isa<VarPtr>(x)) {
-      os << "TypeString[Var]";
-      os << utils::cast<VarPtr>(x);
-    } else if (utils::isa<FuncGraphPtr>(x)) {
-      os << "TypeString[Graph]";
-    }
-    os << "\n";
-  }
-  return os;
-}
-
-
-static BaseRef GetVar(const BaseRef &x) {
-  // MS_LOG(DEBUG) << "getVar start :%s" + x.ToString();
-  if (utils::isa<AnfNodePtr>(x)) {
-    auto node = utils::cast<AnfNodePtr>(x);
-    // MS_LOG(DEBUG) << "TypeString [" + node->type_name() + "]";
-    if (node->isa<VarNode>()) {
-      // MS_LOG(DEBUG) << "IsVarNode " + node->cast<VarNodePtr>()->var_->ToString();
-      return node->cast<VarNodePtr>()->var_;
-    }
-//    if (node->isa<ValueNode>()) {
-//      MS_LOG(DEBUG) << "value " + GetValueNode(node)->ToString() + " addr: " + node->ToString();
-//    } else {
-//      MS_LOG(DEBUG) << "type " + node->type_name();
-//    }
-//  } else if (utils::isa<Named>(x)) {
-//    MS_LOG(DEBUG) << "Named " + x.ToString();
-//  } else if (utils::isa<VectorRef>(x)) {
-//    MS_LOG(DEBUG) << "VectorRef";
-//  } else if (utils::isa<VarPtr>(x)) {
-//    MS_LOG(DEBUG) << "TypeString[Var] " + x.ToString();
-  }
-//  MS_LOG(DEBUG) << "GetVar end: " + x.ToString();
-  return x;
-}
-
-EquivPtr MatchOnVar(const BaseRef &pattern, const BaseRef &expr, EquivPtr equiv) {
-  MS_LOG(DEBUG) << "MatchOnVar pattern " + pattern.ToString() + " expr: " + expr.ToString();
-  MS_EXCEPTION_IF_NULL(equiv);
-  if (utils::isa<VarPtr>(pattern)) {
-    VarPtr var = utils::cast<VarPtr>(pattern);
-    if (var->matches(expr)) {
-      (*equiv)[var] = expr;
-      MS_LOG(DEBUG) << "pattern is var match: " + pattern.ToString() + ", " + expr.ToString();
-      return equiv;
-    }
-  }
-
-  return nullptr;
-}
-
-bool PatternEngine::ToVector(const VectorRef &pattern_ref, const VectorRef &expr_ref, VectorRef *const values_pattern,
-                             VectorRef *const values_expr) const {
-  MS_EXCEPTION_IF_NULL(values_expr);
-  if (utils::isa<SeqPtr>(pattern_ref)) {
-    *values_pattern = pattern_ref;
-    *values_expr = expr_ref;
-    return true;
-  }
-  return false;
-}
-
-bool PatternEngine::ToVector(const BaseRef &pattern_ref, const BaseRef &expr_ref, VectorRef *const values_pattern,
-                             VectorRef *const values_expr) const {
-  MS_EXCEPTION_IF_NULL(values_expr);
-  // visitor to visite the list
-  auto appender_pattern = [](VectorRef &values) {
-    std::function<BaseRef(const BaseRef &)> fn = [&](const BaseRef &u) {
-      values.push_back(GetVar(u));
-      return u;
-    };
-    return fn;
-  };
-
-  visitor_->SetFn(appender_pattern(*values_pattern));
-  // MS_LOG(DEBUG) << "visit pattern_ref";
-  bool success = visitor_->Visit(pattern_ref, nullptr);
-  if (!success) {
-    return false;
-  }
-
-  auto appender_expr = [](VectorRef &values) {
-    std::function<BaseRef(const BaseRef &)> fn = [&](const BaseRef &u) {
-      values.push_back(u);
-      return u;
-    };
-    return fn;
-  };
-
-  visitor_->SetFn(appender_expr(*values_expr));
-  // MS_LOG(DEBUG) << "visit expr_ref";
-  return visitor_->Visit(expr_ref, nullptr);
-}
-
-static int GetSVarStartIndex(const VectorRef &values) {
-  int index = -1;
-  int count = 0;
-  for (auto &value : values) {
-    if (utils::isa<VarPtr>(value) && utils::cast<VarPtr>(value)->isa<SeqVar>()) {
-      if (index != -1) {
-        // MS_LOG(DEBUG) << "Multiple SVars in sequence";
-        return kInvalidVarIndex;
-      }
-      index = count;
-    }
-    count++;
-  }
-  return index;
-}
-
-void UpdateEquivMap(const VectorRef &values_pattern, const BaseRef &expr_ref, const PrimitiveVarMap &primitive_vars,
-                    EquivPtr equiv) {
-  if (equiv == nullptr || values_pattern.empty() || !utils::isa<AnfNodePtr>(values_pattern[0]) ||
-      !utils::isa<AnfNodePtr>(expr_ref)) {
-    return;
-  }
-  auto real_node = utils::cast<AnfNodePtr>(expr_ref);
-  MS_EXCEPTION_IF_NULL(real_node);
-  if (!real_node->isa<CNode>()) {
-    return;
-  }
-  auto prim_node = utils::cast<AnfNodePtr>(values_pattern[0]);
-  MS_EXCEPTION_IF_NULL(prim_node);
-  if (!IsValueNode<Primitive>(prim_node)) {
-    return;
-  }
-  ValuePtr value = GetValueNode(prim_node);
-  MS_EXCEPTION_IF_NULL(value);
-  auto prim = value->cast<PrimitivePtr>();
-  MS_EXCEPTION_IF_NULL(prim);
-  auto iter = primitive_vars.find(prim);
-  if (iter == primitive_vars.end()) {
-    return;
-  }
-  (*equiv)[iter->second] = real_node;
-}
-
-EquivPtr PatternEngine::AlignSVar(const VectorRef &values_pattern, const VectorRef &values_expr,
-                                  const PrimitiveVarMap &primitive_vars, EquivPtr equiv) const {
-  int svar_index = GetSVarStartIndex(values_pattern);
-  if (svar_index == kInvalidVarIndex) {
-    return nullptr;
-  }
-
-  size_t values_pattern_len = values_pattern.size();
-  size_t values_expr_len = values_expr.size();
-
-  if (svar_index == -1) {
-    if (values_pattern_len != values_expr_len) {
-      // MS_LOG(DEBUG) << "Structures of differing size: pattern len " << values_pattern_len << ",
-      // expr len " << values_expr_len;
-      return nullptr;
-    }
-  }
-  if (values_expr_len < values_pattern_len - 1) {
-    MS_LOG(DEBUG) << "invalid size: pattern len " << values_pattern_len << ", expr len " << values_expr_len;
-    return nullptr;
-  }
-  size_t diff = values_expr_len - values_pattern_len + 1;
-  for (size_t i = 0; i < values_pattern_len; i++) {
-    size_t expr_i = i;
-    if (svar_index != -1 && i == IntToSize(svar_index)) {
-      auto seq =
-        std::vector<BaseRef>(values_expr.begin() + svar_index, values_expr.begin() + svar_index + SizeToInt(diff));
-      equiv = Match(values_pattern[svar_index], seq, primitive_vars, equiv);
-    } else {
-      if (svar_index != -1 && i > IntToSize(svar_index)) {
-        expr_i = i + diff - 1;
-      }
-      equiv = Match(values_pattern[i], values_expr[expr_i], primitive_vars, equiv);
-    }
-    if (equiv == nullptr) {
-      return nullptr;
-    }
-  }
-  return equiv;
-}
-
-EquivPtr PatternEngine::Match(const BaseRef &pattern, const BaseRef &expr, const PrimitiveVarMap &primitive_vars,
-                              EquivPtr equiv) const {
-  MS_LOG(DEBUG) << "-----[in Match]";
-  // MS_LOG(DEBUG) << "GetVar w";
-  BaseRef pattern_ref = GetVar(pattern);
-  // MS_LOG(DEBUG) << "GetVar v";
-  BaseRef expr_ref = expr;
-
-  if (equiv == nullptr) {
-    MS_LOG(EXCEPTION) << "Equiv pointer is null";
-  }
-
-  MS_LOG(DEBUG) << "Pattern ref " + pattern_ref.ToString() + ", expr ref" + expr_ref.ToString();
-  // 1. if pattern_ref is var and already in equiv, replace it.
-  if (utils::isa<VarPtr>(pattern_ref)) {
-    VarPtr var = utils::cast<VarPtr>(pattern_ref);
-    auto iter = equiv->find(var);
-    if (iter != equiv->end()) {
-      pattern_ref = iter->second;
-    }
-  }
-
-  // 2. check equal
-  if (eq_(pattern_ref, expr_ref)) {
-    return equiv;
-  }
-
-  // 3. match var
-  EquivPtr ret_equiv = MatchOnVar(pattern_ref, expr_ref, equiv);
-  if (ret_equiv) {
-    return ret_equiv;
-  }
-
-  // 4. here the type can be std:vector, std:list,
-  // or cnode.
-  if (!type_eq_(pattern_ref, expr_ref)) {
-    MS_LOG(DEBUG) << "Type mismatch";
-    return nullptr;
-  }
-
-  // 5. transfer the Containers by visitor to std::vector
-  VectorRef values_pattern;
-  VectorRef values_expr;
-  if (!ToVector(pattern_ref, expr_ref, &values_pattern, &values_expr)) {
-    return nullptr;
-  }
-
-  // 6. if any svar in both side, find the SeqVar index,
-  // try to pack the Var s in std::vector to a Seq and match elements one by one.
-  // check svar
-  equiv = AlignSVar(values_pattern, values_expr, primitive_vars, equiv);
-  UpdateEquivMap(values_pattern, expr_ref, primitive_vars, equiv);
-  return equiv;
-}
-
-BaseRef PatternEngine::Replace(const BaseRef &pattern, const EquivPtr &equiv) const {
-  MS_EXCEPTION_IF_NULL(equiv);
-  MS_LOG(DEBUG) << "-----[in Replace]";
-  BaseRef ref = GetVar(pattern);
-  BaseRef out;
-  bool is_match = false;
-
-  // w is var
-  if (utils::isa<VarPtr>(ref)) {
-    const VarPtr &var = utils::cast<VarPtr>(ref);
-    auto iter = equiv->find(var);
-    if (iter != equiv->end()) {
-      out = iter->second;
-      is_match = true;
-    }
-  }
-  if (is_match) {
-    return out;
-  }
-
-  // visitor to visit the list
-  std::function<BaseRef(BaseRef)> fn = [&, this, equiv](const BaseRef &u) { return Replace(u, equiv); };
-
-  visitor_->SetFn(fn);
-  BaseRef visit_out;
-  if (!visitor_->Visit(pattern, &visit_out)) {
-    return pattern;
-  }
-  return visit_out;
-}
-}  // namespace mindspore
-

mindspore/lite/src/gllo/common/pattern_engine.h

@@ -1,203 +0,0 @@
-/**
- * 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.
- */
-
-#ifndef MINDSPORE_LITE_SRC_PASS_COMMON_PATTERN_ENGINE_H_
-#define MINDSPORE_LITE_SRC_PASS_COMMON_PATTERN_ENGINE_H_
-
-#include <string>
-#include <sstream>
-#include <memory>
-#include <vector>
-#include <unordered_set>
-#include <unordered_map>
-#include <initializer_list>
-#include <iostream>
-#include <algorithm>
-#include <map>
-#include <stdexcept>
-#include <list>
-#include <utility>
-
-#include "src/gllo/common/visit.h"
-#include "mindspore/core/base/base.h"
-#include "utils/log_adapter.h"
-#include "base/base_ref.h"
-
-namespace mindspore {
-class CondVar;
-class SeqVar;
-using CondVarPtr = std::shared_ptr<CondVar>;
-using SVarPtr = std::shared_ptr<SeqVar>;
-const int kInvalidVarIndex = -2;
-
-using ConditionFunc = std::function<bool(const BaseRef &)>;
-
-// Base wildcard variable which could match any anf node.
-class Var : public Base {
-  friend class VarHasher;
-
- public:
-  explicit Var(std::string tag = "") : tag_(std::move(tag)), primitive_(nullptr) { EnsureTag(); }
-  explicit Var(const PrimitivePtr &primitive, std::string tag = "") : tag_(std::move(tag)), primitive_(primitive) {
-    EnsureTag();
-  }
-  Var(const Var &other) : Base(other), tag_(other.tag_) {}
-  virtual Var &operator=(const Var &other) {
-    if (&other == this) {
-      return *this;
-    }
-    this->tag_ = other.tag_;
-    return *this;
-  }
-  ~Var() override = default;
-  MS_DECLARE_PARENT(Var, Base);
-
-  virtual bool matches(const BaseRef &) { return true; }
-
-  virtual bool operator==(const Var &other) const { return tag_ == other.tag_; }
-  bool operator!=(const Var &other) const { return !(&other == this); }
-
-  std::string tag() const { return tag_; }
-  PrimitivePtr primitive() const { return primitive_; }
-  std::string ToString() const override {
-    std::ostringstream buffer;
-    buffer << "Var(" << tag_ << ")";
-    return buffer.str();
-  }
-  std::size_t hash() const override { return std::hash<std::string>()(tag_); }
-
- protected:
-  void EnsureTag();
-
-  std::string tag_;
-  PrimitivePtr primitive_;
-};
-
-// VarNode means variable node, a subclass of AnfNode
-class VarNode : public AnfNode {
- public:
-  VarNode(const VarPtr &value, const FuncGraphPtr &func_graph) : AnfNode(func_graph), var_(value) {}
-  ~VarNode() override = default;
-  MS_DECLARE_PARENT(VarNode, AnfNode);
-
-  const VarPtr var_;
-};
-using VarNodePtr = std::shared_ptr<VarNode>;
-
-class VarHasher {
- public:
-  std::size_t operator()(const Var &var) const { return var.hash(); }
-};
-
-// Condition Var, match an anf node when condition function return true.
-class CondVar : public Var {
- public:
-  explicit CondVar(const ConditionFunc &cond) : cond_fn_(cond) {}
-  ~CondVar() override = default;
-  MS_DECLARE_PARENT(CondVar, Var);
-  bool matches(const BaseRef &value) override {
-    // MS_LOG(DEBUG) << "CondVarPtr match: " + value.ToString();
-    if (utils::isa<Var>(value)) {
-      return false;
-    }
-    return cond_fn_(value);
-  }
-  ConditionFunc cond_fn_;
-};
-
-using Seq = VectorRef;
-using SeqPtr = std::shared_ptr<Seq>;
-
-// Sequence Var which could match multiple consecutive input nodes of a CNode.
-class SeqVar : public Var {
- public:
-  SeqVar() : subvar_(std::make_shared<Var>()) {}
-  ~SeqVar() override = default;
-  MS_DECLARE_PARENT(SeqVar, Var);
-  explicit SeqVar(const VarPtr subvar) : subvar_(subvar) {}
-  bool matches(const BaseRef &value) override {
-    // match Seq.
-    if (utils::isa<Seq>(value)) {
-      const Seq &seq = utils::cast<Seq>(value);
-      return std::all_of(seq.begin(), seq.end(), [this](const BaseRef &v) {
-        auto eq = subvar_->matches(v);
-        return eq;
-      });
-    }
-    return false;
-  }
-  bool operator==(const SeqVar &other) const { return *subvar_ == *other.subvar_; }
-  std::string ToString() const override;
-
- private:
-  VarPtr subvar_;
-};
-
-bool operator==(const VarPtr &lhs, const VarPtr &rhs);
-
-inline bool operator!=(const VarPtr &lhs, const VarPtr &rhs) { return !(lhs == rhs); }
-
-std::ostream &operator<<(std::ostream &os, const VarPtr &var);
-
-using Equiv = std::map<VarPtr, BaseRef>;
-using EquivPtr = std::shared_ptr<Equiv>;
-using PrimitiveVarMap = std::unordered_map<PrimitivePtr, VarPtr>;
-using PrimitiveVarMapPtr = std::shared_ptr<PrimitiveVarMap>;
-
-inline bool DefaultTypeEq(const BaseRef &x, const BaseRef &y) { return x.type() == y.type(); }
-
-class PatternEngine {
- public:
-  PatternEngine(const std::shared_ptr<Visitor> &visitor,
-                const std::function<bool(const BaseRef &, const BaseRef &)> &eq,
-                const std::function<bool(const BaseRef &, const BaseRef &)> &type_eq = DefaultTypeEq)
-      : visitor_(visitor), eq_(eq), type_eq_(type_eq) {}
-  ~PatternEngine() = default;
-
-  EquivPtr Match(const BaseRef &pattern, const BaseRef &expr, const PrimitiveVarMap &primitive_vars,
-                 EquivPtr equiv) const;
-  // Replace pattern with equivalent
-  BaseRef Replace(const BaseRef &pattern, const EquivPtr &equiv) const;
-
- private:
-  EquivPtr AlignSVar(const VectorRef &values_pattern, const VectorRef &values_expr,
-                     const PrimitiveVarMap &primitive_vars, EquivPtr equiv) const;
-  bool ToVector(const BaseRef &pattern, const BaseRef &expr, VectorRef *const values_pattern,
-                VectorRef *const values_expr) const;
-  bool ToVector(const VectorRef &pattern_ref, const VectorRef &expr_ref, VectorRef *const values_pattern,
-                VectorRef *const values_expr) const;
-  std::shared_ptr<Visitor> visitor_;
-  std::function<bool(const BaseRef &, const BaseRef &)> eq_;
-  std::function<bool(const BaseRef &, const BaseRef &)> type_eq_;
-};
-}  // namespace mindspore
-namespace std {
-using mindspore::ERROR;
-using mindspore::LogStream;
-using mindspore::NoExceptionType;
-template <>
-struct hash<mindspore::VarPtr> {
-  std::size_t operator()(const mindspore::VarPtr var) const {
-    if (var == nullptr) {
-      MS_LOG(ERROR) << "Invalid var ptr";
-      return 0;
-    }
-    return std::hash<std::string>{}(var->tag());
-  }
-};
-}  // namespace std
-#endif  // MINDSPORE_LITE_SRC_PASS_COMMON_PATTERN_ENGINE_H_
-

mindspore/lite/src/gllo/common/visit.cc

@@ -1,165 +0,0 @@
-/**
- * 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 <vector>
-#include <memory>
-#include <algorithm>
-#include <utility>
-
-#include "src/gllo/common/visit.h"
-#include "src/gllo/common/pattern_engine.h"
-#include "utils/any.h"
-#include "ir/anf.h"
-#include "ir/func_graph.h"
-#include "utils/log_adapter.h"
-
-
-namespace mindspore {
-bool CheckIfNeedExpand(const std::vector<BaseRef> &list) {
-  return std::any_of(list.begin(), list.end(), [](const BaseRef &any) { return utils::isa<Seq>(any); });
-}
-
-std::shared_ptr<VectorRef> ExpandList(const std::vector<BaseRef> &list) {
-  std::shared_ptr<VectorRef> new_list = std::make_shared<VectorRef>();
-  for (auto &item : list) {
-    if (utils::isa<Seq>(item)) {
-      const Seq &seq = utils::cast<Seq>(item);
-      new_list->insert(new_list->end(), seq.begin(), seq.end());
-    } else {
-      new_list->push_back(item);
-    }
-  }
-  return new_list;
-}
-
-bool DefaultVisitor::Visit(const VectorRef &v_any, BaseRef *const visit_out) const {
-  std::vector<BaseRef> out;
-  (void)std::transform(v_any.begin(), v_any.end(), std::back_inserter(out),
-                       [this](const BaseRef &item) { return fn_(item); });
-  if (visit_out != nullptr) {
-    *visit_out = ExpandList(out);
-  }
-  return true;
-}
-
-bool DefaultVisitor::Visit(const BaseRef &any, BaseRef *const visit_out) const {
-  if (utils::isa<Seq>(any)) {
-    return Visit(utils::cast<Seq>(any), visit_out);
-  } else if (utils::isa<AnfNodePtr>(any)) {
-    auto nodeptr = utils::cast<AnfNodePtr>(any);
-    AnfNodePtr output;
-    AnfNodePtr *p_output = &output;
-    if (visit_out == nullptr) {
-      p_output = nullptr;
-    }
-    Visit(nodeptr, fn_, p_output);
-    if (visit_out != nullptr) {
-      *visit_out = output;
-    }
-    return true;
-  }
-  MS_LOG(DEBUG) << "VisitError, not support type to Visit: " + any.ToString();
-  return false;
-}
-
-void DefaultVisitor::Visit(const AnfNodePtr &node, const VisitFn &fn, AnfNodePtr *output) const {
-  if (node->isa<CNode>()) {
-    Visit(node->cast<CNodePtr>(), fn, output);
-    return;
-  }
-
-  if (node->isa<ValueNode>()) {
-    Visit(node->cast<ValueNodePtr>(), fn, output);
-    return;
-  }
-
-  if (output != nullptr) {
-    *output = node;
-  }
-}
-
-void DefaultVisitor::Visit(const CNodePtr &cnode, const VisitFn &fn, AnfNodePtr *output) const {
-  // if output is nullptr, it's not required to make the new CNode node.
-  if (output == nullptr) {
-    for (auto &inp : cnode->inputs()) {
-      (void)fn(inp);
-    }
-
-    if (cnode->func_graph() != nullptr) {
-      (void)fn(cnode->func_graph());
-    } else {
-      (void)fn(cnode->func_graph_as_var());
-    }
-    return;
-  }
-
-  std::vector<AnfNodePtr> new_inputs;
-  std::vector<BaseRef> after_cnode_fn;
-  std::shared_ptr<VectorRef> out;
-  (void)std::transform(cnode->inputs().begin(), cnode->inputs().end(), std::back_inserter(after_cnode_fn), fn);
-  if (CheckIfNeedExpand(after_cnode_fn)) {
-    out = ExpandList(after_cnode_fn);
-  }
-
-  std::vector<BaseRef> &outs = after_cnode_fn;
-  if (out != nullptr) {
-    outs = out->elements();
-  }
-
-  for (auto &any_item : outs) {
-    if (!utils::isa<AnfNodePtr>(any_item)) {
-      MS_LOG(EXCEPTION) << "VisitError, fn not return the same type AnfNodePtr";
-    }
-    new_inputs.push_back(utils::cast<AnfNodePtr>(any_item));
-  }
-
-  BaseRef any_fg;
-  AnfNodePtr new_cnode = nullptr;
-  if (cnode->func_graph() != nullptr) {
-    any_fg = fn(cnode->func_graph());
-    if (!utils::isa<FuncGraphPtr>(any_fg)) {
-      MS_LOG(EXCEPTION) << "VisitError, fn not return the same type FuncGraphPtr";
-    }
-    new_cnode = std::make_shared<CNode>(new_inputs, utils::cast<FuncGraphPtr>(any_fg));
-  } else {
-    any_fg = fn(cnode->func_graph_as_var());
-    if (utils::isa<VarPtr>(any_fg)) {
-      new_cnode = std::make_shared<CNode>(new_inputs, utils::cast<VarPtr>(any_fg));
-    } else if (utils::isa<FuncGraphPtr>(any_fg)) {
-      new_cnode = std::make_shared<CNode>(new_inputs, utils::cast<FuncGraphPtr>(any_fg));
-    } else {
-      MS_LOG(EXCEPTION) << "VisitError, fn not return VarPtr or FuncGraphPtr";
-    }
-  }
-  new_cnode->set_abstract(cnode->abstract());
-  *output = new_cnode;
-}
-
-void DefaultVisitor::Visit(const ValueNodePtr &vnode, const VisitFn &fn, AnfNodePtr *output) const {
-  const BaseRef &value = utils::cast<ValuePtr>(fn(vnode->value()));
-  if (utils::isa<ValuePtr>(value)) {
-    if (output != nullptr) {
-      auto ct = NewValueNode(utils::cast<ValuePtr>(value));
-      ct->set_abstract(vnode->abstract());
-      *output = ct;
-    }
-    return;
-  }
-  MS_LOG(EXCEPTION) << "Visit result is not ValuePtr.";
-}
-}  // namespace mindspore
-

mindspore/lite/src/gllo/common/visit.h

@@ -1,59 +0,0 @@
-/**
- * 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.
- */
-
-#ifndef MINDSPORE_LIFT_SRC_PASS_COMMON_VISIT_H_
-#define MINDSPORE_LIFT_SRC_PASS_COMMON_VISIT_H_
-
-#include <unordered_map>
-#include <stdexcept>
-#include <list>
-#include <vector>
-#include <string>
-#include <memory>
-
-#include "mindspore/core/base/base.h"
-#include "base/base_ref.h"
-
-namespace mindspore {
-using VisitFn = std::function<BaseRef(const BaseRef &)>;
-
-class Visitor {
- public:
-  virtual void SetFn(VisitFn fn) = 0;
-  virtual bool Visit(const BaseRef &e, BaseRef *out) const = 0;
-  virtual bool Visit(const VectorRef &e, BaseRef *out) const = 0;
-  virtual ~Visitor() = default;
-};
-
-class DefaultVisitor : public Visitor {
- public:
-  DefaultVisitor() : fn_(nullptr) {}
-  ~DefaultVisitor() override = default;
-  void SetFn(VisitFn fn) override { fn_ = fn; };
-  bool Visit(const VectorRef &e, BaseRef *out) const override;
-  bool Visit(const BaseRef &e, BaseRef *out) const override;
-  void Visit(const AnfNodePtr &node, const VisitFn &fn, AnfNodePtr *output) const;
-  void Visit(const CNodePtr &cnode, const VisitFn &fn, AnfNodePtr *output) const;
-  void Visit(const ValueNodePtr &vnode, const VisitFn &fn, AnfNodePtr *output) const;
-
-  VisitFn fn_;
-};
-
-std::shared_ptr<VectorRef> ExpandList(const std::vector<BaseRef> &list);
-bool CheckIfNeedExpand(const std::vector<BaseRef> &list);
-}  // namespace mindspore
-#endif  // MINDSPORE_LIFT_SRC_PASS_COMMON_VISIT_H_
-

mindspore/lite/src/gllo/fusion/conv_activation_fusion.cc

@@ -14,12 +14,12 @@
  * limitations under the License.
  */
 
-#include "mindspore/lite/src/gllo/fusion/conv_activation_fusion.h"
+#include "src/gllo/fusion/conv_activation_fusion.h"
 #include <memory>
-#include "mindspore/lite/schema/inner/model_generated.h"
-#include "mindspore/lite/src/ir/primitive_t_value.h"
-#include "mindspore/ccsrc/utils/utils.h"
-#include "mindspore/lite/src/gllo/common/utils.h"
+#include "schema/inner/model_generated.h"
+#include "src/ir/primitive_t_value.h"
+#include "utils/utils.h"
+#include "src/gllo/common/gllo_utils.h"
 
 namespace mindspore::opt {
 namespace {

mindspore/lite/src/gllo/fusion/conv_activation_fusion.h

@@ -18,7 +18,7 @@
 #define MINDSPORE_LITE_SRC_PASS_FUSION_CONV_ACTIVATION_FUSION_H_
 
 #include <string>
-#include "mindspore/lite/src/gllo/common/optimizer.h"
+#include "src/gllo/common/optimizer.h"
 
 namespace mindspore {
 namespace opt {

mindspore/lite/src/gllo/fusion/conv_biasadd_fusion.cc

@@ -13,13 +13,13 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#include "mindspore/lite/src/gllo/fusion/conv_biasadd_fusion.h"
-#include <mindspore/lite/src/param_value_lite.h>
+#include "src/gllo/fusion/conv_biasadd_fusion.h"
 #include <memory>
-#include "mindspore/lite/schema/inner/model_generated.h"
-#include "mindspore/lite/src/ir/primitive_t_value.h"
-#include "mindspore/ccsrc/utils/utils.h"
-#include "mindspore/lite/src/gllo/common/utils.h"
+#include "src/param_value_lite.h"
+#include "schema/inner/model_generated.h"
+#include "src/ir/primitive_t_value.h"
+#include "utils/utils.h"
+#include "src/gllo/common/gllo_utils.h"
 #include "securec/include/securec.h"
 
 namespace mindspore::opt {
@@ -142,7 +142,7 @@ const AnfNodePtr ConvBiasaddFusion::Process(const FuncGraphPtr &func_graph, cons
   CheckIfCNodeIsNull(conv_node);
   GenConvNewBias(func_graph, conv_node, add_node);
   auto primitiveT_value = GetValueNode<std::shared_ptr<lite::PrimitiveTValue>>(conv_node->input(0));
-  MS_ASSERT(primitiveT_value);
+  MS_ASSERT(primitiveT_value != nullptr);
   auto type = primitiveT_value->GetPrimitiveT()->value.type;
   if (type == schema::PrimitiveType_Conv2D) {
     primitiveT_value->GetPrimitiveT()->value.AsConv2D()->hasBias = true;

mindspore/lite/src/gllo/fusion/conv_biasadd_fusion.h

@@ -17,7 +17,7 @@
 #ifndef MINDSPORE_LITE_SRC_PASS_FUSION_CONV_BIASADD_FUSION_H_
 #define MINDSPORE_LITE_SRC_PASS_FUSION_CONV_BIASADD_FUSION_H_
 
-#include "mindspore/lite/src/gllo/common/optimizer.h"
+#include "src/gllo/common/optimizer.h"
 
 namespace mindspore {
 namespace opt {

mindspore/lite/src/gllo/fusion/conv_bn_fusion.cc

@@ -14,13 +14,13 @@
  * limitations under the License.
  */
 
-#include "mindspore/lite/src/gllo/fusion/conv_bn_fusion.h"
-#include <mindspore/lite/src/param_value_lite.h>
+#include "src/gllo/fusion/conv_bn_fusion.h"
 #include <memory>
-#include "mindspore/lite/schema/inner/model_generated.h"
-#include "mindspore/lite/src/ir/primitive_t_value.h"
-#include "mindspore/ccsrc/utils/utils.h"
-#include "mindspore/lite/src/gllo/common/utils.h"
+#include "src/param_value_lite.h"
+#include "schema/inner/model_generated.h"
+#include "src/ir/primitive_t_value.h"
+#include "utils/utils.h"
+#include "src/gllo/common/gllo_utils.h"
 #include "securec/include/securec.h"
 
 namespace mindspore::opt {

mindspore/lite/src/gllo/fusion/conv_bn_fusion.h

@@ -17,7 +17,7 @@
 #ifndef MINDSPORE_LITE_SRC_PASS_FUSION_CONV_BN_FUSION_H_
 #define MINDSPORE_LITE_SRC_PASS_FUSION_CONV_BN_FUSION_H_
 
-#include "mindspore/lite/src/gllo/fusion/conv_transform_fusion.h"
+#include "src/gllo/fusion/conv_transform_fusion.h"
 
 namespace mindspore::opt {
 class ConvBatchNormFusion : public ConvTransformFusion {

mindspore/lite/src/gllo/fusion/conv_scale_fusion.cc

@@ -14,13 +14,13 @@
  * limitations under the License.
  */
 
-#include "mindspore/lite/src/gllo/fusion/conv_scale_fusion.h"
+#include "src/gllo/fusion/conv_scale_fusion.h"
 #include <memory>
-#include "mindspore/lite/src/param_value_lite.h"
-#include "mindspore/lite/schema/inner/model_generated.h"
-#include "mindspore/lite/src/ir/primitive_t_value.h"
-#include "mindspore/ccsrc/utils/utils.h"
-#include "mindspore/lite/src/gllo/common/utils.h"
+#include "src/param_value_lite.h"
+#include "schema/inner/model_generated.h"
+#include "src/ir/primitive_t_value.h"
+#include "utils/utils.h"
+#include "src/gllo/common/gllo_utils.h"
 #include "include/errorcode.h"
 #include "securec/include/securec.h"
 

mindspore/lite/src/gllo/fusion/conv_scale_fusion.h

@@ -17,7 +17,7 @@
 #ifndef MINDSPORE_LITE_SRC_PASS_FUSION_CONV_SCALE_FUSION_H_
 #define MINDSPORE_LITE_SRC_PASS_FUSION_CONV_SCALE_FUSION_H_
 
-#include "mindspore/lite/src/gllo/fusion/conv_transform_fusion.h"
+#include "src/gllo/fusion/conv_transform_fusion.h"
 
 namespace mindspore::opt {
 class ConvScaleFusion : public ConvTransformFusion {

mindspore/lite/src/gllo/fusion/conv_transform_fusion.cc

@@ -14,13 +14,13 @@
  * limitations under the License.
  */
 
-#include "mindspore/lite/src/gllo/fusion/conv_transform_fusion.h"
+#include "src/gllo/fusion/conv_transform_fusion.h"
 #include <memory>
-#include "mindspore/lite/src/param_value_lite.h"
-#include "mindspore/lite/schema/inner/model_generated.h"
-#include "mindspore/lite/src/ir/primitive_t_value.h"
-#include "mindspore/ccsrc/utils/utils.h"
-#include "mindspore/lite/src/gllo/common/utils.h"
+#include "src/param_value_lite.h"
+#include "schema/inner/model_generated.h"
+#include "src/ir/primitive_t_value.h"
+#include "utils/utils.h"
+#include "src/gllo/common/gllo_utils.h"
 #include "include/errorcode.h"
 #include "securec/include/securec.h"
 
@@ -78,6 +78,16 @@ const AnfNodePtr ConvTransformFusion::Process(const FuncGraphPtr &func_graph, co
   GenNewConvTensor(func_graph, conv_node, kernel_nums, trans_scale, trans_bias);
   delete[] trans_bias;
   delete[] trans_scale;
+  auto primitiveT_value = GetValueNode<std::shared_ptr<lite::PrimitiveTValue>>(conv_node->input(0));
+  MS_ASSERT(primitiveT_value != nullptr);
+  auto type = primitiveT_value->GetPrimitiveT()->value.type;
+  if (type == schema::PrimitiveType_Conv2D) {
+    primitiveT_value->GetPrimitiveT()->value.AsConv2D()->hasBias = true;
+  } else if (type == schema::PrimitiveType_DepthwiseConv2D) {
+    primitiveT_value->GetPrimitiveT()->value.AsDepthwiseConv2D()->hasBias = true;
+  } else {
+    MS_LOG(EXCEPTION) << "Unsupported opType, " << type;
+  }
   return pre_node;
 }
 

mindspore/lite/src/gllo/fusion/conv_transform_fusion.h

@@ -18,7 +18,7 @@
 #define MINDSPORE_LITE_SRC_PASS_FUSION_CONV_TRANSFORM_FUSION_H_
 
 #include <string>
-#include "mindspore/lite/src/gllo/common/optimizer.h"
+#include "src/gllo/common/optimizer.h"
 
 namespace mindspore::opt {
 class ConvTransformFusion : public PatternProcessPass {

mindspore/lite/test/CMakeLists.txt

@@ -63,6 +63,8 @@ if(BUILD_CONVERTER)
             ${CCSRC_DIR}/pybind_api/export_flags.cc
             ${CCSRC_DIR}/utils/context/context_extends.cc
             ${CCSRC_DIR}/frontend/parallel/costmodel_context.cc
+            ${CCSRC_DIR}/backend/optimizer/common/pattern_engine.cc
+            ${CCSRC_DIR}/backend/optimizer/common/visit.cc
             ${CMAKE_CURRENT_SOURCE_DIR}/../src/common/graph_utils_extends.cc
             )
 else()
@@ -202,12 +204,14 @@ if(BUILD_CONVERTER)
             ${LITE_DIR}/tools/converter/converter.cc
             ${LITE_DIR}/tools/converter/parser/onnx/onnx.pb.cc
             ${LITE_DIR}/test/st/converter_test.cc
+            ${LITE_DIR}/test/ut/src/gllo/fusion/conv_activation_fusion_test.cc
+            ${LITE_DIR}/test/ut/src/gllo/fusion/conv_biasadd_fusion_test.cc
+            ${LITE_DIR}/test/ut/src/gllo/fusion/conv_bn_fusion_test.cc
+            ${LITE_DIR}/test/ut/src/gllo/fusion/conv_scale_fusion_test.cc
             ${LITE_DIR}/src/gllo/common/node_pass.cc
             ${LITE_DIR}/src/gllo/common/optimizer.cc
             ${LITE_DIR}/src/gllo/common/pass_manager.cc
-            ${LITE_DIR}/src/gllo/common/pattern_engine.cc
-            ${LITE_DIR}/src/gllo/common/visit.cc
-            ${LITE_DIR}/src/gllo/common/utils.cc
+            ${LITE_DIR}/src/gllo/common/gllo_utils.cc
             ${LITE_DIR}/src/gllo/fusion/conv_biasadd_fusion.cc
             ${LITE_DIR}/src/gllo/fusion/conv_activation_fusion.cc
             ${LITE_DIR}/src/gllo/fusion/conv_transform_fusion.cc

mindspore/lite/test/ut/src/gllo/fusion/conv_activation_fusion_test.cc

@@ -0,0 +1,184 @@
+/**
+ * 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 <memory>
+#include "schema/inner/model_generated.h"
+#include "include/model.h"
+#include "common/common_test.h"
+#include "include/lite_session.h"
+#include "include/context.h"
+#include "include/errorcode.h"
+#include "utils/log_adapter.h"
+#include "tools/converter/model_parser.h"
+#include "tools/converter/anf_transform.h"
+#include "src/common/anf_exporter/anf_exporter.h"
+
+namespace mindspore {
+class ConvActivationFusionTest : public mindspore::Common {
+ public:
+  ConvActivationFusionTest() = default;
+};
+using MetaGraphTptr = std::shared_ptr<schema::MetaGraphT>;
+using CNodeTptr = std::unique_ptr<schema::CNodeT>;
+
+namespace {
+CNodeTptr BuildConv2D() {
+  auto convNode = std::make_unique<schema::CNodeT>();
+  convNode->inputIndex = {0, 1};
+  convNode->outputIndex = {2};
+  convNode->primitive = std::make_unique<schema::PrimitiveT>();
+  convNode->primitive->value.type = schema::PrimitiveType_Conv2D;
+  auto prim1 = new schema::Conv2DT;
+  prim1->padMode = schema::PadMode_SAME;
+  prim1->format = schema::Format_NHWC;
+  prim1->strideH = 1;
+  prim1->strideW = 1;
+  prim1->kernelH = 3;
+  prim1->kernelW = 3;
+  prim1->dilateH = 1;
+  prim1->dilateW = 1;
+  prim1->channelOut = 3;
+  convNode->primitive->value.value = prim1;
+  convNode->name = "Conv2D";
+  return convNode;
+}
+CNodeTptr BuildDepthwiseConv2D() {
+  auto convNode = std::make_unique<schema::CNodeT>();
+  convNode->inputIndex = {0, 1};
+  convNode->outputIndex = {2};
+  convNode->primitive = std::make_unique<schema::PrimitiveT>();
+  convNode->primitive->value.type = schema::PrimitiveType_DepthwiseConv2D;
+  auto prim1 = new schema::DepthwiseConv2DT;
+  prim1->padMode = schema::PadMode_SAME;
+  prim1->format = schema::Format_NHWC;
+  prim1->strideH = 1;
+  prim1->strideW = 1;
+  prim1->kernelH = 3;
+  prim1->kernelW = 3;
+  prim1->dilateH = 1;
+  prim1->dilateW = 1;
+  prim1->channelIn = 1;
+  prim1->channelMultiplier = 3;
+  convNode->primitive->value.value = prim1;
+  convNode->name = "Conv2D";
+  return convNode;
+}
+
+MetaGraphTptr BuildGraph(schema::PrimitiveType conv_type,
+                         schema::ActivationType activation_type) {
+  auto meta_graph = std::make_shared<schema::MetaGraphT>();
+  meta_graph->name = "graph";
+  // conv node
+  CNodeTptr convNode;
+  if (conv_type == schema::PrimitiveType_Conv2D) {
+    convNode = BuildConv2D();
+  } else {
+    convNode = BuildDepthwiseConv2D();
+  }
+  meta_graph->nodes.emplace_back(std::move(convNode));
+
+  // relu node
+  auto next_node = std::make_unique<schema::CNodeT>();
+  next_node->inputIndex = {2};
+  next_node->outputIndex = {3};
+  next_node->primitive = std::make_unique<schema::PrimitiveT>();
+  next_node->primitive->value.type = schema::PrimitiveType_Activation;
+  auto prim2 = new schema::ActivationT;
+  prim2->type = activation_type;
+  next_node->primitive->value.value = prim2;
+  next_node->name = "activation";
+  meta_graph->nodes.emplace_back(std::move(next_node));
+
+  meta_graph->inputIndex = {0};
+  meta_graph->outputIndex = {3};
+
+  // input 0: data
+  auto input0 = std::make_unique<schema::TensorT>();
+  input0->nodeType = schema::NodeType::NodeType_ValueNode;
+  input0->format = schema::Format_NHWC;
+  input0->dataType = TypeId::kNumberTypeFloat32;
+  input0->dims = {1, 5, 5, 3};
+  input0->offset = -1;
+  meta_graph->allTensors.emplace_back(std::move(input0));
+
+  // input 1: weight
+  auto input1 = std::make_unique<schema::TensorT>();
+  input1->nodeType = schema::NodeType::NodeType_ValueNode;
+  input1->format = schema::Format_KHWC;
+  input1->dataType = TypeId::kNumberTypeFloat32;
+  input1->dims = {8, 3, 3, 3};
+  input1->data.resize(sizeof(float) * 8 * 3 * 3 * 3);
+  meta_graph->allTensors.emplace_back(std::move(input1));
+
+  // conv output
+  auto conv_output = std::make_unique<schema::TensorT>();
+  conv_output->nodeType = schema::NodeType::NodeType_Parameter;
+  conv_output->format = schema::Format_NHWC;
+  conv_output->dataType = TypeId::kNumberTypeFloat32;
+  conv_output->dims = {1, 5, 5, 8};
+  meta_graph->allTensors.emplace_back(std::move(conv_output));
+
+  // final output
+  auto output = std::make_unique<schema::TensorT>();
+  output->nodeType = schema::NodeType::NodeType_Parameter;
+  output->format = schema::Format_NHWC;
+  output->dataType = TypeId::kNumberTypeFloat32;
+  output->dims = {1, 5, 5, 8};
+  meta_graph->allTensors.emplace_back(std::move(output));
+  return meta_graph;
+}
+}  //  namespace
+TEST_F(ConvActivationFusionTest, TestConvReluNode) {
+  auto meta_graph = BuildGraph(schema::PrimitiveType_Conv2D, schema::ActivationType_RELU);
+  auto func_graph = lite::ModelParser::Fb2Anf(meta_graph.get());
+  auto anf_transform = new lite::AnfTransform();
+  auto new_graph = anf_transform->Transform(func_graph);
+  ASSERT_NE(nullptr, new_graph);
+  auto new_meta_graph = lite::Export(new_graph);
+  ASSERT_EQ(new_meta_graph->nodes.size(), 1);
+  for (auto &cnode : new_meta_graph->nodes) {
+    ASSERT_EQ(cnode->primitive->value.AsConv2D()->activationType, schema::ActivationType_RELU);
+  }
+}
+
+TEST_F(ConvActivationFusionTest, TestConvRelu6Node) {
+  auto meta_graph = BuildGraph(schema::PrimitiveType_Conv2D, schema::ActivationType_RELU6);
+  auto func_graph = lite::ModelParser::Fb2Anf(meta_graph.get());
+  auto anf_transform = new lite::AnfTransform();
+  auto new_graph = anf_transform->Transform(func_graph);
+  ASSERT_NE(nullptr, new_graph);
+  auto new_meta_graph = lite::Export(new_graph);
+  ASSERT_EQ(new_meta_graph->nodes.size(), 1);
+  for (auto &cnode : new_meta_graph->nodes) {
+    ASSERT_EQ(cnode->primitive->value.AsConv2D()->activationType, schema::ActivationType_RELU6);
+  }
+}
+
+TEST_F(ConvActivationFusionTest, TestBadCase_ConvRelu) {
+  auto meta_graph = BuildGraph(schema::PrimitiveType_DepthwiseConv2D, schema::ActivationType_LEAKY_RELU);
+  auto func_graph = lite::ModelParser::Fb2Anf(meta_graph.get());
+  auto anf_transform = new lite::AnfTransform();
+  auto new_graph = anf_transform->Transform(func_graph);
+  ASSERT_NE(nullptr, new_graph);
+  auto new_meta_graph = lite::Export(new_graph);
+  ASSERT_EQ(new_meta_graph->nodes.size(), 2);
+  for (auto &cnode : new_meta_graph->nodes) {
+    if (cnode->primitive->value.type == schema::PrimitiveType_DepthwiseConv2D) {
+      ASSERT_EQ(cnode->primitive->value.AsDepthwiseConv2D()->activationType, schema::ActivationType_NO_ACTIVATION);
+    }
+  }
+}
+}  // namespace mindspore

mindspore/lite/test/ut/src/gllo/fusion/conv_biasadd_fusion_test.cc

@@ -0,0 +1,194 @@
+/**
+ * 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 <memory>
+#include "schema/inner/model_generated.h"
+#include "include/model.h"
+#include "common/common_test.h"
+#include "include/lite_session.h"
+#include "include/context.h"
+#include "include/errorcode.h"
+#include "utils/log_adapter.h"
+#include "tools/converter/model_parser.h"
+#include "tools/converter/anf_transform.h"
+#include "src/common/anf_exporter/anf_exporter.h"
+
+namespace mindspore {
+class ConvBiasAddFusionTest : public mindspore::Common {
+ public:
+  ConvBiasAddFusionTest() = default;
+};
+using MetaGraphTptr = std::shared_ptr<schema::MetaGraphT>;
+using CNodeTptr = std::unique_ptr<schema::CNodeT>;
+
+namespace {
+CNodeTptr BuildConv2D() {
+  auto convNode = std::make_unique<schema::CNodeT>();
+  convNode->inputIndex = {0, 1};
+  convNode->outputIndex = {2};
+  convNode->primitive = std::make_unique<schema::PrimitiveT>();
+  convNode->primitive->value.type = schema::PrimitiveType_Conv2D;
+  auto prim1 = new schema::Conv2DT;
+  prim1->padMode = schema::PadMode_SAME;
+  prim1->format = schema::Format_NHWC;
+  prim1->strideH = 1;
+  prim1->strideW = 1;
+  prim1->kernelH = 3;
+  prim1->kernelW = 3;
+  prim1->dilateH = 1;
+  prim1->dilateW = 1;
+  prim1->channelOut = 3;
+  convNode->primitive->value.value = prim1;
+  convNode->name = "Conv2D";
+  return convNode;
+}
+CNodeTptr BuildDepthwiseConv2D() {
+  auto convNode = std::make_unique<schema::CNodeT>();
+  convNode->inputIndex = {0, 1};
+  convNode->outputIndex = {2};
+  convNode->primitive = std::make_unique<schema::PrimitiveT>();
+  convNode->primitive->value.type = schema::PrimitiveType_DepthwiseConv2D;
+  auto prim1 = new schema::DepthwiseConv2DT;
+  prim1->padMode = schema::PadMode_SAME;
+  prim1->format = schema::Format_NHWC;
+  prim1->strideH = 1;
+  prim1->strideW = 1;
+  prim1->kernelH = 3;
+  prim1->kernelW = 3;
+  prim1->dilateH = 1;
+  prim1->dilateW = 1;
+  prim1->channelIn = 1;
+  prim1->channelMultiplier = 3;
+  convNode->primitive->value.value = prim1;
+  convNode->name = "Conv2D";
+  return convNode;
+}
+
+MetaGraphTptr BuildGraph(schema::PrimitiveType conv_type,
+                         schema::PrimitiveType add_type) {
+  auto meta_graph = std::make_shared<schema::MetaGraphT>();
+  meta_graph->name = "graph";
+  // conv node
+  CNodeTptr convNode;
+  if (conv_type == schema::PrimitiveType_Conv2D) {
+    convNode = BuildConv2D();
+  } else {
+    convNode = BuildDepthwiseConv2D();
+  }
+
+  meta_graph->nodes.emplace_back(std::move(convNode));
+
+  // biasadd node
+  auto biasadd_node = std::make_unique<schema::CNodeT>();
+  biasadd_node->inputIndex = {2, 3};
+  biasadd_node->outputIndex = {4};
+  biasadd_node->primitive = std::make_unique<schema::PrimitiveT>();
+  biasadd_node->primitive->value.type = add_type;
+  auto prim2 = new schema::BiasAddT;
+  biasadd_node->primitive->value.value = prim2;
+  biasadd_node->name = "BiasAdd";
+  meta_graph->nodes.emplace_back(std::move(biasadd_node));
+
+  meta_graph->inputIndex = {0};
+  meta_graph->outputIndex = {4};
+
+  // input 0: data
+  auto input0 = std::make_unique<schema::TensorT>();
+  input0->nodeType = schema::NodeType::NodeType_ValueNode;
+  input0->format = schema::Format_NHWC;
+  input0->dataType = TypeId::kNumberTypeFloat32;
+  input0->dims = {1, 5, 5, 3};
+  input0->offset = -1;
+  meta_graph->allTensors.emplace_back(std::move(input0));
+
+  // input 1: weight
+  auto input1 = std::make_unique<schema::TensorT>();
+  input1->nodeType = schema::NodeType::NodeType_ValueNode;
+  input1->format = schema::Format_KHWC;
+  input1->dataType = TypeId::kNumberTypeFloat32;
+  input1->dims = {8, 3, 3, 3};
+  input1->data.resize(sizeof(float) * 8 * 3 * 3 * 3);
+  meta_graph->allTensors.emplace_back(std::move(input1));
+
+  // conv output
+  auto conv_output = std::make_unique<schema::TensorT>();
+  conv_output->nodeType = schema::NodeType::NodeType_Parameter;
+  conv_output->format = schema::Format_NHWC;
+  conv_output->dataType = TypeId::kNumberTypeFloat32;
+  conv_output->dims = {1, 5, 5, 8};
+  meta_graph->allTensors.emplace_back(std::move(conv_output));
+
+  // input2: bias
+  auto input2 = std::make_unique<schema::TensorT>();
+  input2->nodeType = schema::NodeType::NodeType_ValueNode;
+  input2->format = schema::Format_NHWC;
+  input2->dataType = TypeId::kNumberTypeFloat32;
+  input2->dims = {1, 5, 5, 8};
+  input2->data.resize(sizeof(float) * 8 * 5 * 5);
+  meta_graph->allTensors.emplace_back(std::move(input2));
+
+  // final output
+  auto output = std::make_unique<schema::TensorT>();
+  output->nodeType = schema::NodeType::NodeType_Parameter;
+  output->format = schema::Format_NHWC;
+  output->dataType = TypeId::kNumberTypeFloat32;
+  output->dims = {1, 5, 5, 8};
+  meta_graph->allTensors.emplace_back(std::move(output));
+  return meta_graph;
+}
+}  //  namespace
+TEST_F(ConvBiasAddFusionTest, TestConvAddNode) {
+  auto meta_graph = BuildGraph(schema::PrimitiveType_Conv2D, schema::PrimitiveType_BiasAdd);
+  auto func_graph = lite::ModelParser::Fb2Anf(meta_graph.get());
+  auto anf_transform = new lite::AnfTransform();
+  auto new_graph = anf_transform->Transform(func_graph);
+  ASSERT_NE(nullptr, new_graph);
+  auto new_meta_graph = lite::Export(new_graph);
+  ASSERT_EQ(new_meta_graph->nodes.size(), 1);
+  for (auto &cnode : new_meta_graph->nodes) {
+    ASSERT_EQ(cnode->primitive->value.AsConv2D()->hasBias, true);
+  }
+  MS_LOG(INFO) << "Passed";
+}
+
+TEST_F(ConvBiasAddFusionTest, TestDeptiwiseConvAddNode) {
+  auto meta_graph = BuildGraph(schema::PrimitiveType_DepthwiseConv2D, schema::PrimitiveType_Add);
+  auto func_graph = lite::ModelParser::Fb2Anf(meta_graph.get());
+  auto anf_transform = new lite::AnfTransform();
+  auto new_graph = anf_transform->Transform(func_graph);
+  ASSERT_NE(nullptr, new_graph);
+  auto new_meta_graph = lite::Export(new_graph);
+  ASSERT_EQ(new_meta_graph->nodes.size(), 1);
+  for (auto &cnode : new_meta_graph->nodes) {
+    ASSERT_EQ(cnode->primitive->value.AsDepthwiseConv2D()->hasBias, true);
+  }
+}
+
+TEST_F(ConvBiasAddFusionTest, TestBadCase_ConvAdd) {
+  auto meta_graph = BuildGraph(schema::PrimitiveType_DepthwiseConv2D, schema::PrimitiveType_MatMul);
+  auto func_graph = lite::ModelParser::Fb2Anf(meta_graph.get());
+  auto anf_transform = new lite::AnfTransform();
+  auto new_graph = anf_transform->Transform(func_graph);
+  ASSERT_NE(nullptr, new_graph);
+  auto new_meta_graph = lite::Export(new_graph);
+  ASSERT_EQ(new_meta_graph->nodes.size(), 2);
+  for (auto &cnode : new_meta_graph->nodes) {
+    if (cnode->primitive->value.type == schema::PrimitiveType_DepthwiseConv2D) {
+      ASSERT_EQ(cnode->primitive->value.AsDepthwiseConv2D()->hasBias, false);
+    }
+  }
+}
+}  // namespace mindspore

mindspore/lite/test/ut/src/gllo/fusion/conv_bn_fusion_test.cc

@@ -0,0 +1,296 @@
+/**
+ * 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 <memory>
+#include "schema/inner/model_generated.h"
+#include "include/model.h"
+#include "common/common_test.h"
+#include "include/lite_session.h"
+#include "include/context.h"
+#include "include/errorcode.h"
+#include "mindspore/core/utils/log_adapter.h"
+#include "tools/converter/model_parser.h"
+#include "tools/converter/anf_transform.h"
+#include "src/common/anf_exporter/anf_exporter.h"
+
+namespace mindspore {
+class ConvBNFusionTest : public mindspore::Common {
+ public:
+  ConvBNFusionTest() = default;
+};
+using MetaGraphTptr = std::shared_ptr<schema::MetaGraphT>;
+using CNodeTptr = std::unique_ptr<schema::CNodeT>;
+
+namespace {
+CNodeTptr BuildConv2D() {
+  auto convNode = std::make_unique<schema::CNodeT>();
+  convNode->inputIndex = {0, 1};
+  convNode->outputIndex = {2};
+  convNode->primitive = std::make_unique<schema::PrimitiveT>();
+  convNode->primitive->value.type = schema::PrimitiveType_Conv2D;
+  auto prim1 = new schema::Conv2DT;
+  prim1->padMode = schema::PadMode_SAME;
+  prim1->format = schema::Format_NHWC;
+  prim1->strideH = 1;
+  prim1->strideW = 1;
+  prim1->kernelH = 3;
+  prim1->kernelW = 3;
+  prim1->dilateH = 1;
+  prim1->dilateW = 1;
+  prim1->channelOut = 3;
+  convNode->primitive->value.value = prim1;
+  convNode->name = "Conv2D";
+  return convNode;
+}
+CNodeTptr BuildDepthwiseConv2D() {
+  auto convNode = std::make_unique<schema::CNodeT>();
+  convNode->inputIndex = {0, 1, 2};
+  convNode->outputIndex = {3};
+  convNode->primitive = std::make_unique<schema::PrimitiveT>();
+  convNode->primitive->value.type = schema::PrimitiveType_DepthwiseConv2D;
+  auto prim1 = new schema::DepthwiseConv2DT;
+  prim1->padMode = schema::PadMode_SAME;
+  prim1->format = schema::Format_NHWC;
+  prim1->strideH = 1;
+  prim1->strideW = 1;
+  prim1->kernelH = 3;
+  prim1->kernelW = 3;
+  prim1->dilateH = 1;
+  prim1->dilateW = 1;
+  prim1->channelIn = 1;
+  prim1->channelMultiplier = 3;
+
+  convNode->primitive->value.value = prim1;
+  convNode->name = "Conv2D";
+  return convNode;
+}
+// caffe bn op has 3 inputs
+MetaGraphTptr BuildCaffeGraph(schema::PrimitiveType conv_type) {
+  auto meta_graph = std::make_shared<schema::MetaGraphT>();
+  meta_graph->name = "graph";
+  // conv node
+  CNodeTptr convNode;
+  if (conv_type == schema::PrimitiveType_Conv2D) {
+    convNode = BuildConv2D();
+  } else {
+    convNode = BuildDepthwiseConv2D();
+  }
+
+  meta_graph->nodes.emplace_back(std::move(convNode));
+
+  // bn_node
+  auto bn_node = std::make_unique<schema::CNodeT>();
+  bn_node->inputIndex = {2, 3, 4};
+  bn_node->outputIndex = {5};
+  bn_node->primitive = std::make_unique<schema::PrimitiveT>();
+  bn_node->primitive->value.type = schema::PrimitiveType_CaffeBatchNorm;
+  auto prim2 = new schema::CaffeBatchNormT;
+  bn_node->primitive->value.value = prim2;
+  bn_node->name = "bn";
+  meta_graph->nodes.emplace_back(std::move(bn_node));
+
+  // input 0: data
+  auto input0 = std::make_unique<schema::TensorT>();
+  input0->nodeType = schema::NodeType::NodeType_ValueNode;
+  input0->format = schema::Format_NHWC;
+  input0->dataType = TypeId::kNumberTypeFloat32;
+  input0->dims = {1, 5, 5, 3};
+  input0->offset = -1;
+  meta_graph->allTensors.emplace_back(std::move(input0));
+
+  // input 1: weight
+  auto input1 = std::make_unique<schema::TensorT>();
+  input1->nodeType = schema::NodeType::NodeType_ValueNode;
+  input1->format = schema::Format_KHWC;
+  input1->dataType = TypeId::kNumberTypeFloat32;
+  input1->dims = {8, 3, 3, 3};
+  input1->data.resize(sizeof(float) * 8 * 3 * 3 * 3);
+  meta_graph->allTensors.emplace_back(std::move(input1));
+
+  // conv output
+  auto conv_output = std::make_unique<schema::TensorT>();
+  conv_output->nodeType = schema::NodeType::NodeType_Parameter;
+  conv_output->format = schema::Format_NHWC;
+  conv_output->dataType = TypeId::kNumberTypeFloat32;
+  conv_output->dims = {1, 5, 5, 8};
+  meta_graph->allTensors.emplace_back(std::move(conv_output));
+
+  // caffe bn : mean
+  auto input2 = std::make_unique<schema::TensorT>();
+  input2->nodeType = schema::NodeType::NodeType_ValueNode;
+  input2->format = schema::Format_NHWC;
+  input2->dataType = TypeId::kNumberTypeFloat32;
+  input2->dims = {1, 5, 5, 8};
+  input2->data.resize(sizeof(float) * 8 * 5 * 5);
+  meta_graph->allTensors.emplace_back(std::move(input2));
+
+  // caffe bn : var
+  auto input3 = std::make_unique<schema::TensorT>();
+  input3->nodeType = schema::NodeType::NodeType_ValueNode;
+  input3->format = schema::Format_NHWC;
+  input3->dataType = TypeId::kNumberTypeFloat32;
+  input3->dims = {1, 5, 5, 8};
+  input3->data.resize(sizeof(float) * 8 * 5 * 5);
+  meta_graph->allTensors.emplace_back(std::move(input3));
+
+
+  // final bn output
+  auto output = std::make_unique<schema::TensorT>();
+  output->nodeType = schema::NodeType::NodeType_Parameter;
+  output->format = schema::Format_NHWC;
+  output->dataType = TypeId::kNumberTypeFloat32;
+  output->dims = {1, 5, 5, 8};
+  meta_graph->allTensors.emplace_back(std::move(output));
+  meta_graph->inputIndex = {0};
+  meta_graph->outputIndex = {5};
+  return meta_graph;
+}
+
+// tf bn op has 4 inputs
+MetaGraphTptr BuildTFGraph(schema::PrimitiveType conv_type) {
+  auto meta_graph = std::make_shared<schema::MetaGraphT>();
+  meta_graph->name = "graph";
+  // conv node
+  CNodeTptr convNode;
+  if (conv_type == schema::PrimitiveType_Conv2D) {
+    convNode = BuildConv2D();
+  } else {
+    convNode = BuildDepthwiseConv2D();
+  }
+
+  meta_graph->nodes.emplace_back(std::move(convNode));
+
+  // bn_node
+  auto bn_node = std::make_unique<schema::CNodeT>();
+  bn_node->inputIndex = {3, 4, 5, 6, 7};
+  bn_node->outputIndex = {8};
+  bn_node->primitive = std::make_unique<schema::PrimitiveT>();
+  bn_node->primitive->value.type = schema::PrimitiveType_FusedBatchNorm;
+  auto prim2 = new schema::FusedBatchNormT;
+  bn_node->primitive->value.value = prim2;
+  bn_node->name = "bn";
+  meta_graph->nodes.emplace_back(std::move(bn_node));
+
+  // input 0: data
+  auto input0 = std::make_unique<schema::TensorT>();
+  input0->nodeType = schema::NodeType::NodeType_ValueNode;
+  input0->format = schema::Format_NHWC;
+  input0->dataType = TypeId::kNumberTypeFloat32;
+  input0->dims = {1, 5, 5, 3};
+  input0->offset = -1;
+  meta_graph->allTensors.emplace_back(std::move(input0));
+
+
+  // input 1: conv_bias
+  auto input11 = std::make_unique<schema::TensorT>();
+  input11->nodeType = schema::NodeType::NodeType_ValueNode;
+  input11->format = schema::Format_KHWC;
+  input11->dataType = TypeId::kNumberTypeFloat32;
+  input11->dims = {8, 3, 3, 3};
+  input11->data.resize(sizeof(float) * 8 * 3 * 3 * 3);
+  meta_graph->allTensors.emplace_back(std::move(input11));
+
+  // input 1: weight
+  auto input1 = std::make_unique<schema::TensorT>();
+  input1->nodeType = schema::NodeType::NodeType_ValueNode;
+  input1->format = schema::Format_KHWC;
+  input1->dataType = TypeId::kNumberTypeFloat32;
+  input1->dims = {8, 3, 3, 3};
+  input1->data.resize(sizeof(float) * 8 * 3 * 3 * 3);
+  meta_graph->allTensors.emplace_back(std::move(input1));
+
+  // conv output
+  auto conv_output = std::make_unique<schema::TensorT>();
+  conv_output->nodeType = schema::NodeType::NodeType_Parameter;
+  conv_output->format = schema::Format_NHWC;
+  conv_output->dataType = TypeId::kNumberTypeFloat32;
+  conv_output->dims = {1, 5, 5, 8};
+  meta_graph->allTensors.emplace_back(std::move(conv_output));
+
+  // tflite bn : scale
+  auto input2 = std::make_unique<schema::TensorT>();
+  input2->nodeType = schema::NodeType::NodeType_ValueNode;
+  input2->format = schema::Format_NHWC;
+  input2->dataType = TypeId::kNumberTypeFloat32;
+  input2->dims = {1, 5, 5, 8};
+  input2->data.resize(sizeof(float) * 8 * 5 * 5);
+  meta_graph->allTensors.emplace_back(std::move(input2));
+
+  // tflite bn : bias
+  auto input3 = std::make_unique<schema::TensorT>();
+  input3->nodeType = schema::NodeType::NodeType_ValueNode;
+  input3->format = schema::Format_NHWC;
+  input3->dataType = TypeId::kNumberTypeFloat32;
+  input3->dims = {1, 5, 5, 8};
+  input3->data.resize(sizeof(float) * 8 * 5 * 5);
+  meta_graph->allTensors.emplace_back(std::move(input3));
+
+  // tflite bn : mean
+  auto input4 = std::make_unique<schema::TensorT>();
+  input4->nodeType = schema::NodeType::NodeType_ValueNode;
+  input4->format = schema::Format_NHWC;
+  input4->dataType = TypeId::kNumberTypeFloat32;
+  input4->dims = {1, 5, 5, 8};
+  input4->data.resize(sizeof(float) * 8 * 5 * 5);
+  meta_graph->allTensors.emplace_back(std::move(input4));
+
+  // tflite bn : var
+  auto input5 = std::make_unique<schema::TensorT>();
+  input5->nodeType = schema::NodeType::NodeType_ValueNode;
+  input5->format = schema::Format_NHWC;
+  input5->dataType = TypeId::kNumberTypeFloat32;
+  input5->dims = {1, 5, 5, 8};
+  input5->data.resize(sizeof(float) * 8 * 5 * 5);
+  meta_graph->allTensors.emplace_back(std::move(input5));
+
+  // final output
+  auto output = std::make_unique<schema::TensorT>();
+  output->nodeType = schema::NodeType::NodeType_Parameter;
+  output->format = schema::Format_NHWC;
+  output->dataType = TypeId::kNumberTypeFloat32;
+  output->dims = {1, 5, 5, 8};
+  meta_graph->allTensors.emplace_back(std::move(output));
+  meta_graph->inputIndex = {0};
+  meta_graph->outputIndex = {8};
+  return meta_graph;
+}
+}  //  namespace
+TEST_F(ConvBNFusionTest, TestConvAddNode) {
+  auto meta_graph = BuildCaffeGraph(schema::PrimitiveType_Conv2D);
+  auto func_graph = lite::ModelParser::Fb2Anf(meta_graph.get());
+  auto anf_transform = new lite::AnfTransform();
+  auto new_graph = anf_transform->Transform(func_graph);
+  ASSERT_NE(nullptr, new_graph);
+  auto new_meta_graph = lite::Export(new_graph);
+  ASSERT_EQ(new_meta_graph->nodes.size(), 1);
+  for (auto &cnode : new_meta_graph->nodes) {
+    ASSERT_EQ(cnode->primitive->value.AsConv2D()->hasBias, true);
+  }
+}
+
+TEST_F(ConvBNFusionTest, TestDeptiwiseConvAddNode) {
+  auto meta_graph = BuildTFGraph(schema::PrimitiveType_DepthwiseConv2D);
+  auto func_graph = lite::ModelParser::Fb2Anf(meta_graph.get());
+  auto anf_transform = new lite::AnfTransform();
+  auto new_graph = anf_transform->Transform(func_graph);
+  ASSERT_NE(nullptr, new_graph);
+  auto new_meta_graph = lite::Export(new_graph);
+  ASSERT_EQ(new_meta_graph->nodes.size(), 1);
+  for (auto &cnode : new_meta_graph->nodes) {
+    ASSERT_EQ(cnode->primitive->value.AsDepthwiseConv2D()->hasBias, true);
+  }
+}
+}  // namespace mindspore

mindspore/lite/test/ut/src/gllo/fusion/conv_scale_fusion_test.cc

@@ -0,0 +1,221 @@
+/**
+ * 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 <memory>
+#include "schema/inner/model_generated.h"
+#include "include/model.h"
+#include "common/common_test.h"
+#include "include/lite_session.h"
+#include "include/context.h"
+#include "include/errorcode.h"
+#include "utils/log_adapter.h"
+#include "tools/converter/model_parser.h"
+#include "tools/converter/anf_transform.h"
+#include "src/common/anf_exporter/anf_exporter.h"
+
+namespace mindspore {
+class ConvScaleFusionTest : public mindspore::Common {
+ public:
+  ConvScaleFusionTest() = default;
+};
+using MetaGraphTptr = std::shared_ptr<schema::MetaGraphT>;
+using CNodeTptr = std::unique_ptr<schema::CNodeT>;
+
+namespace {
+// conv has 2 inputs
+CNodeTptr BuildConv2D(int with_bias_flag) {
+  auto convNode = std::make_unique<schema::CNodeT>();
+  if (with_bias_flag) {
+    convNode->inputIndex = {0, 1, 2};
+    convNode->outputIndex = {3};
+  } else {
+    convNode->inputIndex = {0, 1};
+    convNode->outputIndex = {2};
+  }
+  convNode->primitive = std::make_unique<schema::PrimitiveT>();
+  convNode->primitive->value.type = schema::PrimitiveType_Conv2D;
+  auto prim1 = new schema::Conv2DT;
+  prim1->padMode = schema::PadMode_SAME;
+  prim1->format = schema::Format_NHWC;
+  prim1->strideH = 1;
+  prim1->strideW = 1;
+  prim1->kernelH = 3;
+  prim1->kernelW = 3;
+  prim1->dilateH = 1;
+  prim1->dilateW = 1;
+  prim1->channelOut = 3;
+  convNode->primitive->value.value = prim1;
+  convNode->name = "Conv2D";
+  return convNode;
+}
+// conv2d has 3 inputs
+CNodeTptr BuildDepthwiseConv2D(int with_bias_flag) {
+  auto convNode = std::make_unique<schema::CNodeT>();
+  if (with_bias_flag) {
+    convNode->inputIndex = {0, 1, 2};
+    convNode->outputIndex = {3};
+  } else {
+    convNode->inputIndex = {0, 1};
+    convNode->outputIndex = {2};
+  }
+  convNode->primitive = std::make_unique<schema::PrimitiveT>();
+  convNode->primitive->value.type = schema::PrimitiveType_DepthwiseConv2D;
+  auto prim1 = new schema::DepthwiseConv2DT;
+  prim1->padMode = schema::PadMode_SAME;
+  prim1->format = schema::Format_NHWC;
+  prim1->strideH = 1;
+  prim1->strideW = 1;
+  prim1->kernelH = 3;
+  prim1->kernelW = 3;
+  prim1->dilateH = 1;
+  prim1->dilateW = 1;
+  prim1->channelIn = 1;
+  prim1->channelMultiplier = 3;
+
+  convNode->primitive->value.value = prim1;
+  convNode->name = "Conv2D";
+  return convNode;
+}
+
+MetaGraphTptr BuildGraph(schema::PrimitiveType conv_type, bool conv_with_bias) {
+  auto meta_graph = std::make_shared<schema::MetaGraphT>();
+  meta_graph->name = "graph";
+  // conv node
+  CNodeTptr convNode;
+  if (conv_type == schema::PrimitiveType_Conv2D) {
+    convNode = BuildConv2D(conv_with_bias);
+  } else {
+    convNode = BuildDepthwiseConv2D(conv_with_bias);
+  }
+
+  meta_graph->nodes.emplace_back(std::move(convNode));
+
+  // scale_node weight bias
+  auto scale_node = std::make_unique<schema::CNodeT>();
+  if (conv_with_bias) {
+    scale_node->inputIndex = {3, 4, 5};
+    scale_node->outputIndex = {6};
+  } else {
+    scale_node->inputIndex = {2, 3, 4};
+    scale_node->outputIndex = {5};
+  }
+
+  scale_node->primitive = std::make_unique<schema::PrimitiveT>();
+  scale_node->primitive->value.type = schema::PrimitiveType_Scale;
+  auto prim2 = new schema::ScaleT;
+  scale_node->primitive->value.value = prim2;
+  scale_node->name = "scale";
+  meta_graph->nodes.emplace_back(std::move(scale_node));
+
+  // input 0: data
+  auto input0 = std::make_unique<schema::TensorT>();
+  input0->nodeType = schema::NodeType::NodeType_ValueNode;
+  input0->format = schema::Format_NHWC;
+  input0->dataType = TypeId::kNumberTypeFloat32;
+  input0->dims = {1, 5, 5, 3};
+  input0->offset = -1;
+  meta_graph->allTensors.emplace_back(std::move(input0));
+
+  // input 1: weight
+  auto input1 = std::make_unique<schema::TensorT>();
+  input1->nodeType = schema::NodeType::NodeType_ValueNode;
+  input1->format = schema::Format_KHWC;
+  input1->dataType = TypeId::kNumberTypeFloat32;
+  input1->dims = {8, 3, 3, 3};
+  input1->data.resize(sizeof(float) * 8 * 3 * 3 * 3);
+  meta_graph->allTensors.emplace_back(std::move(input1));
+
+  if (conv_with_bias) {
+    // input 00: bias
+    auto input00 = std::make_unique<schema::TensorT>();
+    input00->nodeType = schema::NodeType::NodeType_ValueNode;
+    input00->format = schema::Format_NHWC;
+    input00->dataType = TypeId::kNumberTypeFloat32;
+    input00->dims = {1, 5, 5, 3};
+    input00->offset = -1;
+    meta_graph->allTensors.emplace_back(std::move(input00));
+  }
+
+  // conv output
+  auto conv_output = std::make_unique<schema::TensorT>();
+  conv_output->nodeType = schema::NodeType::NodeType_Parameter;
+  conv_output->format = schema::Format_NHWC;
+  conv_output->dataType = TypeId::kNumberTypeFloat32;
+  conv_output->dims = {1, 5, 5, 8};
+  meta_graph->allTensors.emplace_back(std::move(conv_output));
+
+  // scale weight input
+  auto input2 = std::make_unique<schema::TensorT>();
+  input2->nodeType = schema::NodeType::NodeType_ValueNode;
+  input2->format = schema::Format_NHWC;
+  input2->dataType = TypeId::kNumberTypeFloat32;
+  input2->dims = {1, 5, 5, 8};
+  input2->data.resize(sizeof(float) * 8 * 5 * 5);
+  meta_graph->allTensors.emplace_back(std::move(input2));
+
+  // scale bias input
+  auto input3 = std::make_unique<schema::TensorT>();
+  input3->nodeType = schema::NodeType::NodeType_ValueNode;
+  input3->format = schema::Format_NHWC;
+  input3->dataType = TypeId::kNumberTypeFloat32;
+  input3->dims = {1, 5, 5, 8};
+  input3->data.resize(sizeof(float) * 8 * 5 * 5);
+  meta_graph->allTensors.emplace_back(std::move(input3));
+
+  // final scale output
+  auto output = std::make_unique<schema::TensorT>();
+  output->nodeType = schema::NodeType::NodeType_Parameter;
+  output->format = schema::Format_NHWC;
+  output->dataType = TypeId::kNumberTypeFloat32;
+  output->dims = {1, 5, 5, 8};
+  meta_graph->allTensors.emplace_back(std::move(output));
+  if (conv_with_bias) {
+    meta_graph->inputIndex = {0};
+    meta_graph->outputIndex = {6};
+  } else {
+    meta_graph->inputIndex = {0};
+    meta_graph->outputIndex = {5};
+  }
+  return meta_graph;
+}
+}  //  namespace
+TEST_F(ConvScaleFusionTest, TestConvScaleNode) {
+  auto meta_graph = BuildGraph(schema::PrimitiveType_Conv2D, true);
+  auto func_graph = lite::ModelParser::Fb2Anf(meta_graph.get());
+  auto anf_transform = new lite::AnfTransform();
+  auto new_graph = anf_transform->Transform(func_graph);
+  ASSERT_NE(nullptr, new_graph);
+  auto new_meta_graph = lite::Export(new_graph);
+  ASSERT_EQ(new_meta_graph->nodes.size(), 1);
+  for (auto &cnode : new_meta_graph->nodes) {
+    ASSERT_EQ(cnode->primitive->value.AsConv2D()->hasBias, true);
+  }
+}
+
+TEST_F(ConvScaleFusionTest, TestDeptiwiseConvScaleNode) {
+  auto meta_graph = BuildGraph(schema::PrimitiveType_DepthwiseConv2D, false);
+  auto func_graph = lite::ModelParser::Fb2Anf(meta_graph.get());
+  auto anf_transform = new lite::AnfTransform();
+  auto new_graph = anf_transform->Transform(func_graph);
+  ASSERT_NE(nullptr, new_graph);
+  auto new_meta_graph = lite::Export(new_graph);
+  ASSERT_EQ(new_meta_graph->nodes.size(), 1);
+  for (auto &cnode : new_meta_graph->nodes) {
+    ASSERT_EQ(cnode->primitive->value.AsDepthwiseConv2D()->hasBias, true);
+    ASSERT_EQ(cnode->inputIndex.size(), 3);
+  }
+}
+}  // namespace mindspore

mindspore/lite/tools/converter/CMakeLists.txt

@@ -49,6 +49,8 @@ set(ANF_SRC
         ${CCSRC_DIR}/pybind_api/export_flags.cc
         ${CCSRC_DIR}/utils/context/context_extends.cc
         ${CCSRC_DIR}/frontend/parallel/costmodel_context.cc
+        ${CCSRC_DIR}/backend/optimizer/common/pattern_engine.cc
+        ${CCSRC_DIR}/backend/optimizer/common/visit.cc
         ${CMAKE_CURRENT_SOURCE_DIR}/../../src/common/graph_utils_extends.cc
         )
 
@@ -75,9 +77,7 @@ file(GLOB_RECURSE CONVERTER_SRC RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}
         ${CMAKE_CURRENT_SOURCE_DIR}/../../src/gllo/common/node_pass.cc
         ${CMAKE_CURRENT_SOURCE_DIR}/../../src/gllo/common/optimizer.cc
         ${CMAKE_CURRENT_SOURCE_DIR}/../../src/gllo/common/pass_manager.cc
-        ${CMAKE_CURRENT_SOURCE_DIR}/../../src/gllo/common/pattern_engine.cc
-        ${CMAKE_CURRENT_SOURCE_DIR}/../../src/gllo/common/visit.cc
-        ${CMAKE_CURRENT_SOURCE_DIR}/../../src/gllo/common/utils.cc
+        ${CMAKE_CURRENT_SOURCE_DIR}/../../src/gllo/common/gllo_utils.cc
         ${CMAKE_CURRENT_SOURCE_DIR}/../../src/gllo/fusion/conv_biasadd_fusion.cc
         ${CMAKE_CURRENT_SOURCE_DIR}/../../src/gllo/fusion/conv_activation_fusion.cc
         ${CMAKE_CURRENT_SOURCE_DIR}/../../src/gllo/fusion/conv_transform_fusion.cc

mindspore/lite/tools/converter/converter.cc

@@ -90,7 +90,7 @@ MetaGraphT *Converter::Convert(const converter::Flags *flag) {
     return nullptr;
   }
 
-//  auto newGraph = anfTransform->Transform(graph);
+  graph = anfTransform->Transform(graph);
 
   CreateQuantizer(graph, flag);
   if (mQuantizer != nullptr) {

mindspore/lite/tools/converter/graphdef_transform.cc

@@ -100,20 +100,20 @@ int GraphDefTransform::Transform(const converter::Flags &ctx) {
   //  }
 
   // fusion
-  {
-    Optimizer fusionOptimizer;
-    fusionOptimizer.AddPass(new (std::nothrow) ConvBiasAddFusionPass());
-    fusionOptimizer.AddPass(new (std::nothrow) ConvBNFusionPass());
-    fusionOptimizer.AddPass(new (std::nothrow) ConvScaleFusionPass());
-    fusionOptimizer.AddPass(new (std::nothrow) ConvReluFusionPass());
-    fusionOptimizer.AddPass(new (std::nothrow) ConvRelu6FusionPass());
-    fusionOptimizer.AddPass(new (std::nothrow) IsolatedNodeRemovePass());
-    status = fusionOptimizer.Run(graphDefT);
-    if (status != RET_OK && status != RET_NO_CHANGE) {
-      MS_LOG(ERROR) << "Run fusionOptimizer graphPasses Failed";
-      return status;
-    }
-  }
+  // {
+  //   Optimizer fusionOptimizer;
+  //   fusionOptimizer.AddPass(new (std::nothrow) ConvBiasAddFusionPass());
+  //   fusionOptimizer.AddPass(new (std::nothrow) ConvBNFusionPass());
+  //   fusionOptimizer.AddPass(new (std::nothrow) ConvScaleFusionPass());
+  //   fusionOptimizer.AddPass(new (std::nothrow) ConvReluFusionPass());
+  //   fusionOptimizer.AddPass(new (std::nothrow) ConvRelu6FusionPass());
+  //   fusionOptimizer.AddPass(new (std::nothrow) IsolatedNodeRemovePass());
+  //   status = fusionOptimizer.Run(graphDefT);
+  //   if (status != RET_OK && status != RET_NO_CHANGE) {
+  //     MS_LOG(ERROR) << "Run fusionOptimizer graphPasses Failed";
+  //     return status;
+  //   }
+  // }
 
   // weight format trans
   if (ctx.formatTrans) {