!5181 Python pass pattern renaming and interface tweaking

Merge pull request !5181 from BowenK/new_parameter

mindspore/ccsrc/frontend/optimizer/pattern.cc

@@ -21,25 +21,23 @@ namespace opt {
 namespace python_pass {
 int Pattern::g_id_ = 0;
 
-MatchResultPtr IsPrimTypeOf::match(const AnfNodePtr &node) {
+MatchResultPtr Prim::match(const AnfNodePtr &node) {
   if (!IsValueNode<Primitive>(node)) {
     return nullptr;
   }
   MatchResultPtr res = std::make_shared<MatchResult>();
-  if (IsValueNode<Primitive>(node)) {
-    // iterate over all primitives
-    for (auto &iter : primitives_) {
-      if (IsPrimitive(node, iter) || iter->name() == "*") {
-        matched_prim_ = iter;
-        res->add_entry(shared_from_base<IsPrimTypeOf>(), node);
-        return res;
-      }
+  // iterate over all primitives
+  for (auto &iter : primitives_) {
+    if (IsPrimitive(node, iter) || iter->name() == "*") {
+      matched_prim_ = iter;
+      res->add_entry(shared_from_base<Prim>(), node);
+      return res;
     }
   }
   return nullptr;
 }
 
-MatchResultPtr CallWith::match(const AnfNodePtr &node) {
+MatchResultPtr Call::match(const AnfNodePtr &node) {
   if (!IsPrimitiveCNode(node)) {
     return nullptr;
   }
@@ -71,7 +69,7 @@ MatchResultPtr CallWith::match(const AnfNodePtr &node) {
   }
   // If inputs is not specified, add node without looking into its inputs
   if (p_inputs_size == 0) {
-    res->add_entry(shared_from_base<CallWith>(), cnode->input(0));
+    res->add_entry(shared_from_base<Call>(), cnode->input(0));
     return res;
   }
   bool failed = false;
@@ -86,24 +84,24 @@ MatchResultPtr CallWith::match(const AnfNodePtr &node) {
     res->merge(input_match_result);
   }
   if (!failed) {
-    res->add_entry(shared_from_base<CallWith>(), cnode->input(0));
+    res->add_entry(shared_from_base<Call>(), cnode->input(0));
     return res;
   }
   return nullptr;
 }
 
-MatchResultPtr IsIn::match(const AnfNodePtr &node) {
+MatchResultPtr OneOf::match(const AnfNodePtr &node) {
   for (auto &iter : patterns_) {
     auto res = iter->match(node);
     if (res != nullptr) {
-      res->add_entry(shared_from_base<IsIn>(), node);
+      res->add_entry(shared_from_base<OneOf>(), node);
       return res;
     }
   }
   return nullptr;
 }
 
-MatchResultPtr IsNot::match(const AnfNodePtr &node) {
+MatchResultPtr NoneOf::match(const AnfNodePtr &node) {
   for (auto &iter : patterns_) {
     auto res = iter->match(node);
     if (res != nullptr) {
@@ -111,16 +109,33 @@ MatchResultPtr IsNot::match(const AnfNodePtr &node) {
     }
   }
   auto res = std::make_shared<MatchResult>();
-  res->add_entry(shared_from_base<IsNot>(), node);
+  res->add_entry(shared_from_base<NoneOf>(), node);
   return res;
 }
 
-MatchResultPtr AnyPattern::match(const AnfNodePtr &node) {
+MatchResultPtr Any::match(const AnfNodePtr &node) {
   MatchResultPtr res = std::make_shared<MatchResult>();
-  res->add_entry(shared_from_base<AnyPattern>(), node);
+  res->add_entry(shared_from_base<Any>(), node);
   return res;
 }
 
+MatchResultPtr Imm::match(const AnfNodePtr &node) {
+  if (!IsValueNode<Int32Imm>(node)) {
+    return nullptr;
+  }
+  // Check value
+  auto value_node = node->cast<ValueNodePtr>();
+  MS_EXCEPTION_IF_NULL(value_node);
+  auto value_ptr = value_node->value()->cast<Int32ImmPtr>();
+  MS_EXCEPTION_IF_NULL(value_ptr);
+  if ((int32_t)value_ptr->value() == value_) {
+    MatchResultPtr res = std::make_shared<MatchResult>();
+    res->add_entry(shared_from_base<Imm>(), node);
+    return res;
+  }
+  return nullptr;
+}
+
 AnfNodePtr MatchResult::get_node(const PatternPtr &pattern) {
   auto entry = match_result_.find(pattern);
   if (entry == match_result_.end()) {
@@ -140,20 +155,20 @@ void MatchResult::merge(const MatchResultPtr &other_result) {
 REGISTER_PYBIND_DEFINE(
   Pattern, ([](const py::module *m) {
     (void)py::class_<Pattern, std::shared_ptr<Pattern>>(*m, "Pattern").def(py::init<>());
-    (void)py::class_<IsIn, std::shared_ptr<IsIn>, Pattern>(*m, "IsIn_").def(py::init<vector<PatternPtr>>());
-    (void)py::class_<IsPrimTypeOf, std::shared_ptr<IsPrimTypeOf>, Pattern>(*m, "IsPrimTypeOf_", py::dynamic_attr())
-      .def(py::init<vector<PrimitivePyPtr>, string, bool>())
-      .def(py::init<vector<string>, string, bool>());
-    (void)py::class_<CallWith, std::shared_ptr<CallWith>, Pattern>(*m, "CallWith_")
-      .def(py::init<PatternPtr, vector<PatternPtr>, bool>())
-      .def(py::init<PrimitivePyPtr, vector<PatternPtr>, bool>())
-      .def(py::init<string, vector<PatternPtr>, bool>());
-    (void)py::class_<IsNot, std::shared_ptr<IsNot>, Pattern>(*m, "IsNot_").def(py::init<vector<PatternPtr>>());
-    (void)py::class_<AnyPattern, std::shared_ptr<AnyPattern>, Pattern>(*m, "AnyPattern").def(py::init<>());
+    (void)py::class_<OneOf, std::shared_ptr<OneOf>, Pattern>(*m, "OneOf_").def(py::init<vector<PatternPtr>>());
+    (void)py::class_<Prim, std::shared_ptr<Prim>, Pattern>(*m, "Prim_", py::dynamic_attr())
+      .def(py::init<vector<PrimitivePyPtr>, string>())
+      .def(py::init<vector<string>, string>());
+    (void)py::class_<Call, std::shared_ptr<Call>, Pattern>(*m, "Call_")
+      .def(py::init<PatternPtr, vector<PatternPtr>>())
+      .def(py::init<PrimitivePyPtr, vector<PatternPtr>>())
+      .def(py::init<string, vector<PatternPtr>>());
+    (void)py::class_<NoneOf, std::shared_ptr<NoneOf>, Pattern>(*m, "NoneOf_").def(py::init<vector<PatternPtr>>());
+    (void)py::class_<Any, std::shared_ptr<Any>, Pattern>(*m, "Any").def(py::init<>());
     (void)py::class_<NewTensor, std::shared_ptr<NewTensor>, Pattern>(*m, "NewTensor_")
       .def(py::init<tensor::TensorPtr>());
     (void)py::class_<NewParameter, std::shared_ptr<NewParameter>, Pattern>(*m, "NewParameter_")
-      .def(py::init<string, tensor::TensorPtr, bool, bool, bool>());
+      .def(py::init<string, tensor::TensorPtr, bool, bool>());
     (void)py::class_<Imm, std::shared_ptr<Imm>, Pattern>(*m, "Imm").def(py::init<int>());
   }));
 }  // namespace python_pass

mindspore/ccsrc/frontend/optimizer/pattern.h

@@ -36,10 +36,10 @@ class MatchResult;
 using MatchResultPtr = std::shared_ptr<MatchResult>;
 class Pattern;
 using PatternPtr = std::shared_ptr<Pattern>;
-class IsPrimTypeOf;
-using IsPrimTypeOfPtr = std::shared_ptr<IsPrimTypeOf>;
-class CallWith;
-using CallWithPtr = std::shared_ptr<CallWith>;
+class Prim;
+using PrimPtr = std::shared_ptr<Prim>;
+class Call;
+using CallPtr = std::shared_ptr<Call>;
 class NewTensor;
 using NewTensorPtr = std::shared_ptr<NewTensor>;
 class NewParameter;
@@ -58,8 +58,6 @@ class Pattern : public Base {
   virtual bool operator==(const Pattern &other) const { return unique_name_ == other.unique_name_; }
   string unique_name() const { return unique_name_; }
   vector<PatternPtr> inputs() { return inputs_; }
-  bool should_replace() { return should_replace_; }
-  void set_should_replace(bool should_replace) { should_replace_ = should_replace; }
   virtual void reset() {}
 
  protected:
@@ -67,7 +65,6 @@ class Pattern : public Base {
   // NOTE: To ensure uniqueness of the name, raise g_id_ by 1 every time a pattern got constructed
   string unique_name_;
   vector<PatternPtr> inputs_;
-  bool should_replace_ = true;
 };
 
 struct PatternEqual {
@@ -85,70 +82,61 @@ struct PatternHasher {
   }
 };
 
-class IsPrimTypeOf : public Pattern {
+class Prim : public Pattern {
  public:
-  IsPrimTypeOf() { unique_name_ = std::to_string(g_id_++); }
-  ~IsPrimTypeOf() = default;
-  IsPrimTypeOf(vector<PrimitivePyPtr> prims, string name, bool should_replace)
-      : primitives_(prims), name_(name), matched_prim_(nullptr) {
-    unique_name_ = std::to_string(g_id_++) + "IsPrimTypeOf_" + name;
-    should_replace_ = should_replace;
-    if (!should_replace) {
-      matched_prim_ = prims[0];
-    }
+  Prim() { unique_name_ = std::to_string(g_id_++); }
+  ~Prim() = default;
+  Prim(vector<PrimitivePyPtr> prims, string name) : primitives_(prims), name_(name) {
+    unique_name_ = std::to_string(g_id_++) + "Prim_" + name;
+    // Default using the first prim to build target
+    matched_prim_ = primitives_[0];
   }
-  IsPrimTypeOf(vector<string> types, string name, bool should_replace) : types_(types), name_(name) {
-    unique_name_ = std::to_string(g_id_++) + "IsPrimTypeOf_" + name;
+  Prim(vector<string> types, string name) : types_(types), name_(name) {
+    unique_name_ = std::to_string(g_id_++) + "Prim_" + name;
     // Make primitives_
     for (auto &iter : types) {
       primitives_.push_back(std::make_shared<PrimitivePy>(iter, py::cast(nullptr)));
     }
-    should_replace_ = should_replace;
-    if (!should_replace) {
-      matched_prim_ = primitives_[0];
-    }
+    // Default using the first prim to build target
+    matched_prim_ = primitives_[0];
   }
-  MS_DECLARE_PARENT(IsPrimTypeOf, Pattern);
+  MS_DECLARE_PARENT(Prim, Pattern);
   MatchResultPtr match(const AnfNodePtr &node) override;
   PrimitivePyPtr matched_primitive() { return matched_prim_; }
   void reset() override {
-    if (should_replace_) {
-      matched_prim_ = nullptr;
-    }
+    // Init before reset
+    MS_EXCEPTION_IF_NULL(matched_prim_);
+    matched_prim_ = primitives_[0];
   }
 
  private:
   vector<string> types_;
   vector<PrimitivePyPtr> primitives_;
   string name_;
-  PrimitivePyPtr matched_prim_;
+  PrimitivePyPtr matched_prim_{nullptr};
 };
 
-class CallWith : public Pattern {
+class Call : public Pattern {
  public:
-  CallWith() { unique_name_ = std::to_string(g_id_++); }
-  ~CallWith() = default;
-  CallWith(PatternPtr prim_pattern, vector<PatternPtr> inputs, bool should_replace) {
+  Call() { unique_name_ = std::to_string(g_id_++); }
+  ~Call() = default;
+  Call(PatternPtr prim_pattern, vector<PatternPtr> inputs) {
     // NOTE: should_replace is ignored in this case, since each sub-pattern has its own setting
     prim_pattern_ = prim_pattern;
-    unique_name_ = std::to_string(g_id_++) + "CallWithPattern_" + prim_pattern->unique_name();
+    unique_name_ = std::to_string(g_id_++) + "Call_" + prim_pattern->unique_name();
     inputs_ = inputs;
-    // NOTE: should_replace_ is overrided by it prim_pattern(if exists) silently.
-    should_replace_ = prim_pattern->should_replace();
   }
-  CallWith(PrimitivePyPtr prim, vector<PatternPtr> inputs, bool should_replace) {
+  Call(PrimitivePyPtr prim, vector<PatternPtr> inputs) {
     prim_ = prim;
-    unique_name_ = std::to_string(g_id_++) + "CallWithPrim_" + prim_->ToString();
+    unique_name_ = std::to_string(g_id_++) + "Call_" + prim_->ToString();
     inputs_ = inputs;
-    should_replace_ = should_replace;
   }
-  CallWith(string prim_str, vector<PatternPtr> inputs, bool should_replace) {
+  Call(string prim_str, vector<PatternPtr> inputs) {
     prim_ = std::make_shared<PrimitivePy>(prim_str, py::cast(nullptr));
-    unique_name_ = std::to_string(g_id_++) + "CallWithStr_" + prim_->ToString();
+    unique_name_ = std::to_string(g_id_++) + "CallStr_" + prim_->ToString();
     inputs_ = inputs;
-    should_replace_ = should_replace;
   }
-  MS_DECLARE_PARENT(CallWith, Pattern);
+  MS_DECLARE_PARENT(Call, Pattern);
   MatchResultPtr match(const AnfNodePtr &node) override;
   PrimitivePtr prim_value() { return prim_; }
   PatternPtr prim_pattern() { return prim_pattern_; }
@@ -160,45 +148,45 @@ class CallWith : public Pattern {
   string name_;
 };
 
-class IsIn : public Pattern {
+class OneOf : public Pattern {
  public:
-  IsIn() { unique_name_ = std::to_string(g_id_++); }
-  ~IsIn() = default;
-  explicit IsIn(vector<PatternPtr> patterns) : patterns_(patterns) {
-    unique_name_ = std::to_string(g_id_++) + "IsIn";
+  OneOf() { unique_name_ = std::to_string(g_id_++); }
+  ~OneOf() = default;
+  explicit OneOf(vector<PatternPtr> patterns) : patterns_(patterns) {
+    unique_name_ = std::to_string(g_id_++) + "OneOf";
     for (auto &iter : patterns) {
       unique_name_ = unique_name_ + "_" + iter->unique_name();
     }
   }
-  MS_DECLARE_PARENT(IsIn, Pattern);
+  MS_DECLARE_PARENT(OneOf, Pattern);
   MatchResultPtr match(const AnfNodePtr &node) override;
 
  private:
   vector<PatternPtr> patterns_;
 };
 
-class IsNot : public Pattern {
+class NoneOf : public Pattern {
  public:
-  IsNot() { unique_name_ = std::to_string(g_id_++); }
-  ~IsNot() = default;
-  explicit IsNot(vector<PatternPtr> patterns) : patterns_(patterns) {
-    unique_name_ = std::to_string(g_id_++) + "IsNot";
+  NoneOf() { unique_name_ = std::to_string(g_id_++); }
+  ~NoneOf() = default;
+  explicit NoneOf(vector<PatternPtr> patterns) : patterns_(patterns) {
+    unique_name_ = std::to_string(g_id_++) + "NoneOf";
     for (auto &iter : patterns) {
       unique_name_ = unique_name_ + "_" + iter->unique_name();
     }
   }
-  MS_DECLARE_PARENT(IsNot, Pattern);
+  MS_DECLARE_PARENT(NoneOf, Pattern);
   MatchResultPtr match(const AnfNodePtr &node) override;
 
  private:
   vector<PatternPtr> patterns_;
 };
 
-class AnyPattern : public Pattern {
+class Any : public Pattern {
  public:
-  AnyPattern() { unique_name_ = std::to_string(g_id_++) + "_AnyPattern"; }
-  ~AnyPattern() = default;
-  MS_DECLARE_PARENT(AnyPattern, Pattern);
+  Any() { unique_name_ = std::to_string(g_id_++) + "_Any"; }
+  ~Any() = default;
+  MS_DECLARE_PARENT(Any, Pattern);
   MatchResultPtr match(const AnfNodePtr &node) override;
 };
 
@@ -207,7 +195,6 @@ class NewTensor : public Pattern {
   NewTensor() { unique_name_ = std::to_string(g_id_++); }
   ~NewTensor() = default;
   explicit NewTensor(tensor::TensorPtr input_tensor) : input_tensor_(input_tensor) {
-    should_replace_ = false;
     unique_name_ = std::to_string(g_id_++) + "NewTensor";
   }
   MS_DECLARE_PARENT(NewTensor, Pattern);
@@ -223,10 +210,8 @@ class NewTensor : public Pattern {
 class NewParameter : public Pattern {
  public:
   NewParameter() { unique_name_ = std::to_string(g_id_++); }
-  explicit NewParameter(string para_name, tensor::TensorPtr default_tensor, bool requires_grad, bool layerwise_parallel,
-                        bool should_replace)
+  explicit NewParameter(string para_name, tensor::TensorPtr default_tensor, bool requires_grad, bool layerwise_parallel)
       : para_name_(para_name), requires_grad_(requires_grad), layerwise_parallel_(layerwise_parallel) {
-    should_replace_ = should_replace;
     unique_name_ = std::to_string(g_id_++) + "NewParameter_" + para_name;
     // clone input tensor
     default_tensor_ = std::make_shared<tensor::Tensor>(*default_tensor.get());
@@ -243,11 +228,14 @@ class NewParameter : public Pattern {
   bool built() { return built_; }
   void set_built(bool built) { built_ = built; }
   void reset() override { built_ = false; }
+  bool should_last() { return last_across_passes_; }
+  void set_last(bool last) { last_across_passes_ = last; }
 
  private:
   string para_name_;
   bool requires_grad_;
   bool layerwise_parallel_;
+  bool last_across_passes_{false};
   bool built_;
   tensor::TensorPtr default_tensor_;
 };
@@ -255,13 +243,9 @@ class NewParameter : public Pattern {
 class Imm : public Pattern {
  public:
   Imm() { unique_name_ = std::to_string(g_id_++); }
-  explicit Imm(int value) : value_(value) {
-    should_replace_ = false;
-    unique_name_ = std::to_string(g_id_++) + "Imm_" + std::to_string(value);
-  }
+  explicit Imm(int value) : value_(value) { unique_name_ = std::to_string(g_id_++) + "Imm_" + std::to_string(value); }
   MS_DECLARE_PARENT(Imm, Pattern);
-  // NOTE: Doesn't support Imm in src pattern currently.
-  MatchResultPtr match(const AnfNodePtr &node) override { return nullptr; }
+  MatchResultPtr match(const AnfNodePtr &node) override;
   int value() { return value_; }
 
  private:

mindspore/ccsrc/frontend/optimizer/py_pass.cc

@@ -80,7 +80,7 @@ bool IsTraversable(const AnfNodePtr &node) {
 
 AnfNodePtr BuildPrimitive(const PatternPtr &pattern, const MatchResultPtr &res) {
   // Build up AnfNode from primitive
-  auto prim_pattern = pattern->cast<IsPrimTypeOfPtr>();
+  auto prim_pattern = pattern->cast<PrimPtr>();
   MS_EXCEPTION_IF_NULL(prim_pattern);
   PrimitivePyPtr prim = prim_pattern->matched_primitive();
   MS_EXCEPTION_IF_NULL(prim);
@@ -98,13 +98,13 @@ AnfNodePtr BuildNewTensor(const PatternPtr &pattern, const MatchResultPtr &res)
 }
 
 AnfNodePtr BuildPrimitiveValueNode(const PatternPtr &pattern, const MatchResultPtr &res, const FuncGraphPtr &fg) {
-  auto call_with_pattern = pattern->cast<CallWithPtr>();
-  MS_EXCEPTION_IF_NULL(call_with_pattern);
-  auto prim = call_with_pattern->prim_value();
+  auto call_pattern = pattern->cast<CallPtr>();
+  MS_EXCEPTION_IF_NULL(call_pattern);
+  auto prim = call_pattern->prim_value();
   if (prim != nullptr) {
     return std::make_shared<ValueNode>(prim);
   }
-  auto prim_pattern = call_with_pattern->prim_pattern();
+  auto prim_pattern = call_pattern->prim_pattern();
   MS_EXCEPTION_IF_NULL(prim_pattern);
   return ProcessSinglePattern(prim_pattern, res, fg);
 }
@@ -152,45 +152,35 @@ AnfNodePtr BuildImmNode(const PatternPtr &pattern, const MatchResultPtr &res) {
 }
 
 AnfNodePtr ProcessSinglePattern(const PatternPtr &pattern, const MatchResultPtr &res, const FuncGraphPtr &func_graph) {
-  if (pattern->should_replace()) {
-    // Find replacement in the MatchResult
-    auto target_node = res->get_node(pattern);
-    if (target_node == nullptr) {
-      // If it's base pattern(in contrast to complex pattern like CallWith/IsIn/IsNot), raise runtime exception.
-      if (pattern->isa<IsPrimTypeOf>() || pattern->isa<NewTensor>() || pattern->isa<NewParameter>()) {
-        MS_LOG(EXCEPTION) << "Cannot find target node, pattern: " + pattern->unique_name() + "\n";
-        return nullptr;
-      }
-      // Try to build this pattern and add to MatchResult, since this pattern is defined inside target
-      auto new_node = BuildTarget(pattern, func_graph, res);
-      if (new_node == nullptr) {
-        MS_LOG(EXCEPTION) << "Try to build pattern node but FAILED. pattern: " + pattern->unique_name() + "\n";
-      }
-      return new_node;
-    }
-    if (pattern->isa<NewParameter>()) {
+  auto target_node = res->get_node(pattern);
+  if (target_node != nullptr) {
+    // If pattern is NewParameter, check whether it shouldn't last and is not built
+    auto new_para = pattern->cast<NewParameterPtr>();
+    if (new_para == nullptr || new_para->should_last() || new_para->built()) {
       return target_node;
     }
-    return target_node;
   }
   // Build up new node from pattern
-  if (pattern->isa<IsPrimTypeOf>()) {
+  if (pattern->isa<Prim>()) {
     return BuildPrimitive(pattern, res);
   } else if (pattern->isa<NewTensor>()) {
     return BuildNewTensor(pattern, res);
-  } else if (pattern->isa<CallWith>()) {
+  } else if (pattern->isa<Call>()) {
     return BuildPrimitiveValueNode(pattern, res, func_graph);
   } else if (pattern->isa<NewParameter>()) {
     return BuildNewParameter(pattern, res, func_graph);
   } else if (pattern->isa<Imm>()) {
     return BuildImmNode(pattern, res);
+  } else {
+    MS_LOG(EXCEPTION) << "Cannot find or build target node, pattern: " + pattern->unique_name() + "\n";
+    return nullptr;
   }
   return nullptr;
 }
 
 AnfNodePtr ProcessComplexPatternFirstInput(const PatternPtr &pattern, const MatchResultPtr &res,
                                            const FuncGraphPtr &func_graph) {
-  if (pattern->isa<CallWith>()) {
+  if (pattern->isa<Call>()) {
     return BuildPrimitiveValueNode(pattern, res, func_graph);
   }
   return nullptr;
@@ -269,16 +259,16 @@ void ReflectParamBackToPython(const AnfNodePtr &param, string param_name, tensor
 }
 
 void Reset(PatternPtr pattern) {
-  if (pattern->isa<IsPrimTypeOf>()) {
-    auto prim_pattern = pattern->cast<IsPrimTypeOfPtr>();
+  if (pattern->isa<Prim>()) {
+    auto prim_pattern = pattern->cast<PrimPtr>();
     prim_pattern->reset();
     return;
   } else if (pattern->isa<NewParameter>()) {
     auto new_param_pattern = pattern->cast<NewParameterPtr>();
     new_param_pattern->reset();
     return;
-  } else if (pattern->isa<CallWith>()) {
-    auto call_with_pattern = pattern->cast<CallWithPtr>();
+  } else if (pattern->isa<Call>()) {
+    auto call_with_pattern = pattern->cast<CallPtr>();
     for (auto sub_pattern : call_with_pattern->inputs()) {
       Reset(sub_pattern);
     }

mindspore/ccsrc/frontend/optimizer/py_pass_manager.cc

@@ -49,8 +49,9 @@ PyPassManager::PyPassManager() {
 }
 
 void PyPassManager::Registe(const std::string &pass_name, const PatternPtr &pattern, const PatternPtr &target,
-                            Phase phase, bool run_only_once) {
-  auto cur_pg = GetPassGroup(phase);
+                            bool run_only_once) {
+  // NOTE: remove phase option to avoid unnecessary confusion.
+  auto cur_pg = GetPassGroup(Phase::OPT);
   MS_EXCEPTION_IF_NULL(cur_pg);
   cur_pg->SetRunOnlyOnce(run_only_once);
   MS_EXCEPTION_IF_NULL(pattern);
@@ -60,8 +61,9 @@ void PyPassManager::Registe(const std::string &pass_name, const PatternPtr &patt
   cur_pg->AddPass(new_pass);
 }
 
-void PyPassManager::Unregiste(const std::string &pass_name, Phase phase) {
-  auto cur_pm = GetPassGroup(phase);
+void PyPassManager::Unregiste(const std::string &pass_name) {
+  // NOTE: remove phase option to avoid unnecessary confusion.
+  auto cur_pm = GetPassGroup(Phase::OPT);
   MS_EXCEPTION_IF_NULL(cur_pm);
   if (!cur_pm->DeletePass(pass_name)) {
     MS_LOG(WARNING) << "No such pass : " + pass_name + "\n";
@@ -70,7 +72,6 @@ void PyPassManager::Unregiste(const std::string &pass_name, Phase phase) {
 
 void PyPassManager::GenNewParameter(const PatternPtr &parameter) {
   MS_EXCEPTION_IF_NULL(parameter);
-  // Add new parameter after resolve
   // NOTE: Add NewParameter at early stage will cause CSE problems
   auto cur_pg = GetPassGroup(Phase::OPT);
   MS_EXCEPTION_IF_NULL(cur_pg);
@@ -78,7 +79,7 @@ void PyPassManager::GenNewParameter(const PatternPtr &parameter) {
   auto new_para_pattern = parameter->cast<NewParameterPtr>();
   MS_EXCEPTION_IF_NULL(new_para_pattern);
   auto pass_name = new_para_pattern->para_name();
-  parameter->set_should_replace(false);
+  new_para_pattern->set_last(true);
   auto new_pass = std::make_shared<PythonPass>(pass_name, nullptr, parameter, true);
   cur_pg->AddPass(new_pass);
 }

mindspore/ccsrc/frontend/optimizer/py_pass_manager.h

@@ -53,16 +53,17 @@ class PyPassManager {
   static PyPassManagerPtr GetInstance();
   virtual ~PyPassManager() = default;
   void Registe(const std::string &pass_name, const PatternPtr &pattern, const PatternPtr &target,
-               Phase phase = Phase::RESOLVE, bool run_only_once = false);
-  void Unregiste(const std::string &pass_name, Phase phase);
+               bool run_only_once = false);
+  void Unregiste(const std::string &pass_name);
   void GenNewParameter(const PatternPtr &parameter);
   PassGroupPtr GetPassGroup(Phase phase);
-  void ClearRes();
   MatchResultPtr GetMatchResult() { return res_; }
   void SetRenorm(bool should_renorm) { should_renorm_ = should_renorm; }
   bool ShouldRenorm() { return should_renorm_; }
   void SetResource(pipeline::ResourcePtr resource) { resource_ = resource; }
   pipeline::ResourcePtr GetResource() { return resource_; }
+  void ClearRes();
+  void ClearPipelineRes() { resource_ = nullptr; }
 
  private:
   bool should_renorm_ = true;

mindspore/ccsrc/pipeline/jit/pipeline.cc

@@ -477,6 +477,7 @@ bool ExecutorPy::CompileInner(const py::object &obj, const py::tuple &args, cons
   // save the run graph func to MsPipeLine
   SaveCompiledGraph(phase_s);
 
+  opt::python_pass::PyPassManager::GetInstance()->ClearPipelineRes();
   resource->Clean();
   // Reclaim all resource used by optimizer;
   ReclaimOptimizer();

mindspore/graph_utils/graph_pattern.py

@@ -15,50 +15,43 @@
 """Patterns for describing graphs"""
 from mindspore.ops import Primitive
 from mindspore.common.tensor import Tensor
-from mindspore._c_expression import Pattern, IsIn_, IsPrimTypeOf_, CallWith_, IsNot_, AnyPattern, NewTensor_,\
-                                    NewParameter_, Imm
+from mindspore._c_expression import Pattern, OneOf_, Prim_, Call_, NoneOf_, Any, NewTensor_, NewParameter_, Imm
 
 __all__ = [
-    "IsIn",
-    "IsPrimTypeOf",
-    "CallWith",
-    "IsNot",
-    "AnyPattern",
+    "OneOf",
+    "Prim",
+    "Call",
+    "NoneOf",
+    "Any",
     "NewTensor",
     "NewParameter",
     "Imm"
 ]
 
-class IsIn(IsIn_):
+class OneOf(OneOf_):
     r"""
     Express a pattern which allows a list of patterns.
     """
-    def __init__(self, patterns=None, should_replace=True):
+    def __init__(self, patterns=None):
         r"""
         Args:
-            patterns(Union[tuple[:class:`mindspore.graph_utils.graph_pattern`],
+            patterns(Union[:class:`mindspore.graph_utils.graph_pattern`,
+                           tuple[:class:`mindspore.graph_utils.graph_pattern`],
                            list[:class:`mindspore.graph_utils.graph_pattern`]]): list of allowed patterns,
                 each element should be one of the exposed Pattern instance.
-            should_replace(bool): added this for interface consistency. Should only set this in sub-patterns.
 
         Raises:
-            ValueError: raise if should_replace is False
             TypeError: raise type error for invalid inputs.
         """
-        if not should_replace:
-            raise ValueError("IsIn pattern does not have its own should_replace attribute. Set should_replace in \
-                              its sub-pattern instead.")
         self.patterns = patterns
-        if patterns is None:
-            IsIn_.__init__(self, ())
-        elif isinstance(patterns, Pattern):
-            IsIn_.__init__(self, [patterns])
+        if isinstance(patterns, Pattern):
+            OneOf_.__init__(self, [patterns])
         elif isinstance(patterns, (tuple, list)) and all(isinstance(pattern, Pattern) for pattern in patterns):
-            IsIn_.__init__(self, patterns)
+            OneOf_.__init__(self, patterns)
         else:
             raise TypeError(f"Expect patterns to be a list of Patterns/Pattern, got : {patterns}")
 
-class IsPrimTypeOf(IsPrimTypeOf_):
+class Prim(Prim_):
     r"""
     Express a pattern of certain primitive type(s).
 
@@ -66,7 +59,7 @@ class IsPrimTypeOf(IsPrimTypeOf_):
         This pattern will match and only match the primitive value node. If matching primitive CNode is needed,
         please refer to CallWith pattern.
     """
-    def __init__(self, types, name=None, should_replace=True):
+    def __init__(self, types, name=None):
         r"""
         Args:
             types (Union[str, :class:`mindspore.ops.Primitive`, list[:class:`mindspore.ops.Primitive`],
@@ -77,9 +70,6 @@ class IsPrimTypeOf(IsPrimTypeOf_):
                     2) a set of primitive types separated by '|', e.g. 'MatMul|Conv2D'
                 It can also be a Primitive or a list/tuple of Primitives, e.g. [ops.Conv2D(1, 6)]
             name (str): name of the pattern, optional. Default: None.
-            should_replace(bool): If pattern is part of the pass replacement target, this would set how this pattern is
-                used when building the replacement target node. Use captured node if True, build from scratch otherwise.
-                Default: True.
 
         Raises:
             TypeError: raise type error for invalid argument.
@@ -103,13 +93,13 @@ class IsPrimTypeOf(IsPrimTypeOf_):
             self.types = types
         else:
             raise TypeError(f"Expecting a primitive type string or a list of Primitives, got : {types}")
-        IsPrimTypeOf_.__init__(self, self.types, self.name, should_replace)
+        Prim_.__init__(self, self.types, self.name)
 
-class CallWith(CallWith_):
+class Call(Call_):
     r"""
     Express a primitive CNode.
     """
-    def __init__(self, prim_pattern, inputs=None, should_replace=True):
+    def __init__(self, prim_pattern, inputs=None):
         r"""
         Args:
             prim_pattern (Union[str, :class:`mindspore.graph_utils.graph_pattern.IsPrimTypeOf`,
@@ -118,9 +108,6 @@ class CallWith(CallWith_):
                           tuple[:class:`mindspore.graph_utils.graph_pattern`]]):
                 Specify inputs pattern for the primitive(s), optional. If None, accepts any inputs; if specified, input
                 patterns should be of right order and each element should be one of the exposed Pattern instance.
-            should_replace(bool): If pattern is part of the pass replacement target, this would set how this pattern is
-                used when building the replacement target node. Use captured node if True, build from scratch otherwise.
-                Default: True.
 
         Raises:
             TypeError: raise type error for invalid argument.
@@ -135,36 +122,31 @@ class CallWith(CallWith_):
             self.inputs = inputs
         else:
             raise TypeError(f"Expect inputs to be a list of Patterns, got : {inputs}")
-        CallWith_.__init__(self, self.prim_pattern, self.inputs, should_replace)
+        Call_.__init__(self, self.prim_pattern, self.inputs)
 
-class IsNot(IsNot_):
+class NoneOf(NoneOf_):
     r"""
     Express a pattern which forbids a list of patterns.
 
     NOTE:
-        IsNot pattern should not be the root pattern.
+        NoneOf pattern should not be the root pattern.
     """
-    def __init__(self, patterns=None, should_replace=True):
+    def __init__(self, patterns=None):
         r"""
         Args:
             patterns(Union[list[:class:`mindspore.graph_utils.graph_pattern`]]: list of forbiden patterns, each element
                 should be one of the exposed Pattern instance.
-            should_replace(bool): added this for interface consistency. Should only set this in sub-patterns.
 
         Raises:
-            ValueError: raise if should_replace is False.
             TypeError: raise type error for invalid argument.
         """
-        if not should_replace:
-            raise ValueError("IsNot pattern does not have its own should_replace attribute. Set should_replace in \
-                              its sub-pattern instead.")
         self.patterns = patterns
         if patterns is None:
-            IsNot_.__init__(self, ())
+            NoneOf_.__init__(self, ())
         elif isinstance(patterns, Pattern):
-            IsNot_.__init__(self, [patterns])
+            NoneOf_.__init__(self, [patterns])
         elif isinstance(patterns, (tuple, list)) and all(isinstance(pattern, Pattern) for pattern in patterns):
-            IsNot_.__init__(self, patterns)
+            NoneOf_.__init__(self, patterns)
         else:
             raise TypeError(f"Expect list of Patterns/Pattern, got : {patterns}")
 
@@ -172,18 +154,14 @@ class NewTensor(NewTensor_):
     r"""
     New Tensor to be used in the target.
     """
-    def __init__(self, input_tensor, should_replace=False):
+    def __init__(self, input_tensor):
         r"""
         Args:
             input_tensor(:class:`mindspore.common.tensor.Tensor`): new tensor to be used in the target
-            should_replace(bool): added this for interface consistency. NewTensor should only appear in the target.
 
         Raises:
-            ValueError: raise if should_replace is True
             TypeError: raise type error for invalid argument.
         """
-        if should_replace:
-            raise ValueError("NewTensor should only appear in the target, thus should_replace can only be False.")
         self.input_tensor = input_tensor
         if isinstance(input_tensor, Tensor):
             NewTensor_.__init__(self, input_tensor)
@@ -194,15 +172,13 @@ class NewParameter(NewParameter_):
     r"""
     New Parameter to be used in the target.
     """
-    def __init__(self, para_name, default_tensor, requires_grad=False, layerwise_parallel=False, should_replace=False):
+    def __init__(self, para_name, default_tensor, requires_grad=False, layerwise_parallel=False):
         r"""
         Args:
             para_name(str): name for the new Parameter
             default_tensor(:class:`mindspore.common.tensor.Tensor`): default value for the new Parameter
             requires_grad(bool): True if the parameter requires gradient. Default: True
             layerwise_parallel(bool): switch for layerwise parallel mode. Default: False
-            should_replace(bool): gen new parameter once and replace after if set to be true; otherwise build a new
-                                  parameter everytime a pass target got built. Default: False
 
         Raises:
             TypeError: raise type error for invalid argument.
@@ -211,12 +187,11 @@ class NewParameter(NewParameter_):
         self.default_tensor = default_tensor
         self.requires_grad = requires_grad
         self.layerwise_parallel = layerwise_parallel
-        self.should_replace = should_replace
         if isinstance(para_name, str) and isinstance(default_tensor, Tensor) and isinstance(requires_grad, bool) and\
-           isinstance(layerwise_parallel, bool) and isinstance(should_replace, bool):
+           isinstance(layerwise_parallel, bool):
             NewParameter_.__init__(self, self.para_name, self.default_tensor, self.requires_grad,
-                                   self.layerwise_parallel, self.should_replace)
+                                   self.layerwise_parallel)
         else:
             raise TypeError(f"Expect para_name(str), default_tensor(Tensor), requires_grad(bool), \
-                              layerwise_parallel(bool) should_replace(bool)， got : {para_name}, {default_tensor}, \
-                              {requires_grad}, {layerwise_parallel}, {should_replace}")
+                              layerwise_parallel(bool)， got : {para_name}, {default_tensor}, \
+                              {requires_grad}, {layerwise_parallel}")

mindspore/graph_utils/python_pass/python_pass_register.py

@@ -15,7 +15,7 @@
 """Python pass register"""
 from inspect import isfunction
 from mindspore.graph_utils.graph_pattern import Pattern, NewParameter
-from mindspore._c_expression import PyPassManager_, phase
+from mindspore._c_expression import PyPassManager_
 
 
 __all__ = [
@@ -30,21 +30,16 @@ class PyPassManager(PyPassManager_):
     Used to registe and unregiste python passes which can be used to alter graphs.
 
     Args:
-        pipeline_phase (phase): Specify the stage in which the pass will run in the pipeline. Default: phase.opt.
         run_only_once (bool): Specify whether or not to run pass only once. Default: False.
-        multigraph (bool): Whether or not the pattern exists across graphs. Default: True.
 
     Raises:
         TypeError: If argument has invalid type.
     """
-    def __init__(self, pipeline_phase=phase.opt, run_only_once=False):
-        if not isinstance(pipeline_phase, phase):
-            raise TypeError(f"Expect phase, got : ({type(pipeline_phase)}){pipeline_phase}")
+    def __init__(self, run_only_once=False):
         if not isinstance(run_only_once, bool):
             raise TypeError(f"Expect bool, got : ({type(run_only_once)}){run_only_once}")
-        PyPassManager_.__init__(self)
-        self.phase_ = pipeline_phase
         self.run_only_once_ = run_only_once
+        PyPassManager_.__init__(self)
 
     def registe(self, py_pass):
         if not isfunction(py_pass):
@@ -55,16 +50,14 @@ class PyPassManager(PyPassManager_):
             raise TypeError(f"Expect pattern of Pattern type, got : ({type(pattern)}){pattern}")
         if not isinstance(target, Pattern):
             raise TypeError(f"Expect target of Pattern type, got : ({type(target)}){target}")
-        super().registe(pass_name, pattern, target, self.phase_, self.run_only_once_)
+        super().registe(pass_name, pattern, target, self.run_only_once_)
 
-    def unregiste(self, py_pass, pipeline_phase=phase.opt):
-        if not isinstance(pipeline_phase, phase):
-            raise TypeError(f"Expect phase, got : ({type(pipeline_phase)}){pipeline_phase}")
+    def unregiste(self, py_pass):
         if isinstance(py_pass, str):
-            super().unregiste(py_pass, pipeline_phase)
+            super().unregiste(py_pass)
             return
         if isfunction(py_pass):
-            super().unregiste(py_pass.__name__, pipeline_phase)
+            super().unregiste(py_pass.__name__)
             return
         raise TypeError(f"Expect py_pass to be string or function, got ({type(py_pass)}){py_pass}")
 
@@ -82,13 +75,11 @@ class PyPassManager(PyPassManager_):
             raise TypeError(f"Expect should_renorm to be a bool, got {should_renorm}")
         super().set_renorm(should_renorm)
 
-def registe_pass(pipeline_phase=phase.opt, run_only_once=False):
+def registe_pass(run_only_once=False):
     """
     Registe python pass to specified pipeline phase which would be used in compilation.
 
     Args:
-        pipeline_phase(:class:`mindspore._c_expression.phase`): To which compilation pipeline stage the pass is
-            registed. Support phase.resolve and phase.opt. Default: phase.opt.
         run_only_once(bool): Run this pass only once if set true. Otherwise run the pass until converge. Default: False.
 
     Returns:
@@ -102,19 +93,17 @@ def registe_pass(pipeline_phase=phase.opt, run_only_once=False):
         >>>     target = IsPrimTypeOf("ReLU6")
         >>>     return pattern, target
     """
-    return PyPassManager(pipeline_phase, run_only_once)
+    return PyPassManager(run_only_once)
 
-def unregiste_pass(py_pass, pipeline_phase=phase.opt):
+def unregiste_pass(py_pass):
     """
     Unregiste python pass.
 
     Args:
         py_pass(Union(str, function)): target python pass to unregiste.
-        pipeline_phase(:class:`mindspore._c_expression.phase`): To which compilation pipeline stage the pass is
-            unregisted. Support phase.resolve and phase.opt. Default: phase.opt.
     """
     ppm = PyPassManager()
-    ppm.unregiste(py_pass, pipeline_phase)
+    ppm.unregiste(py_pass)
 
 def gen_new_parameter(pattern):
     """
@@ -164,7 +153,14 @@ def cancel_new_parameter(pattern):
 
 def set_renorm(should_renorm):
     """
-    Set whether or not to do renorm after modified graph in python pass(es).
+    Set whether or not to do renormalization after modified graph in python pass(es).
+
+    Args:
+        should_renorm(bool): whether or not to do renormalization after modified graph in python pass(es).
+
+    NOTE:
+        This interface is mainly intended for testing modifying graph without worrying about its validity. Turn off
+        renormalization may BREAK the network.
     """
     ppm = PyPassManager()
     ppm.set_renorm(should_renorm)

tests/ut/python/optimizer/test_python_pass.py

@@ -23,8 +23,7 @@ from mindspore.graph_utils.python_pass import registe_pass, unregiste_pass, set_
     cancel_new_parameter
 from mindspore.common.api import _generate_pip_args
 from mindspore._c_expression import generate_key, Executor_
-from mindspore.graph_utils.graph_pattern import IsIn, IsPrimTypeOf, CallWith, IsNot, AnyPattern, NewTensor,\
-    NewParameter, Imm
+from mindspore.graph_utils.graph_pattern import OneOf, Prim, Call, NoneOf, Any, NewTensor, NewParameter, Imm
 
 context.set_context(mode=context.GRAPH_MODE)
 
@@ -50,11 +49,9 @@ def test_softmax_relu():
 
     @registe_pass(run_only_once=True)
     def softmax_relu_pass():
-        x = AnyPattern()
-        softmax_pattern = IsPrimTypeOf(P.Softmax())
-        pattern = CallWith(softmax_pattern, inputs=[x])
-        relu_pattern = IsPrimTypeOf(P.ReLU(), should_replace=False)
-        target = CallWith(relu_pattern, inputs=[x])
+        x = Any()
+        pattern = Call(P.Softmax(), inputs=[x])
+        target = Call(P.ReLU(), inputs=[x])
         return pattern, target
 
     transformed_repr = get_func_graph(softmax_model, inputs).get_return().expanded_str(2)
@@ -74,13 +71,13 @@ def test_softmax_relu_sigmoid():
 
     @registe_pass(run_only_once=True)
     def softmax_relu_pass():
-        x = AnyPattern()
-        softmax_pattern = IsPrimTypeOf(P.Softmax())
-        pattern = CallWith(softmax_pattern, inputs=[x])
-        sigmoid_pattern = IsPrimTypeOf(P.Sigmoid(), should_replace=False)
-        call_sigmoid = CallWith(sigmoid_pattern, [x])
-        relu_pattern = IsPrimTypeOf(P.ReLU(), should_replace=False)
-        target = CallWith(relu_pattern, inputs=[call_sigmoid])
+        x = Any()
+        softmax_pattern = Prim(P.Softmax())
+        pattern = Call(softmax_pattern, inputs=[x])
+        sigmoid_pattern = Prim(P.Sigmoid())
+        call_sigmoid = Call(sigmoid_pattern, [x])
+        relu_pattern = Prim(P.ReLU())
+        target = Call(relu_pattern, inputs=[call_sigmoid])
         return pattern, target
 
     transformed_repr = get_func_graph(softmax_model, inputs).get_return().expanded_str(3)
@@ -99,15 +96,15 @@ def test_isin_pattern_0():
 
     @registe_pass(run_only_once=True)
     def softmax_relu_pass():
-        x = AnyPattern()
-        softmax_pattern = IsPrimTypeOf(P.Softmax())
-        call_softmax = CallWith(softmax_pattern, inputs=[x])
-        relu_pattern = IsPrimTypeOf(P.ReLU())
-        call_relu = CallWith(relu_pattern, inputs=[x])
-
-        pattern = IsIn([call_softmax, call_relu])
-        relu6_pattern = IsPrimTypeOf(P.ReLU6(), should_replace=False)
-        target = CallWith(relu6_pattern, inputs=[x])
+        x = Any()
+        softmax_pattern = Prim(P.Softmax())
+        call_softmax = Call(softmax_pattern, inputs=[x])
+        relu_pattern = Prim(P.ReLU())
+        call_relu = Call(relu_pattern, inputs=[x])
+
+        pattern = OneOf([call_softmax, call_relu])
+        relu6_pattern = Prim(P.ReLU6())
+        target = Call(relu6_pattern, inputs=[x])
         return pattern, target
     transformed_repr = get_func_graph(softmax_model, inputs).get_return().expanded_str(2)
     unregiste_pass(softmax_relu_pass)
@@ -123,18 +120,17 @@ def test_isin_pattern_1():
 
     @registe_pass(run_only_once=True)
     def softmax_neg_pass():
-        x = AnyPattern()
-        softmax_pattern = IsPrimTypeOf(P.Softmax())
-        call_softmax = CallWith(softmax_pattern, inputs=[x])
-        relu_pattern = IsPrimTypeOf(P.ReLU())
-        call_relu = CallWith(relu_pattern, inputs=[x])
-
-        pattern = IsIn([call_softmax, call_relu])
-        neg_ops = IsPrimTypeOf(P.Neg(), should_replace=False)
-        target = CallWith(neg_ops, inputs=[pattern])
+        x = Any()
+        softmax_pattern = Prim(P.Softmax())
+        call_softmax = Call(softmax_pattern, inputs=[x])
+        relu_pattern = Prim(P.ReLU())
+        call_relu = Call(relu_pattern, inputs=[x])
+
+        pattern = OneOf([call_softmax, call_relu])
+        neg_ops = Prim(P.Neg())
+        target = Call(neg_ops, inputs=[pattern])
         return pattern, target
     transformed_repr = get_func_graph(softmax_model, inputs).get_return().expanded_str(4)
-    print(transformed_repr)
     unregiste_pass(softmax_neg_pass)
     assert "Neg" in transformed_repr
     assert "Softmax" in transformed_repr
@@ -167,11 +163,11 @@ def test_isnot_pattern_0():
         """
         Sub a BN which does NOT take Conv as inputs to ReLU6.
         """
-        conv2d_prim = IsPrimTypeOf("Conv2D")
-        conv2d = CallWith(conv2d_prim)
-        pattern_0 = IsNot(conv2d)
-        pattern = CallWith(P.BatchNorm(), inputs=[pattern_0])
-        target = CallWith(P.ReLU6(), inputs=[pattern_0])
+        conv2d_prim = Prim("Conv2D")
+        conv2d = Call(conv2d_prim)
+        pattern_0 = NoneOf(conv2d)
+        pattern = Call(P.BatchNorm(), inputs=[pattern_0])
+        target = Call(P.ReLU6(), inputs=[pattern_0])
         return pattern, target
 
     @registe_pass(run_only_once=True)
@@ -179,10 +175,8 @@ def test_isnot_pattern_0():
         """
         Sub a BN to Softmax.
         """
-        bn = P.BatchNorm()
-        pattern = CallWith(bn)
-        softmax = P.Softmax()
-        target = CallWith(softmax, should_replace=False)
+        pattern = Call(P.BatchNorm())
+        target = Call(P.Softmax())
         return pattern, target
 
     transformed_repr = get_func_graph(conv_bn_model, inputs).get_return().expanded_str(5)
@@ -205,12 +199,12 @@ def test_isnot_pattern_1():
         """
         Sub a BN which does NOT take MatMul as inputs to ReLU6.
         """
-        matmul = IsPrimTypeOf("MatMul")
-        pattern_0 = IsNot(matmul)
+        matmul = Prim("MatMul")
+        pattern_0 = NoneOf(matmul)
         softmax = P.Softmax()
-        pattern = CallWith(softmax, inputs=[pattern_0])
+        pattern = Call(softmax, inputs=[pattern_0])
         relu6 = P.ReLU6()
-        target = CallWith(relu6, inputs=[pattern_0], should_replace=False)
+        target = Call(relu6, inputs=[pattern_0])
         return pattern, target
 
     transformed_repr = get_func_graph(softmax_model, inputs).get_return().expanded_str(5)
@@ -228,14 +222,12 @@ def test_newtensor_pattern():
 
     @registe_pass(run_only_once=True)
     def softmax_addn_pass():
-        x = AnyPattern()
-        softmax = P.Softmax()
-        pattern = CallWith(softmax, inputs=[x])
+        x = Any()
+        pattern = Call(P.Softmax(), inputs=[x])
 
         weight_tensor = Tensor(np.zeros([42]), mindspore.float16)
         new_weight = NewTensor(weight_tensor)
-        addn_ops = P.AddN()
-        target = CallWith(addn_ops, inputs=[x, new_weight], should_replace=False)
+        target = Call(P.AddN(), inputs=[x, new_weight])
         return pattern, target
     transformed_repr = get_func_graph(softmax_model, inputs).get_return().expanded_str(2)
     unregiste_pass(softmax_addn_pass)
@@ -252,25 +244,23 @@ def test_newparameter_pattern():
 
     @registe_pass(run_only_once=True)
     def softmax_addn_pass():
-        x = AnyPattern()
-        softmax = P.Softmax()
-        pattern = CallWith(softmax, inputs=[x])
+        x = Any()
+        pattern = Call(P.Softmax(), inputs=[x])
 
         default_tensor0 = Tensor(np.ones((4, 4)), mindspore.float32)
         default_tensor1 = Tensor(np.ones((4, 4)), mindspore.float32)
         new_para_0 = NewParameter("Merlin", default_tensor0)
         new_para_1 = NewParameter("Arthur", default_tensor1)
-        target_0 = CallWith(P.MatMul(), inputs=[new_para_0, new_para_1], should_replace=False)
-        target = CallWith("make_tuple", inputs=[target_0], should_replace=False)
+        target_0 = Call(P.MatMul(), inputs=[new_para_0, new_para_1])
+        target = Call("make_tuple", inputs=[target_0])
         return pattern, target
     transformed_repr = get_func_graph(softmax_model, inputs).get_return().expanded_str(5)
-    print(transformed_repr)
     unregiste_pass(softmax_addn_pass)
     assert "MatMul" in transformed_repr
     assert "make_tuple" in transformed_repr
     assert "Softmax" not in transformed_repr
 
-def test_imm_pattern():
+def test_imm_target():
     """
     Test NewParameter pattern in the target
     """
@@ -278,17 +268,15 @@ def test_imm_pattern():
     softmax_model = nn.Softmax()
 
     @registe_pass(run_only_once=True)
-    def softmax_addn_pass():
-        x = AnyPattern()
-        softmax = P.Softmax()
-        pattern = CallWith(softmax, inputs=[x])
+    def softmax_pass():
+        x = Any()
+        pattern = Call(P.Softmax(), inputs=[x])
         imm = Imm(0)
-        target_0 = CallWith("make_tuple", inputs=[pattern], should_replace=False)
-        target = CallWith("tuple_getitem", inputs=[target_0, imm], should_replace=False)
+        target_0 = Call("make_tuple", inputs=[pattern])
+        target = Call("tuple_getitem", inputs=[target_0, imm])
         return pattern, target
     transformed_repr = get_func_graph(softmax_model, inputs).get_return().expanded_str(5)
-    print(transformed_repr)
-    unregiste_pass(softmax_addn_pass)
+    unregiste_pass(softmax_pass)
     assert "make_tuple" in transformed_repr
     assert "tuple_getitem" in transformed_repr
     assert "Softmax" in transformed_repr
@@ -301,21 +289,19 @@ def test_gen_new_parameter():
     softmax_model = nn.Softmax()
 
     default_tensor = Tensor(np.ones((4, 4)), mindspore.float32)
-    new_para = NewParameter("Merlin", default_tensor, should_replace=True)
+    new_para = NewParameter("Merlin", default_tensor)
     gen_new_parameter(new_para)
     @registe_pass(run_only_once=True)
     def softmax_make_tuple_pass():
-        x = AnyPattern()
+        x = Any()
         softmax = P.Softmax()
-        pattern = CallWith(softmax, inputs=[x])
+        pattern = Call(softmax, inputs=[x])
 
-        target = CallWith("make_tuple", inputs=[pattern, new_para], should_replace=False)
+        target = Call("make_tuple", inputs=[pattern, new_para])
         return pattern, target
     transformed_repr = get_func_graph(softmax_model, inputs).get_return().expanded_str(5)
-    print(transformed_repr)
     assert "Merlin" in transformed_repr
     unregiste_pass(softmax_make_tuple_pass)
     cancel_new_parameter(new_para)
     transformed_repr = get_func_graph(softmax_model, inputs).get_return().expanded_str(5)
-    print(transformed_repr)
     assert "Merlin" not in transformed_repr