!5692 Add requires_grad option for python pass

Merge pull request !5692 from BowenK/pre_ad

mindspore/ccsrc/frontend/optimizer/ad/kprim.cc

@@ -49,7 +49,15 @@ FuncGraphPtr KPrim::GetBprop(const PrimitivePtr &prim) {
   auto scope = std::make_shared<Scope>(gradients_scope + ScopeManager::GetInstance().GetCurrentScope()->name() +
                                        grad_op_child_scope_prefix + prim->name());
   ScopeGuard scope_guard(scope);
-  py::function fn = prim->is_base() ? GetBpropFunction(prim->name()) : prim->cast<PrimitivePyPtr>()->GetBpropFunction();
+  py::function fn;
+  if (prim->is_base()) {
+    fn = GetBpropFunction(prim->name());
+  } else {
+    fn = prim->cast<PrimitivePyPtr>()->GetBpropFunction();
+    if (py::isinstance<py::none>(fn)) {
+      fn = GetBpropFunction(prim->name());
+    }
+  }
   if (!fn || py::isinstance<py::none>(fn)) {
     MS_LOG(DEBUG) << "Fail to find bprop function for " << prim->name() << ".";
     return nullptr;

mindspore/ccsrc/frontend/optimizer/py_pass.cc

@@ -35,8 +35,10 @@ namespace internal {
 const char PARAMETER_MODULE[] = "mindspore.common.parameter";
 const char PARAMETER_CLASS[] = "Parameter";
 const char SET_PARAM[] = "__setattr__";
-AnfNodePtr ProcessSinglePattern(const PatternPtr &pattern, const MatchResultPtr &res, const FuncGraphPtr &func_graph);
-AnfNodePtr BuildTarget(const PatternPtr &pattern, const FuncGraphPtr &func_graph, const MatchResultPtr &res);
+AnfNodePtr ProcessSinglePattern(const PatternPtr &pattern, const MatchResultPtr &res, const FuncGraphPtr &func_graph,
+                                const FuncGraphPtr &top_graph);
+AnfNodePtr BuildTarget(const PatternPtr &pattern, const FuncGraphPtr &func_graph, const FuncGraphPtr &top_graph,
+                       const MatchResultPtr &res);
 void ReflectParamBackToPython(const AnfNodePtr &param, string param_name, tensor::TensorPtr default_input,
                               bool requires_grad, bool layerwise_parallel);
 
@@ -72,7 +74,8 @@ AnfNodePtr BuildNewTensor(const PatternPtr &pattern, const MatchResultPtr &res)
   return std::make_shared<ValueNode>(input_tensor);
 }
 
-AnfNodePtr BuildPrimitiveValueNode(const PatternPtr &pattern, const MatchResultPtr &res, const FuncGraphPtr &fg) {
+AnfNodePtr BuildPrimitiveValueNode(const PatternPtr &pattern, const MatchResultPtr &res, const FuncGraphPtr &fg,
+                                   const FuncGraphPtr &top_graph) {
   auto call_pattern = pattern->cast<CallPtr>();
   MS_EXCEPTION_IF_NULL(call_pattern);
   auto prim = call_pattern->prim_value();
@@ -81,20 +84,20 @@ AnfNodePtr BuildPrimitiveValueNode(const PatternPtr &pattern, const MatchResultP
   }
   auto prim_pattern = call_pattern->prim_pattern();
   MS_EXCEPTION_IF_NULL(prim_pattern);
-  return ProcessSinglePattern(prim_pattern, res, fg);
+  return ProcessSinglePattern(prim_pattern, res, fg, top_graph);
 }
 
-AnfNodePtr BuildNewParameter(const PatternPtr &pattern, const MatchResultPtr &res, const FuncGraphPtr &func_graph) {
+AnfNodePtr BuildNewParameter(const PatternPtr &pattern, const MatchResultPtr &res, const FuncGraphPtr &top_graph) {
   auto new_para_pattern = pattern->cast<NewParameterPtr>();
   MS_EXCEPTION_IF_NULL(new_para_pattern);
   if (!new_para_pattern->built()) {
     static int parameter_id = 0;
     auto para_name = new_para_pattern->para_name() + new_para_pattern->unique_name() + std::to_string(parameter_id++);
-    auto para_node = std::make_shared<Parameter>(func_graph);
+    auto para_node = std::make_shared<Parameter>(top_graph);
     MS_EXCEPTION_IF_NULL(para_node);
     para_node->set_name(para_name);
     // Set function graph
-    para_node->set_func_graph(func_graph);
+    para_node->set_func_graph(top_graph);
     // Set Debug Info
     auto debug_info = std::make_shared<NodeDebugInfo>(para_name);
     para_node->set_debug_info(debug_info);
@@ -103,7 +106,7 @@ AnfNodePtr BuildNewParameter(const PatternPtr &pattern, const MatchResultPtr &re
     MS_EXCEPTION_IF_NULL(default_value);
     para_node->set_abstract(default_value->ToAbstract()->Broaden());
     res->add_entry(pattern, para_node);
-    func_graph->add_parameter(para_node);
+    top_graph->add_parameter(para_node);
     // Reflect back to Cell._params
     internal::ReflectParamBackToPython(para_node, para_name, default_value, new_para_pattern->requires_grad(),
                                        new_para_pattern->layerwise_parallel());
@@ -126,7 +129,8 @@ AnfNodePtr BuildImmNode(const PatternPtr &pattern, const MatchResultPtr &res) {
   return std::make_shared<ValueNode>(scalar_value_ptr);
 }
 
-AnfNodePtr ProcessSinglePattern(const PatternPtr &pattern, const MatchResultPtr &res, const FuncGraphPtr &func_graph) {
+AnfNodePtr ProcessSinglePattern(const PatternPtr &pattern, const MatchResultPtr &res, const FuncGraphPtr &func_graph,
+                                const FuncGraphPtr &top_graph) {
   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
@@ -141,9 +145,10 @@ AnfNodePtr ProcessSinglePattern(const PatternPtr &pattern, const MatchResultPtr
   } else if (pattern->isa<NewTensor>()) {
     return BuildNewTensor(pattern, res);
   } else if (pattern->isa<Call>()) {
-    return BuildPrimitiveValueNode(pattern, res, func_graph);
+    return BuildPrimitiveValueNode(pattern, res, func_graph, top_graph);
   } else if (pattern->isa<NewParameter>()) {
-    return BuildNewParameter(pattern, res, func_graph);
+    // Add new parameter to top graph instead of current graph
+    return BuildNewParameter(pattern, res, top_graph);
   } else if (pattern->isa<Imm>()) {
     return BuildImmNode(pattern, res);
   } else {
@@ -154,17 +159,18 @@ AnfNodePtr ProcessSinglePattern(const PatternPtr &pattern, const MatchResultPtr
 }
 
 AnfNodePtr ProcessComplexPatternFirstInput(const PatternPtr &pattern, const MatchResultPtr &res,
-                                           const FuncGraphPtr &func_graph) {
+                                           const FuncGraphPtr &func_graph, const FuncGraphPtr &top_graph) {
   if (pattern->isa<Call>()) {
-    return BuildPrimitiveValueNode(pattern, res, func_graph);
+    return BuildPrimitiveValueNode(pattern, res, func_graph, top_graph);
   }
   return nullptr;
 }
 
-AnfNodePtr BuildTarget(const PatternPtr &pattern, const FuncGraphPtr &func_graph, const MatchResultPtr &res) {
+AnfNodePtr BuildTarget(const PatternPtr &pattern, const FuncGraphPtr &func_graph, const FuncGraphPtr &top_graph,
+                       const MatchResultPtr &res) {
   auto target_inputs = pattern->inputs();
   if (target_inputs.size() == 0) {
-    auto new_node = ProcessSinglePattern(pattern, res, func_graph);
+    auto new_node = ProcessSinglePattern(pattern, res, func_graph, top_graph);
     if (new_node != nullptr) {
       res->add_entry(pattern, new_node);
     }
@@ -172,14 +178,14 @@ AnfNodePtr BuildTarget(const PatternPtr &pattern, const FuncGraphPtr &func_graph
   }
   // Build up the AnfNode in a recursive manner
   std::vector<AnfNodePtr> new_inputs;
-  auto prim_value_node = ProcessComplexPatternFirstInput(pattern, res, func_graph);
+  auto prim_value_node = ProcessComplexPatternFirstInput(pattern, res, func_graph, top_graph);
   MS_EXCEPTION_IF_NULL(prim_value_node);
   new_inputs.push_back(prim_value_node);
   for (auto &iter : target_inputs) {
     if (iter == pattern) {
       MS_LOG(EXCEPTION) << "Circle references. Got pattern: " + pattern->unique_name() + "\n";
     }
-    auto input_node = BuildTarget(iter, func_graph, res);
+    auto input_node = BuildTarget(iter, func_graph, top_graph, res);
     if (input_node == nullptr) {
       MS_LOG(EXCEPTION) << "Failed to build input node for pattern : " + iter->unique_name() + "\n";
     }
@@ -240,11 +246,12 @@ void Reset(PatternPtr pattern) {
 
 }  // namespace internal
 
-AnfNodePtr PythonPass::Run(const FuncGraphPtr &func_graph, const AnfNodePtr &node, const MatchResultPtr &res) {
+AnfNodePtr PythonPass::Run(const FuncGraphPtr &func_graph, const FuncGraphPtr &top_graph, const AnfNodePtr &node,
+                           const MatchResultPtr &res) {
   auto match_res = src_pattern_->match(node);
   if (match_res != nullptr) {
     res->merge(match_res);
-    auto new_node = internal::BuildTarget(dst_pattern_, func_graph, res);
+    auto new_node = internal::BuildTarget(dst_pattern_, func_graph, top_graph, res);
     internal::Reset(dst_pattern());
     return new_node;
   }
@@ -284,16 +291,19 @@ bool PythonPass::Run(const FuncGraphPtr &func_graph, const MatchResultPtr &res)
   }
   FuncGraphManagerPtr manager = func_graph->manager();
   MS_EXCEPTION_IF_NULL(manager);
-  manager->AddFuncGraph(func_graph);
-  auto graph_nodes_sorted = TopoSort(func_graph->output());
+  auto func_graphs = manager->func_graphs();
   bool changes = false;
-
-  // Traverse once
-  for (auto &node : graph_nodes_sorted) {
-    AnfNodePtr new_node = Run(func_graph, node, res);
-    if (new_node != nullptr && new_node != node) {
-      (void)manager->Replace(node, new_node);
-      changes = true;
+  for (auto &fg : func_graphs) {
+    manager->AddFuncGraph(fg);
+    auto graph_nodes_sorted = TopoSort(fg->output());
+    // Traverse once
+    for (auto &node : graph_nodes_sorted) {
+      AnfNodePtr new_node = Run(fg, func_graph, node, res);
+      if (new_node != nullptr && new_node != node) {
+        MS_LOG(WARNING) << "Matched";
+        (void)manager->Replace(node, new_node);
+        changes = true;
+      }
     }
   }
   return changes;

mindspore/ccsrc/frontend/optimizer/py_pass.h

@@ -39,7 +39,8 @@ class PythonPass {
   ~PythonPass() = default;
   bool Run(const FuncGraphPtr &func_graph, const MatchResultPtr &res);
   std::string name() const { return name_; }
-  AnfNodePtr Run(const FuncGraphPtr &func_graph, const AnfNodePtr &node, const MatchResultPtr &res);
+  AnfNodePtr Run(const FuncGraphPtr &func_graph, const FuncGraphPtr &top_graph, const AnfNodePtr &node,
+                 const MatchResultPtr &res);
   PatternPtr src_pattern() { return src_pattern_; }
   PatternPtr dst_pattern() { return dst_pattern_; }
 

mindspore/ccsrc/frontend/optimizer/py_pass_manager.cc

@@ -43,15 +43,19 @@ PyPassManagerPtr PyPassManager::GetInstance() {
 }
 
 PyPassManager::PyPassManager() {
-  phase_to_group_[Phase::RESOLVE] = std::make_shared<PassGroup>();
-  phase_to_group_[Phase::OPT] = std::make_shared<PassGroup>();
+  phase_to_group_[Phase::PREAD] = std::make_shared<PassGroup>("Pre_AD_PassGroup");
+  phase_to_group_[Phase::OPT] = std::make_shared<PassGroup>("After_OPT_PassGroup");
   res_ = std::make_shared<MatchResult>();
 }
 
 void PyPassManager::Registe(const std::string &pass_name, const PatternPtr &pattern, const PatternPtr &target,
-                            bool run_only_once) {
-  // NOTE: remove phase option to avoid unnecessary confusion.
-  auto cur_pg = GetPassGroup(Phase::OPT);
+                            bool requires_grad, bool run_only_once) {
+  PassGroupPtr cur_pg;
+  if (requires_grad) {
+    cur_pg = GetPassGroup(Phase::PREAD);
+  } else {
+    cur_pg = GetPassGroup(Phase::OPT);
+  }
   MS_EXCEPTION_IF_NULL(cur_pg);
   cur_pg->SetRunOnlyOnce(run_only_once);
   MS_EXCEPTION_IF_NULL(pattern);
@@ -62,11 +66,13 @@ void PyPassManager::Registe(const std::string &pass_name, const PatternPtr &patt
 }
 
 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";
+  auto opt_pm = GetPassGroup(Phase::OPT);
+  if (!opt_pm->DeletePass(pass_name)) {
+    MS_LOG(WARNING) << "Opt has no such pass : " + pass_name + "\n";
+  }
+  auto pre_ad_pm = GetPassGroup(Phase::PREAD);
+  if (!pre_ad_pm->DeletePass(pass_name)) {
+    MS_LOG(WARNING) << "Pre_AD has no such pass : " + pass_name + "\n";
   }
 }
 
@@ -92,7 +98,7 @@ void PyPassManager::ClearRes() {
 
 REGISTER_PYBIND_DEFINE(
   PyPassManager_, ([](const py::module *m) {
-    (void)py::enum_<Phase>(*m, "phase", py::arithmetic()).value("resolve", Phase::RESOLVE).value("opt", Phase::OPT);
+    (void)py::enum_<Phase>(*m, "phase", py::arithmetic()).value("pre_ad", Phase::PREAD).value("opt", Phase::OPT);
     (void)py::class_<PyPassManager, std::shared_ptr<PyPassManager>>(*m, "PyPassManager_")
       .def(py::init([]() { return PyPassManager::GetInstance(); }))
       .def("registe", &PyPassManager::Registe, "Registe python pass")

mindspore/ccsrc/frontend/optimizer/py_pass_manager.h

@@ -38,7 +38,7 @@ namespace python_pass {
 class PyPassManager;
 using PyPassManagerPtr = std::shared_ptr<PyPassManager>;
 
-enum Phase { RESOLVE, OPT };
+enum Phase { PREAD, OPT };
 
 class PyPassManager {
  protected:
@@ -52,8 +52,8 @@ class PyPassManager {
   // Access the only global instance
   static PyPassManagerPtr GetInstance();
   virtual ~PyPassManager() = default;
-  void Registe(const std::string &pass_name, const PatternPtr &pattern, const PatternPtr &target,
-               bool run_only_once = false);
+  void Registe(const std::string &pass_name, const PatternPtr &pattern, const PatternPtr &target, bool requires_grad,
+               bool run_only_once);
   void Unregiste(const std::string &pass_name);
   void GenNewParameter(const PatternPtr &parameter);
   PassGroupPtr GetPassGroup(Phase phase);

mindspore/ccsrc/pipeline/jit/action.cc

@@ -301,6 +301,8 @@ bool OptimizeAction(const ResourcePtr &res, const std::vector<PassItem> &passes)
   return true;
 }
 
+bool OptInlineAction(const ResourcePtr &res) { return OptimizeAction(res, kInlinePasses); }
+
 bool GeOptimizeAction(const ResourcePtr &res) { return OptimizeAction(res, kGePasses); }
 
 bool VmOptimizeAction(const ResourcePtr &res) { return OptimizeAction(res, kVmPasses); }
@@ -473,7 +475,12 @@ bool ActionPyStub(const ResourcePtr &res, opt::python_pass::Phase phase) {
   return ppm->GetPassGroup(phase)->Run(res->func_graph());
 }
 
-bool ResolveActionPyStub(const ResourcePtr &res) { return true || ActionPyStub(res, opt::python_pass::Phase::RESOLVE); }
+bool PreAdActionPyStub(const ResourcePtr &res) {
+  if (!ActionPyStub(res, opt::python_pass::Phase::PREAD)) {
+    MS_LOG(DEBUG) << "No Match.";
+  }
+  return true;
+}
 
 bool OptActionVmPyStub(const ResourcePtr &res) {
   if (ActionPyStub(res, opt::python_pass::Phase::OPT)) {
@@ -529,12 +536,14 @@ static std::vector<ActionItem> CommonPipeline() {
   if (!multi_graphs) {
     actions.emplace_back(std::make_pair("combine_like_graphs", CombineLikeGraphs));
   }
-  // Add resolve-stage python pass stub
-  actions.emplace_back(std::make_pair("py_resolve", ResolveActionPyStub));
 
   actions.emplace_back(std::make_pair("inference_opt_prepare", InferenceOptPrepareAction));
   // Evaluate type and shape, and specialize
   actions.emplace_back(std::make_pair("abstract_specialize", AbstractSpecializeAction));
+  // Do data structure simplifications and inline
+  actions.emplace_back(std::make_pair("inline", OptInlineAction));
+  // Add pre-ad, post-inline python pass stub
+  actions.emplace_back(std::make_pair("py_pre_ad", PreAdActionPyStub));
 
   return actions;
 }

mindspore/ccsrc/pipeline/jit/pass.cc

@@ -165,6 +165,12 @@ OptPassGroupMap GetOptPassesA(const opt::irpass::OptimizeIRPassLib &irpass) {
   return map_a;
 }
 
+OptPassGroupMap GetA1A2(const opt::irpass::OptimizeIRPassLib &irpass) {
+  auto opt_a = GetOptPassesA(irpass);
+  OptPassGroupMap a1_a2({opt_a[0], opt_a[1]});
+  return a1_a2;
+}
+
 OptPassGroupMap GetOptPassesAfterCconv(const opt::irpass::OptimizeIRPassLib &irpass) {
   opt::OptPassConfig c_1 = opt::OptPassConfig({
     // Safe inlining,
@@ -270,6 +276,7 @@ static std::unordered_map<std::string, std::shared_ptr<Optimizer>> g_pass_opts =
 void InitOpt(const ResourcePtr &res) {
   if (g_pass_opts.size() == 0) {
     opt::irpass::OptimizeIRPassLib irpass;
+    g_pass_opts["a1a2"] = Optimizer::MakeOptimizer("a1a2", res, GetA1A2(irpass));
     g_pass_opts["opt_a"] = Optimizer::MakeOptimizer("opt_a", res, GetOptPassesA(irpass));
     g_pass_opts["opt_b"] = Optimizer::MakeOptimizer("opt_b", res, GetOptPassesB(irpass), false, true);
     g_pass_opts["opt_after_cconv"] =
@@ -318,6 +325,7 @@ bool OptPassGroup(const ResourcePtr &res, const std::string &name) {
   return true;
 }
 
+bool OptPassA1A2(const ResourcePtr &res) { return OptPassGroup(res, "a1a2"); }
 bool OptPassAGroup(const ResourcePtr &res) { return OptPassGroup(res, "opt_a"); }
 bool OptPassBGroup(const ResourcePtr &res) { return OptPassGroup(res, "opt_b"); }
 bool OptPassAfterCconvGroup(const ResourcePtr &res) { return OptPassGroup(res, "opt_after_cconv"); }
@@ -440,5 +448,7 @@ std::vector<PassItem> kPynativePasses = {{"opt_a", OptPassAGroup},
                                          {"cconv", CconvPass},
                                          {"transform_top", TransformTopGraphPass},
                                          {"transform_graph", OptPassTransformGraphGroup}};
+
+std::vector<PassItem> kInlinePasses = {{"simplify_data_structures", SimplifyDataStructuresPass}, {"a1a2", OptPassA1A2}};
 }  // namespace pipeline
 }  // namespace mindspore

mindspore/ccsrc/pipeline/jit/pass.h

@@ -29,6 +29,7 @@ using PassItem = std::pair<std::string, std::function<bool(ResourcePtr)>>;
 
 extern std::vector<PassItem> kGePasses;
 extern std::vector<PassItem> kVmPasses;
+extern std::vector<PassItem> kInlinePasses;
 extern std::vector<PassItem> kPynativePasses;
 
 bool CconvPass(const ResourcePtr &res);

mindspore/graph_utils/python_pass/__init__.py

@@ -13,14 +13,14 @@
 # limitations under the License.
 # ============================================================================
 """Reference for python pass registration."""
-from .python_pass_register import registe_pass, unregiste_pass, gen_new_parameter, cancel_new_parameter, set_renorm,\
-    set_reopt
+from .python_pass_register import registe_pass, unregiste_pass, gen_new_parameter, cancel_new_parameter, _set_renorm,\
+    _set_reopt
 
 __all__ = [
     "registe_pass",
     "unregiste_pass",
     "gen_new_parameter",
     "cancel_new_parameter",
-    "set_renorm",
-    "set_reopt"
+    "_set_renorm",
+    "_set_reopt"
     ]

mindspore/graph_utils/python_pass/python_pass_register.py

@@ -23,22 +23,26 @@ __all__ = [
     "unregiste_pass",
     "gen_new_parameter",
     "cancel_new_parameter",
-    "set_renorm",
-    "set_reopt"
+    "_set_renorm",
+    "_set_reopt"
 ]
 class PyPassManager(PyPassManager_):
     r"""
     Used to registe and unregiste python passes which can be used to alter graphs.
 
     Args:
+        requires_grad(bool): Do automatic-differentiation after modified graph if true. Default: True
         run_only_once (bool): Specify whether or not to run pass only once. Default: False.
 
     Raises:
         TypeError: If argument has invalid type.
     """
-    def __init__(self, run_only_once=False):
+    def __init__(self, requires_grad=True, run_only_once=False):
+        if not isinstance(requires_grad, bool):
+            raise TypeError(f"Expect bool, got : ({type(requires_grad)}){requires_grad}")
         if not isinstance(run_only_once, bool):
             raise TypeError(f"Expect bool, got : ({type(run_only_once)}){run_only_once}")
+        self.requires_grad = requires_grad
         self.run_only_once_ = run_only_once
         PyPassManager_.__init__(self)
 
@@ -51,7 +55,7 @@ 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.run_only_once_)
+        super().registe(pass_name, pattern, target, self.requires_grad, self.run_only_once_)
 
     def unregiste(self, py_pass):
         if isinstance(py_pass, str):
@@ -81,11 +85,12 @@ class PyPassManager(PyPassManager_):
             raise TypeError(f"Expect do_reopt to be a bool, got {do_reopt}")
         super().set_reopt(do_reopt)
 
-def registe_pass(run_only_once=False):
+def registe_pass(requires_grad=True, run_only_once=False):
     """
     Registe python pass to specified pipeline phase which would be used in compilation.
 
     Args:
+        requires_grad(bool): Do automatic-differentiation after modified graph if true. Default: True
         run_only_once(bool): Run this pass only once if set true. Otherwise run the pass until converge. Default: False.
 
     Returns:
@@ -99,7 +104,7 @@ def registe_pass(run_only_once=False):
         >>>     target = IsPrimTypeOf("ReLU6")
         >>>     return pattern, target
     """
-    return PyPassManager(run_only_once)
+    return PyPassManager(requires_grad, run_only_once)
 
 def unregiste_pass(py_pass):
     """
@@ -157,7 +162,7 @@ def cancel_new_parameter(pattern):
     ppm = PyPassManager()
     ppm.unregiste(pattern.para_name)
 
-def set_renorm(should_renorm):
+def _set_renorm(should_renorm):
     """
     Set whether or not to do renormalization after modified graph in python pass(es).
 
@@ -171,7 +176,7 @@ def set_renorm(should_renorm):
     ppm = PyPassManager()
     ppm.set_renorm(should_renorm)
 
-def set_reopt(do_reopt):
+def _set_reopt(do_reopt):
     """
     Set whether or not to do optimization after modified graph in python pass(es).
 

tests/ut/python/optimizer/test_python_pass.py

@@ -19,8 +19,8 @@ import mindspore.nn as nn
 from mindspore import context
 from mindspore.common.tensor import Tensor
 from mindspore.ops import operations as P
-from mindspore.graph_utils.python_pass import registe_pass, unregiste_pass, set_renorm, gen_new_parameter,\
-    cancel_new_parameter, set_reopt
+from mindspore.graph_utils.python_pass import registe_pass, unregiste_pass, _set_renorm, gen_new_parameter,\
+    cancel_new_parameter, _set_reopt
 from mindspore.common.api import _generate_pip_args
 from mindspore._c_expression import generate_key, Executor_
 from mindspore.graph_utils.graph_pattern import OneOf, Prim, Call, NoneOf, Any, NewTensor, NewParameter, Imm
@@ -157,8 +157,8 @@ def test_isnot_pattern_0():
     Test IsNot pattern which expresses the IsNot semantics.
     Case: IsNot pass failed to match
     """
-    set_renorm(False)
-    set_reopt(False)
+    _set_renorm(False)
+    _set_reopt(False)
     class ConvBN(nn.Cell):
         def __init__(self):
             super(ConvBN, self).__init__()
@@ -176,7 +176,7 @@ def test_isnot_pattern_0():
     inputs = Tensor(np.random.normal(0, 1, (10, 32, 32, 32)), mindspore.float32)
     conv_bn_model = ConvBN()
 
-    @registe_pass(run_only_once=True)
+    @registe_pass(requires_grad=False, run_only_once=True)
     def single_bn_pass():
         """
         Sub a BN which does NOT take Conv as inputs to ReLU6.
@@ -188,7 +188,7 @@ def test_isnot_pattern_0():
         target = Call(P.ReLU6(), [pattern_0])
         return pattern, target
 
-    @registe_pass(run_only_once=True)
+    @registe_pass(requires_grad=False, run_only_once=True)
     def bn_pass():
         """
         Sub a BN to Softmax.
@@ -202,7 +202,7 @@ def test_isnot_pattern_0():
     unregiste_pass(bn_pass)
     assert "ReLU6" not in transformed_repr
     assert "Softmax" in transformed_repr
-    set_renorm(True)
+    _set_renorm(True)
 
 def test_isnot_pattern_1():
     """
@@ -234,12 +234,12 @@ def test_newtensor_pattern():
     """
     Test NewTensor pattern in the target
     """
-    set_renorm(False)
-    set_reopt(False)
+    _set_renorm(False)
+    _set_reopt(False)
     inputs = Tensor(np.ones([42]), mindspore.float16)
     softmax_model = nn.Softmax()
 
-    @registe_pass(run_only_once=True)
+    @registe_pass(requires_grad=False, run_only_once=True)
     def softmax_addn_pass():
         x = Any()
         pattern = Call(P.Softmax(), [x])
@@ -252,7 +252,7 @@ def test_newtensor_pattern():
     unregiste_pass(softmax_addn_pass)
     assert "AddN" in transformed_repr
     assert "Softmax" not in transformed_repr
-    set_renorm(True)
+    _set_renorm(True)
 
 def test_newparameter_pattern():
     """
@@ -261,9 +261,9 @@ def test_newparameter_pattern():
     inputs = Tensor(np.ones([42]), mindspore.float16)
     softmax_model = nn.Softmax()
 
-    set_renorm(False)
-    set_reopt(False)
-    @registe_pass(run_only_once=True)
+    _set_renorm(False)
+    _set_reopt(False)
+    @registe_pass(requires_grad=False, run_only_once=True)
     def softmax_addn_pass():
         x = Any()
         pattern = Call(P.Softmax(), [x])
@@ -288,9 +288,9 @@ def test_imm_target():
     inputs = Tensor(np.ones([42]), mindspore.float16)
     softmax_model = nn.Softmax()
 
-    set_renorm(False)
-    set_reopt(False)
-    @registe_pass(run_only_once=True)
+    _set_renorm(False)
+    _set_reopt(False)
+    @registe_pass(requires_grad=False, run_only_once=True)
     def softmax_pass():
         x = Any()
         pattern = Call(P.Softmax(), [x])
@@ -313,10 +313,10 @@ def test_gen_new_parameter():
 
     default_tensor = Tensor(np.ones((4, 4)), mindspore.float32)
     new_para = NewParameter("Merlin", default_tensor)
-    set_renorm(False)
-    set_reopt(False)
+    _set_renorm(False)
+    _set_reopt(False)
     gen_new_parameter(new_para)
-    @registe_pass(run_only_once=True)
+    @registe_pass(requires_grad=False, run_only_once=True)
     def softmax_make_tuple_pass():
         x = Any()
         softmax = P.Softmax()