support grad on graph with variable arguments

use unpack graph primitive instead

add testcases for all grad interface

remove debug log

format code

remove dumpfuncgraph

resolve clang-format

resolve reviews

resolve cpplint

fix review

mindspore/ccsrc/operator/composite/composite.cc

@@ -1199,51 +1199,6 @@ FuncGraphPtr TensorSlice::GenerateFuncGraph(const AbstractBasePtrList& args_spec
   return ret_graph;
 }
 
-FuncGraphPtr UnpackCall::GenerateFuncGraph(const AbstractBasePtrList& args_spec_list) {
-  // slice a tensor
-  // args: tensor, slice or slice tuple
-  const std::string op_name = std::string("UnpackCall");
-  size_t arg_length = args_spec_list.size();
-  if (arg_length < 2) {
-    MS_LOG(EXCEPTION) << "" << op_name << " requires at least two args, but got " << arg_length << ".";
-  }
-
-  (void)abstract::CheckArg<AbstractFunction>(op_name, args_spec_list, 0);
-  FuncGraphPtr ret_graph = std::make_shared<FuncGraph>();
-  ret_graph->set_flags(FUNC_GRAPH_FLAG_CORE, true);
-
-  AnfNodePtr fnNode = ret_graph->add_parameter();
-  std::vector<AnfNodePtr> elems;
-  elems.push_back(fnNode);
-  for (size_t index = 1; index < arg_length; index++) {
-    MS_EXCEPTION_IF_NULL(args_spec_list[index]);
-    if (args_spec_list[index]->isa<AbstractTuple>()) {
-      AbstractTuplePtr arg_tuple = dyn_cast<AbstractTuple>(args_spec_list[index]);
-      AnfNodePtr para_tuple = ret_graph->add_parameter();
-      for (size_t i = 0; i < arg_tuple->size(); ++i) {
-        elems.push_back(
-          ret_graph->NewCNode({NewValueNode(prim::kPrimTupleGetItem), para_tuple, NewValueNode(SizeToInt(i))}));
-      }
-    } else if (args_spec_list[index]->isa<AbstractDictionary>()) {
-      AbstractDictionaryPtr arg_dict = dyn_cast<AbstractDictionary>(args_spec_list[index]);
-      AnfNodePtr para_dict = ret_graph->add_parameter();
-      auto dict_elems = arg_dict->elements();
-      (void)std::transform(
-        dict_elems.begin(), dict_elems.end(), std::back_inserter(elems),
-        [ret_graph, para_dict](const AbstractAttribute& item) {
-          return ret_graph->NewCNode(
-            {NewValueNode(prim::kPrimMakeKeywordArg), NewValueNode(item.first),
-             ret_graph->NewCNode({NewValueNode(prim::kPrimDictGetItem), para_dict, NewValueNode(item.first)})});
-        });
-    } else {
-      MS_LOG(EXCEPTION) << "" << op_name << " require args should be tuple or dict, but got "
-                        << args_spec_list[index]->ToString();
-    }
-  }
-  ret_graph->set_output(ret_graph->NewCNode(elems));
-  return ret_graph;
-}
-
 REGISTER_PYBIND_DEFINE(
   TupleAdd_, ([](const py::module* m) {
     (void)py::class_<TupleAdd, MetaFuncGraph, std::shared_ptr<TupleAdd>>(*m, "TupleAdd_").def(py::init<std::string&>());
@@ -1258,10 +1213,5 @@ REGISTER_PYBIND_DEFINE(TensorSlice_, ([](const py::module* m) {
                          (void)py::class_<TensorSlice, MetaFuncGraph, std::shared_ptr<TensorSlice>>(*m, "TensorSlice_")
                            .def(py::init<std::string&>());
                        }));
-
-REGISTER_PYBIND_DEFINE(UnpackCall_, ([](const py::module* m) {
-                         (void)py::class_<UnpackCall, MetaFuncGraph, std::shared_ptr<UnpackCall>>(*m, "UnpackCall_")
-                           .def(py::init<std::string&>());
-                       }));
 }  // namespace prim
 }  // namespace mindspore

mindspore/ccsrc/operator/composite/composite.h

@@ -29,6 +29,7 @@
 #include "operator/composite/zip_operation.h"
 #include "operator/composite/list_append_operation.h"
 #include "operator/composite/do_signature.h"
+#include "operator/composite/unpack_call.h"
 #include "pipeline/static_analysis/static_analysis.h"
 #include "utils/misc.h"
 #include "utils/any.h"
@@ -154,7 +155,7 @@ class GradOperation : public MetaFuncGraph {
   FuncGraphPtr GetGrad(AnfNodePtr ptrNode, const AnfNodePtr& weights, const std::vector<AnfNodePtr>& ptrParams,
                        bool applyJ = false);
   FuncGraphPtr GenerateFuncGraph(const AbstractBasePtrList& args_spec_list) override;
-
+  bool sens_param() const { return sens_param_; }
   bool get_all_;
   bool get_by_list_;
   bool sens_param_;
@@ -208,17 +209,6 @@ class TensorSlice : public MetaFuncGraph {
 };
 using TensorSlicePtr = std::shared_ptr<TensorSlice>;
 
-// Expand the tuple and dict parameters generated when parsing the function call,
-// and generate positional parameters and key-value pairs for function.
-class UnpackCall : public MetaFuncGraph {
- public:
-  explicit UnpackCall(const std::string& name) : MetaFuncGraph(name) {}
-  ~UnpackCall() override = default;
-  MS_DECLARE_PARENT(UnpackCall, MetaFuncGraph)
-  FuncGraphPtr GenerateFuncGraph(const AbstractBasePtrList& args_spec_list) override;
-  friend bool operator==(const UnpackCall& lhs, const UnpackCall& rhs) { return lhs.name_ == rhs.name_; }
-};
-using UnpackCallPtr = std::shared_ptr<UnpackCall>;
 }  // namespace prim
 }  // namespace mindspore
 

mindspore/ccsrc/operator/composite/unpack_call.cc

@@ -0,0 +1,94 @@
+/**
+ * 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 "operator/composite/unpack_call.h"
+#include <algorithm>
+#include <utility>
+
+#include "./common.h"
+#include "pipeline/static_analysis/abstract_value.h"
+#include "pipeline/static_analysis/dshape.h"
+#include "pipeline/static_analysis/param_validator.h"
+#include "operator/cc_implementations.h"
+#include "ir/anf.h"
+#include "optimizer/opt.h"
+#include "utils/symbolic.h"
+#include "pybind_api/api_register.h"
+
+namespace mindspore {
+// namespace to support composite operators definition
+namespace prim {
+using mindspore::abstract::AbstractAttribute;
+using mindspore::abstract::AbstractBase;
+using mindspore::abstract::AbstractDictionary;
+using mindspore::abstract::AbstractDictionaryPtr;
+using mindspore::abstract::AbstractFunction;
+using mindspore::abstract::AbstractKeywordArg;
+using mindspore::abstract::AbstractTuple;
+using mindspore::abstract::AbstractTuplePtr;
+
+FuncGraphPtr UnpackCall::GenerateFuncGraph(const AbstractBasePtrList& args_spec_list) {
+  // slice a tensor
+  // args: tensor, slice or slice tuple
+  const std::string op_name = std::string("UnpackCall");
+  size_t arg_length = args_spec_list.size();
+  if (arg_length < 2) {
+    MS_LOG(EXCEPTION) << op_name << " requires at least two args, but got " << arg_length << ".";
+  }
+
+  (void)abstract::CheckArg<AbstractFunction>(op_name, args_spec_list, 0);
+  auto ret_graph = std::make_shared<FuncGraph>();
+  ret_graph->set_flags(FUNC_GRAPH_FLAG_CORE, true);
+
+  AnfNodePtr fnNode = ret_graph->add_parameter();
+  std::vector<AnfNodePtr> elems;
+  elems.push_back(fnNode);
+  for (size_t index = 1; index < arg_length; index++) {
+    MS_EXCEPTION_IF_NULL(args_spec_list[index]);
+    if (args_spec_list[index]->isa<AbstractTuple>()) {
+      auto arg_tuple = args_spec_list[index]->cast<AbstractTuplePtr>();
+      AnfNodePtr para_tuple = ret_graph->add_parameter();
+      for (size_t i = 0; i < arg_tuple->size(); ++i) {
+        elems.push_back(
+          ret_graph->NewCNode({NewValueNode(prim::kPrimTupleGetItem), para_tuple, NewValueNode(SizeToInt(i))}));
+      }
+    } else if (args_spec_list[index]->isa<AbstractDictionary>()) {
+      AbstractDictionaryPtr arg_dict = args_spec_list[index]->cast<AbstractDictionaryPtr>();
+      AnfNodePtr para_dict = ret_graph->add_parameter();
+      auto dict_elems = arg_dict->elements();
+      (void)std::transform(dict_elems.begin(), dict_elems.end(), std::back_inserter(elems),
+                           [ret_graph, para_dict](const AbstractAttribute& item) {
+                             auto dict_get_item = ret_graph->NewCNode(
+                               {NewValueNode(prim::kPrimDictGetItem), para_dict, NewValueNode(item.first)});
+                             return ret_graph->NewCNode(
+                               {NewValueNode(prim::kPrimMakeKeywordArg), NewValueNode(item.first), dict_get_item});
+                           });
+    } else {
+      MS_LOG(EXCEPTION) << op_name << " require args should be tuple or dict, but got "
+                        << args_spec_list[index]->ToString();
+    }
+  }
+  ret_graph->set_output(ret_graph->NewCNode(elems));
+  return ret_graph;
+}
+
+REGISTER_PYBIND_DEFINE(UnpackCall_, ([](const py::module* m) {
+                         (void)py::class_<UnpackCall, MetaFuncGraph, std::shared_ptr<UnpackCall>>(*m, "UnpackCall_")
+                           .def(py::init<std::string&>());
+                       }));
+
+}  // namespace prim
+}  // namespace mindspore

mindspore/ccsrc/operator/composite/unpack_call.h

@@ -0,0 +1,54 @@
+/**
+ * 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_CCSRC_OPERATOR_COMPOSITE_UNPACK_CALL_H_
+#define MINDSPORE_CCSRC_OPERATOR_COMPOSITE_UNPACK_CALL_H_
+
+#include <vector>
+#include <string>
+#include <unordered_map>
+#include <utility>
+#include <map>
+#include <set>
+#include <memory>
+
+#include "pipeline/static_analysis/static_analysis.h"
+#include "utils/misc.h"
+#include "utils/any.h"
+#include "ir/dtype.h"
+#include "ir/meta_func_graph.h"
+#include "common/utils.h"
+
+namespace mindspore {
+// namespace to support composite operators definition
+namespace prim {
+
+// Expand the tuple and dict parameters generated when parsing the function call,
+// and generate positional parameters and key-value pairs for function.
+class UnpackCall : public MetaFuncGraph {
+ public:
+  explicit UnpackCall(const std::string& name) : MetaFuncGraph(name) {}
+  ~UnpackCall() override = default;
+  MS_DECLARE_PARENT(UnpackCall, MetaFuncGraph)
+  FuncGraphPtr GenerateFuncGraph(const AbstractBasePtrList& args_spec_list) override;
+  friend bool operator==(const UnpackCall& lhs, const UnpackCall& rhs) { return lhs.name_ == rhs.name_; }
+};
+using UnpackCallPtr = std::shared_ptr<UnpackCall>;
+
+}  // namespace prim
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_OPERATOR_COMPOSITE_UNPACK_CALL_H_

mindspore/ccsrc/operator/ops.h

@@ -246,6 +246,21 @@ class DoSignaturePrimitive : public Primitive {
   ValuePtr function_;
 };
 using DoSignaturePrimitivePtr = std::shared_ptr<DoSignaturePrimitive>;
+
+class UnpackGraphPrimitive : public Primitive {
+ public:
+  explicit UnpackGraphPrimitive(const std::string& name, const bool& with_sens, const bool& need_unpack_args)
+      : Primitive("UnpackGraph"), with_sens_in_args_(with_sens), need_unpack_args_(need_unpack_args) {}
+  ~UnpackGraphPrimitive() override = default;
+  MS_DECLARE_PARENT(UnpackGraphPrimitive, Primitive)
+  bool with_sens_in_args() const { return with_sens_in_args_; }
+  bool need_unpack_args() const { return need_unpack_args_; }
+
+ private:
+  bool with_sens_in_args_;
+  bool need_unpack_args_;
+};
+using UnpackGraphPrimitivePtr = std::shared_ptr<UnpackGraphPrimitive>;
 }  // namespace prim
 }  // namespace mindspore
 

mindspore/ccsrc/optimizer/irpass.cc

@@ -39,6 +39,7 @@
 #include "optimizer/irpass/specialize_transform.h"
 #include "optimizer/irpass/incorporate_getitem.h"
 #include "optimizer/irpass/incorporate_call.h"
+#include "optimizer/irpass/grad_var_prepare.h"
 
 namespace mindspore {
 namespace opt {
@@ -123,6 +124,11 @@ ResolveIRPassLib::ResolveIRPassLib() {
   resolver_resolve_ = MakeSubstitution(ResolverResolve(), "resolver_resolve", prim::kPrimResolve);
   resolver_getattr_ = MakeSubstitution(ResolverGetattr(), "resolver_getattr", prim::kPrimGetAttr);
 }
+
+InferenceOptPrepareLib::InferenceOptPrepareLib() {
+  grad_var_prepare_ = MakeSubstitution(GradVarPrepare(), "grad_var_prepare", IsCNode);
+}
+
 }  // namespace irpass
 }  // namespace opt
 }  // namespace mindspore

mindspore/ccsrc/optimizer/irpass.h

@@ -102,6 +102,13 @@ class ResolveIRPassLib {
   SubstitutionPtr resolver_getattr_;
 };
 
+class InferenceOptPrepareLib {
+ public:
+  InferenceOptPrepareLib();
+  ~InferenceOptPrepareLib() = default;
+  SubstitutionPtr grad_var_prepare_;
+};
+
 // predicate functions
 inline bool IsNode(const AnfNodePtr &) { return true; }
 
@@ -151,6 +158,7 @@ inline bool IsCNodeDup(const AnfNodePtr &node) {
   }
   return false;
 }
+
 }  // namespace irpass
 }  // namespace opt
 }  // namespace mindspore

mindspore/ccsrc/optimizer/irpass/grad_var_prepare.cc

@@ -0,0 +1,144 @@
+/**
+ * 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 "optimizer/irpass/grad_var_prepare.h"
+#include <vector>
+#include <algorithm>
+#include <unordered_map>
+#include <memory>
+
+#include "operator/composite/composite.h"
+#include "operator/ops.h"
+#include "optimizer/irpass.h"
+#include "optimizer/optimizer.h"
+#include "ir/visitor.h"
+#include "ir/func_graph.h"
+#include "ir/func_graph_cloner.h"
+
+namespace mindspore {
+namespace opt {
+namespace irpass {
+
+static AnfNodePtr GenerateUnpackGraphNode(std::vector<AnfNodePtr> inputs_y, FuncGraphPtr func_graph,
+                                          AnfNodePtr func_node, bool is_unpack, bool sens_param) {
+  MS_EXCEPTION_IF_NULL(func_graph);
+  MS_EXCEPTION_IF_NULL(func_node);
+  std::vector<AnfNodePtr> nodes;
+  AnfNodePtr unpack_graph_node = nullptr;
+  if (is_unpack) {
+    auto unpack_graph = std::make_shared<prim::UnpackGraphPrimitive>("unpack_graph", sens_param, true);
+    nodes.push_back(NewValueNode(unpack_graph));
+    nodes.push_back(func_node);
+    // {unpackcall, {GradOperation, ...}, args...}
+    std::transform(inputs_y.begin() + 2, inputs_y.end(), std::back_inserter(nodes),
+                   [](const AnfNodePtr& node) { return node; });
+    unpack_graph_node = func_graph->NewCNode(nodes);
+  } else {
+    auto unpack_graph = std::make_shared<prim::UnpackGraphPrimitive>("unpack_graph", sens_param, false);
+    nodes.push_back(NewValueNode(unpack_graph));
+    nodes.push_back(func_node);
+    // {{GradOperation, ...}, args...}
+    std::transform(inputs_y.begin() + 1, inputs_y.end(), std::back_inserter(nodes),
+                   [](const AnfNodePtr& node) { return node; });
+    unpack_graph_node = func_graph->NewCNode(nodes);
+  }
+  return unpack_graph_node;
+}
+
+// get metagraph of value node
+MetaFuncGraphPtr GetMetaFuncGraphOfValueNode(const AnfNodePtr& node) {
+  ValuePtr value;
+  if (IsValueNode<prim::DoSignaturePrimitive>(node)) {
+    value = GetValueNode(node)->cast<prim::DoSignaturePrimitivePtr>()->function();
+  } else {
+    value = GetValueNode(node);
+  }
+  if (value == nullptr) {
+    return nullptr;
+  }
+  return value->cast<MetaFuncGraphPtr>();
+}
+
+// check if node is a specific metafuncgraph op
+bool IsMetaFuncGraph(const AnfNodePtr& node, const MetaFuncGraphPtr meta_func_graph) {
+  if (node != nullptr) {
+    auto meta_func_graph_ptr = GetMetaFuncGraphOfValueNode(node);
+    if (meta_func_graph_ptr == nullptr) {
+      return false;
+    }
+
+    if (meta_func_graph_ptr->type_name() == meta_func_graph->type_name()) {
+      return true;
+    }
+  }
+  return false;
+}
+
+// {{GradOperation, g, w}, Ys}
+// {UnPackCall, {GradOperation, g, w}, Ys}
+AnfNodePtr GradVarPrepare::operator()(const OptimizerPtr&, const AnfNodePtr& node) {
+  if (!node->isa<CNode>() || node->func_graph() == nullptr) {
+    return nullptr;
+  }
+
+  // {{...}, Ys}
+  auto inputs_y = node->cast<CNodePtr>()->inputs();
+  std::vector<AnfNodePtr> inputs_x;
+  if (IsCNode(inputs_y[0])) {
+    inputs_x = inputs_y[0]->cast<CNodePtr>()->inputs();
+  } else if (IsMetaFuncGraph(inputs_y[0], unpack_op_) && IsCNode(inputs_y[1])) {
+    inputs_x = inputs_y[1]->cast<CNodePtr>()->inputs();
+  } else {
+    return nullptr;
+  }
+
+  // {{...}, Xs}
+  if (inputs_x.size() < 2) {
+    return nullptr;
+  }
+
+  // {GradOperation, g, w} or {GradOperation, g}
+  if (!IsMetaFuncGraph(inputs_x[0], grad_op_)) {
+    return nullptr;
+  }
+
+  auto meta_func = GetMetaFuncGraphOfValueNode(inputs_x[0]);
+  if (meta_func == nullptr) {
+    return nullptr;
+  }
+  auto grad_op_ptr = meta_func->cast<prim::GradOperationPtr>();
+  auto func_node = inputs_x[1];
+  if (!IsValueNode<FuncGraph>(func_node)) {
+    return nullptr;
+  }
+
+  AnfNodePtr unpack_graph_node =
+    GenerateUnpackGraphNode(inputs_y, node->cast<CNodePtr>()->func_graph(), func_node,
+                            IsMetaFuncGraph(inputs_y[0], unpack_op_), grad_op_ptr->sens_param());
+  // constuct new grad_opration
+  inputs_x[1] = unpack_graph_node;
+  auto grad_op_cnode = node->func_graph()->NewCNode(inputs_x);
+  if (IsMetaFuncGraph(inputs_y[0], unpack_op_)) {
+    inputs_y[1] = grad_op_cnode;
+  } else {
+    inputs_y[0] = grad_op_cnode;
+  }
+  auto cnode = node->func_graph()->NewCNode(inputs_y);
+  return cnode;
+}
+}  // namespace irpass
+}  // namespace opt
+}  // namespace mindspore

mindspore/ccsrc/optimizer/irpass/grad_var_prepare.h

@@ -0,0 +1,55 @@
+/**
+ * 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_CCSRC_OPTIMIZER_IRPASS_GRAD_VAR_PREPARE_H_
+#define MINDSPORE_CCSRC_OPTIMIZER_IRPASS_GRAD_VAR_PREPARE_H_
+
+#include <vector>
+#include <algorithm>
+#include <unordered_map>
+#include <memory>
+
+#include "operator/composite/composite.h"
+#include "operator/ops.h"
+#include "optimizer/irpass.h"
+#include "optimizer/optimizer.h"
+#include "ir/visitor.h"
+#include "ir/func_graph.h"
+#include "ir/func_graph_cloner.h"
+
+namespace mindspore {
+namespace opt {
+namespace irpass {
+
+// {{GradOperation, g, w}, Ys}
+// {UnPackCall, {GradOperation, g, w}, Ys}
+class GradVarPrepare : public AnfVisitor {
+ public:
+  GradVarPrepare()
+      : grad_op_(std::make_shared<prim::GradOperation>("grad")),
+        unpack_op_(std::make_shared<prim::UnpackCall>("unpack_call")) {}
+  ~GradVarPrepare() override = default;
+
+  AnfNodePtr operator()(const OptimizerPtr &, const AnfNodePtr &node) override;
+
+ private:
+  MetaFuncGraphPtr grad_op_;
+  MetaFuncGraphPtr unpack_op_;
+};
+}  // namespace irpass
+}  // namespace opt
+}  // namespace mindspore
+#endif  // MINDSPORE_CCSRC_OPTIMIZER_IRPASS_GRAD_VAR_PREPARE_H_

mindspore/ccsrc/pipeline/action.cc

@@ -175,10 +175,10 @@ bool CombineLikeGraphs(const ResourcePtr&) {
 
 bool SymbolResolveAction(const ResourcePtr& res) {
   if (res->manager() == nullptr) {
-    MS_LOG(EXCEPTION) << "Resolve error.";
+    MS_LOG(EXCEPTION) << "SymbolResolve error, manager is null";
   }
   if (res->func_graph() == nullptr) {
-    MS_LOG(EXCEPTION) << "Resolve error";
+    MS_LOG(EXCEPTION) << "SymbolResolve error, graph is null";
   }
   FuncGraphPtr func_graph = res->func_graph();
   auto succ = parse::ResolveFuncGraph(func_graph, res);
@@ -194,6 +194,16 @@ bool SymbolResolveAction(const ResourcePtr& res) {
   return succ;
 }
 
+bool InferenceOptPrepareAction(const ResourcePtr& res) {
+  if (res->manager() == nullptr) {
+    MS_LOG(EXCEPTION) << "InferenceOptPrepare error, manager is null.";
+  }
+  if (res->func_graph() == nullptr) {
+    MS_LOG(EXCEPTION) << "InferenceOptPrepare error, graph is null.";
+  }
+  return InferenceOptPreparePass(res);
+}
+
 bool AbstractSpecializeAction(const ResourcePtr& res) {
   if (res->func_graph() == nullptr) {
     MS_LOG(EXCEPTION) << "AbstractSpecialize error";
@@ -331,7 +341,7 @@ static std::vector<ActionItem> CommonPipeline() {
   // Resolve the python func
   actions.emplace_back(std::make_pair("symbol_resolve", SymbolResolveAction));
   actions.emplace_back(std::make_pair("combine_like_graphs", CombineLikeGraphs));
-
+  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));
 

mindspore/ccsrc/pipeline/pass.cc

@@ -160,6 +160,13 @@ OptPassGroupMap GetControlPhases(const opt::irpass::OptimizeIRPassLib& irpass) {
   return map;
 }
 
+OptPassGroupMap GetInferenceOptPreparePhases() {
+  opt::irpass::InferenceOptPrepareLib irpass;
+  auto grad_var_prepare = opt::OptPassConfig({irpass.grad_var_prepare_});
+  opt::OptPassGroupMap prepare_map({{"inference_opt_prep", grad_var_prepare}});
+  return prepare_map;
+}
+
 OptPassGroupMap GetPreparePhases(const opt::irpass::OptimizeIRPassLib& irpass) {
   opt::OptPassConfig prepare_group = opt::OptPassConfig({irpass.print_tuple_wrapper_});
   OptPassGroupMap map({{"prepare_group", prepare_group}});
@@ -239,6 +246,16 @@ bool ValidatePass(const ResourcePtr& res) {
   return true;
 }
 
+bool InferenceOptPreparePass(const ResourcePtr& res) {
+  FuncGraphPtr func_graph = res->func_graph();
+  MS_EXCEPTION_IF_NULL(func_graph);
+  abstract::AbstractBasePtrList args_spec = res->args_spec();
+  auto prepare_map = GetInferenceOptPreparePhases();
+  auto infer_opt_prepare = opt::Optimizer::MakeOptimizer("inference_prepare", res, prepare_map);
+  (void)infer_opt_prepare->step(func_graph, args_spec, false);
+  return true;
+}
+
 std::vector<PassItem> kVmPasses = {{"simplify_data_structures", SimplifyDataStructuresPass},
                                    {"opt_a", OptPassAGroup},
                                    {"opt_b", OptPassBGroup},

mindspore/ccsrc/pipeline/pass.h

@@ -34,7 +34,7 @@ bool CconvPass(const ResourcePtr& res);
 bool ValidatePass(const ResourcePtr& res);
 bool ConvertPrepareAdapt(const ResourcePtr& res);
 bool AddControlDependPass(const ResourcePtr& res);
-
+bool InferenceOptPreparePass(const ResourcePtr& res);
 void ReclaimOptimizer();
 }  // namespace pipeline
 }  // namespace mindspore

mindspore/ccsrc/pipeline/static_analysis/abstract_function.h

@@ -133,6 +133,7 @@ class FuncGraphAbstractClosure : public AbstractFuncAtom {
   FuncGraphPtr func_graph_;
   AnalysisContextPtr context_;
 };
+using FuncGraphAbstractClosurePtr = std::shared_ptr<FuncGraphAbstractClosure>;
 
 class MetaFuncGraphAbstractClosure : public AbstractFuncAtom {
  public:

mindspore/ccsrc/pipeline/static_analysis/analysis_context.cc

@@ -41,7 +41,7 @@ AnalysisContextPtr AnalysisContext::NewFuncGraphContext(const FuncGraphPtr &func
     } else {
       oss << "nullptr";
     }
-    MS_LOG(EXCEPTION) << "" << oss.str() << " NodeInfo: " << trace::GetDebugInfo(func_graph->debug_info());
+    MS_LOG(EXCEPTION) << oss.str() << " NodeInfo: " << trace::GetDebugInfo(func_graph->debug_info());
   }
   return NewContext(parent_context, func_graph, args_spec_list);
 }

mindspore/ccsrc/pipeline/static_analysis/prim.cc

@@ -180,6 +180,85 @@ AbstractBasePtr DoSignatureEvaluator::Run(AnalysisEnginePtr engine, const Config
   return engine->ForwardConfig(out_conf, fn_conf);
 }
 
+static AbstractBasePtrList GetUnpackGraphSpecArgsList(AbstractBasePtrList args_spec_list, bool need_unpack) {
+  // arg[0] is the func graph to unpack, ignore it
+  AbstractBasePtrList sepcialize_args_before_unpack(args_spec_list.begin() + 1, args_spec_list.end());
+  AbstractBasePtrList graph_sepcialize_args;
+  if (need_unpack) {
+    for (size_t index = 0; index < sepcialize_args_before_unpack.size(); index++) {
+      MS_EXCEPTION_IF_NULL(sepcialize_args_before_unpack[index]);
+      if (sepcialize_args_before_unpack[index]->isa<AbstractTuple>()) {
+        AbstractTuplePtr arg_tuple = sepcialize_args_before_unpack[index]->cast<AbstractTuplePtr>();
+        std::transform(arg_tuple->elements().begin(), arg_tuple->elements().end(),
+                       std::back_inserter(graph_sepcialize_args), [](AbstractBasePtr abs) { return abs; });
+      } else if (sepcialize_args_before_unpack[index]->isa<AbstractDictionary>()) {
+        AbstractDictionaryPtr arg_dict = sepcialize_args_before_unpack[index]->cast<AbstractDictionaryPtr>();
+        auto dict_elems = arg_dict->elements();
+        (void)std::transform(
+          dict_elems.begin(), dict_elems.end(), std::back_inserter(graph_sepcialize_args),
+          [](const AbstractAttribute &item) { return std::make_shared<AbstractKeywordArg>(item.first, item.second); });
+      } else {
+        MS_LOG(EXCEPTION) << "UnpackGraph require args should be tuple or dict, but got "
+                          << sepcialize_args_before_unpack[index]->ToString();
+      }
+    }
+  } else {
+    graph_sepcialize_args = sepcialize_args_before_unpack;
+  }
+  return graph_sepcialize_args;
+}
+
+AbstractBasePtr UnpackGraphEvaluator::Run(AnalysisEnginePtr engine, const ConfigPtrList &args_conf_list,
+                                          AnfNodeConfigPtr out_conf) {
+  if (out_conf->node() == nullptr || !out_conf->node()->isa<CNode>()) {
+    MS_LOG(EXCEPTION) << "Node of out_conf should be CNode";
+  }
+  if (!prim_->isa<prim::UnpackGraphPrimitive>()) {
+    MS_LOG(EXCEPTION) << "Primitive should be UnpackGraphPrimitive, but got " << prim_->ToString();
+  }
+
+  auto unpack_graph = prim_->cast<prim::UnpackGraphPrimitivePtr>();
+  auto out_node = out_conf->node()->cast<CNodePtr>();
+  const auto &out_node_inputs = out_node->inputs();
+  if (out_node->inputs().size() == 0 || (out_node_inputs.size() - 1) != args_conf_list.size()) {
+    MS_LOG(EXCEPTION) << "UnpackGraphPrimitive"
+                      << " args size should equal to inputs size minus 1, but args size " << args_conf_list.size()
+                      << ", inputs size " << out_node_inputs.size();
+  }
+  AnfNodePtrList args_inputs{out_node_inputs.begin() + 1, out_node_inputs.end()};
+  AbstractBasePtrList args_spec_list;
+  (void)std::transform(args_conf_list.begin(), args_conf_list.end(), std::back_inserter(args_spec_list),
+                       [](const ConfigPtr &ref) -> AbstractBasePtr { return ref->GetEvaluatedValue(); });
+  // get the forward graph
+  MS_EXCEPTION_IF_NULL(args_spec_list[0]);
+  AbstractFunctionPtr fn = args_spec_list[0]->cast<AbstractFunctionPtr>();
+  if (fn == nullptr) {
+    MS_LOG(EXCEPTION) << "UnpackGraphPrimitive arg0 must be AbstractFunction, but " << args_spec_list[0]->ToString();
+  }
+  auto real_fn = fn->cast<FuncGraphAbstractClosurePtr>();
+  MS_EXCEPTION_IF_NULL(real_fn);
+  FuncGraphPtr forward_graph = real_fn->func_graph();
+  MS_EXCEPTION_IF_NULL(forward_graph);
+  AbstractBasePtrList graph_sepcialize_args =
+    GetUnpackGraphSpecArgsList(args_spec_list, unpack_graph->need_unpack_args());
+
+  AbstractBasePtrList graph_sepcialize_args_without_sens;
+  (void)std::transform(graph_sepcialize_args.begin(),
+                       graph_sepcialize_args.end() - (unpack_graph->with_sens_in_args() ? 1 : 0),
+                       std::back_inserter(graph_sepcialize_args_without_sens), [](AbstractBasePtr abs) { return abs; });
+  auto new_graph = forward_graph->GenerateGraph(graph_sepcialize_args_without_sens);
+  engine->func_graph_manager()->AddFuncGraph(new_graph);
+  ScopePtr scope = kDefaultScope;
+  if (out_conf != nullptr) {
+    scope = out_conf->node()->scope();
+  }
+  ScopeGuard scope_guard(scope);
+  AnfNodePtr new_vnode = NewValueNode(new_graph);
+  AnfNodeConfigPtr fn_conf = engine->MakeConfig(new_vnode, out_conf->context());
+
+  return engine->ForwardConfig(out_conf, fn_conf);
+}
+
 namespace {
 py::object BuildValue(const ValuePtr &value_ptr) {
   if (value_ptr == nullptr) {

mindspore/ccsrc/pipeline/static_analysis/prim.h

@@ -87,6 +87,21 @@ class DoSignatureEvaluator : public Evaluator {
   PrimitivePtr prim_;
 };
 
+class UnpackGraphEvaluator : public Evaluator {
+ public:
+  explicit UnpackGraphEvaluator(const PrimitivePtr primitive) : Evaluator("UnpackGraphEvaluator"), prim_(primitive) {}
+  ~UnpackGraphEvaluator() override = default;
+  AbstractBasePtr Run(AnalysisEnginePtr engine, const ConfigPtrList &argrefs,
+                      AnfNodeConfigPtr out_config = nullptr) override;
+
+  AbstractBasePtr Infer(AnalysisEnginePtr, const AbstractBasePtrList &) override {
+    MS_LOG(EXCEPTION) << "Infer() should not be called, Run() method should be called";
+  }
+
+ private:
+  PrimitivePtr prim_;
+};
+
 bool IsInWhiteList(PrimitivePtr primitive);
 StandardPrimitiveEvalImpl GetPrimitiveInferImpl(const PrimitivePtr &primitive);
 

mindspore/ccsrc/pipeline/static_analysis/static_analysis.cc

@@ -289,6 +289,10 @@ EvaluatorPtr GetPrimEvaluator(const PrimitivePtr &prim, const AnalysisEnginePtr
     evaluator = std::make_shared<DoSignatureEvaluator>(prim);
     return evaluator;
   }
+  if (prim->isa<prim::UnpackGraphPrimitive>()) {
+    evaluator = std::make_shared<UnpackGraphEvaluator>(prim);
+    return evaluator;
+  }
   if (prim->HasPyEvaluator()) {
     auto prim_py = dyn_cast<PrimitivePy>(prim);
     if (prim_py != nullptr) {

tests/ut/python/parameter_feature/test_var_grad.py

@@ -19,6 +19,8 @@ from mindspore.nn import Cell
 from mindspore.ops import operations as P
 import mindspore.ops.composite as C
 from mindspore.common.api import _executor
+from mindspore.common.parameter import ParameterTuple
+from mindspore.common import dtype as mstype
 
 context.set_context(mode=context.GRAPH_MODE)
 
@@ -34,3 +36,152 @@ def test_net_vargs_expand():
     sens = Tensor(np.random.normal(0, 1, [3, 4, 5]).astype(np.float32))
     net = AddNet()
     out = C.grad_all_with_sens(net, net.trainable_params())(x, y, sens)
+
+class VarNet(Cell):
+    def __init__(self, net):
+        super(VarNet, self).__init__()
+        self.b = Parameter(Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), "b", requires_grad=True)
+        self.w = Parameter(Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), "w", requires_grad=True)
+        self.net = net
+    def construct(self, *args):
+        return self.net(*args)*self.w + self.b
+    
+class SecondNet(Cell):
+    def __init__(self):
+        super(SecondNet, self).__init__()
+        self.b2 = Parameter(Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), "b2", requires_grad=True)
+    def construct(self, *args):
+        res = args[0] + args[1]
+        return res + self.b2
+def test_all_var_args_grad_with_sens():
+    """"test grad_by_list_with_sens with all var args input"""
+    class GradNet(Cell):
+        def __init__(self, net):
+            super(GradNet, self).__init__()
+            self.weights = ParameterTuple(net.trainable_params())
+            self.net = net
+        def construct(self, *inputs):
+            return C.grad_by_list_with_sens(self.net, self.weights)(*inputs)
+    x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
+    y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
+    sens = Tensor(1.0, dtype=mstype.float32)
+    net = VarNet(SecondNet())
+    grad_net = GradNet(net)
+    out = grad_net(x, y, sens)
+
+def test_grad_list_var_args():
+    class GradNet(Cell):
+        def __init__(self, net):
+            super(GradNet, self).__init__()
+            self.weights = ParameterTuple(net.trainable_params())
+            self.net = net
+        def construct(self, *inputs):
+            return C.grad_by_list(self.net, self.weights)(*inputs)
+    x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
+    y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
+    net = VarNet(SecondNet())
+    grad_net = GradNet(net)
+    out = grad_net(x, y)
+
+def test_grad_all_var_args():
+    class GradNet(Cell):
+        def __init__(self, net):
+            super(GradNet, self).__init__()
+            self.weights = ParameterTuple(net.trainable_params())
+            self.net = net
+        def construct(self, *inputs):
+            return C.grad_all(self.net)(*inputs)
+    x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
+    y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
+    net = VarNet(SecondNet())
+    grad_net = GradNet(net)
+    out = grad_net(x, y)
+
+def test_grad_all_var_args_with_sens():
+    class GradNet(Cell):
+        def __init__(self, net):
+            super(GradNet, self).__init__()
+            self.weights = ParameterTuple(net.trainable_params())
+            self.net = net
+        def construct(self, *inputs):
+            return C.grad_all_with_sens(self.net)(*inputs)
+    x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
+    y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
+    sens = Tensor(1.0, dtype=mstype.float32)
+    net = VarNet(SecondNet())
+    grad_net = GradNet(net)
+    out = grad_net(x, y, sens)
+
+def test_grad_var_args_with_sens():
+    class GradNet(Cell):
+        def __init__(self, net):
+            super(GradNet, self).__init__()
+            self.weights = ParameterTuple(net.trainable_params())
+            self.net = net
+        def construct(self, *inputs):
+            return C.grad_with_sens(self.net)(*inputs)
+    x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
+    y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
+    sens = Tensor(1.0, dtype=mstype.float32)
+    net = VarNet(SecondNet())
+    grad_net = GradNet(net)
+    out = grad_net(x, y, sens)
+
+def test_var_args_grad():
+    class VarNet(Cell):
+        def __init__(self, net):
+            super(VarNet, self).__init__()
+            self.b = Parameter(Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), "b", requires_grad=True)
+            self.net = net
+        def construct(self, *args):
+            return self.net(*args) + self.b
+        
+    class SecondNet(Cell):
+        def __init__(self):
+            super(SecondNet, self).__init__()
+            self.b2 = Parameter(Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), "b2", requires_grad=True)
+        def construct(self, *args):
+            res = args[0] + args[1]
+            return res + self.b2
+    class GradNet(Cell):
+        def __init__(self, net):
+            super(GradNet, self).__init__()
+            self.net = net
+            self.weights = ParameterTuple(net.trainable_params())
+        def construct(self, x, y, sens):
+            return C.grad_by_list_with_sens(self.net, self.weights)(x, y, sens)
+    x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
+    y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
+    sens = Tensor(1.0, dtype=mstype.float32)
+    net = VarNet(SecondNet())
+    grad_net = GradNet(net)
+    out = grad_net(x, y, sens)
+
+
+def test_var_args_positional():
+    """"test grad_all with var args in inner graph"""
+    class VarNet(Cell):
+        def __init__(self, net):
+            super(VarNet, self).__init__()
+            self.net = net
+        def construct(self, x, y):
+            return self.net(x, y)*x
+
+    class SecondNet(Cell):
+        def __init__(self):
+            super(SecondNet, self).__init__()
+        def construct(self, *args):
+            return args[0] + args[1]
+
+    class GradNet(Cell):
+        def __init__(self, net):
+            super(GradNet, self).__init__()
+            self.net = net
+            self.weights = ParameterTuple(net.trainable_params())
+        def construct(self, x, y):
+            return C.grad_all(self.net)(x, y)
+    x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
+    y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
+    net = VarNet(SecondNet())
+    grad_net = GradNet(net)
+    out = grad_net(x, y)