Add IndexedSlices

mindspore/_extends/parse/resources.py

@@ -17,6 +17,7 @@
 """Resources for ast tree parse."""
 import ast
 import math
+from mindspore import IndexedSlices
 from mindspore.ops.composite import multitype_ops
 from mindspore.ops import functional as F, composite as C
 from . import standard_method as M
@@ -135,4 +136,7 @@ convert_object_map = {
     math.sin:       NO_IMPLEMENT,
     math.cos:       NO_IMPLEMENT,
     math.tan:       NO_IMPLEMENT,
+
+    # user defined
+    IndexedSlices:  F.make_indexed_slices,
 }

mindspore/ccsrc/debug/dump_proto.cc

@@ -120,6 +120,10 @@ void ProtoExporter::SetNodeOutputType(const TypePtr &type, const BaseShapePtr &s
         type_proto->mutable_tensor_type()->mutable_shape()->add_dim()->set_size(elem);
       }
     }
+  } else if (type->isa<IndexedSlicesType>()) {
+    // Do Nothing
+  } else if (type->isa<UndeterminedType>()) {
+    // Do Nothing
   } else if (type->isa<Tuple>()) {
     TuplePtr tuple_type = dyn_cast<Tuple>(type);
     type_proto->set_data_type(irpb::DT_TUPLE);

mindspore/ccsrc/ir/dtype.cc

@@ -94,6 +94,48 @@ bool Slice::operator==(const Type &other) const {
 
 std::string Slice::DumpText() const { return ToString(); }
 
+TypePtr UndeterminedType::DeepCopy() const {
+  MS_EXCEPTION_IF_NULL(element_type_);
+  if (IsGeneric()) {
+    return std::make_shared<UndeterminedType>();
+  }
+  return std::make_shared<UndeterminedType>(element_type_->DeepCopy());
+}
+
+std::string UndeterminedType::ToReprString() const {
+  if (element_type_ == nullptr) {
+    return "Undetermined";
+  }
+  return "Undetermined[" + element_type_->ToReprString() + "]";
+}
+
+std::string UndeterminedType::ToString() const {
+  if (element_type_ == nullptr) {
+    return "Undetermined";
+  }
+  return "Undetermined[" + element_type_->ToString() + "]";
+}
+
+std::string UndeterminedType::DumpText() const {
+  if (element_type_ == nullptr) {
+    return "Undetermined";
+  }
+  return "Undetermined[" + element_type_->DumpText() + "]";
+}
+
+bool UndeterminedType::operator==(const Type &other) const {
+  if (!IsSameObjectType(*this, other)) {
+    return false;
+  }
+  auto other_elem_type = static_cast<const UndeterminedType &>(other).element_type_;
+  if (element_type_ == nullptr && other_elem_type == nullptr) {
+    return true;
+  } else if (element_type_ == nullptr || other_elem_type == nullptr) {
+    return false;
+  }
+  return *element_type_ == *other_elem_type;
+}
+
 TypePtr TensorType::DeepCopy() const {
   MS_EXCEPTION_IF_NULL(element_type_);
   if (IsGeneric()) {
@@ -137,6 +179,48 @@ bool TensorType::operator==(const Type &other) const {
   return *element_type_ == *other_elem_type;
 }
 
+TypePtr IndexedSlicesType::DeepCopy() const {
+  MS_EXCEPTION_IF_NULL(element_type_);
+  if (IsGeneric()) {
+    return std::make_shared<IndexedSlicesType>();
+  }
+  return std::make_shared<IndexedSlicesType>(element_type_->DeepCopy());
+}
+
+std::string IndexedSlicesType::ToReprString() const {
+  if (element_type_ == nullptr) {
+    return "IndexedSlices";
+  }
+  return "IndexedSlices[" + element_type_->ToReprString() + "]";
+}
+
+std::string IndexedSlicesType::ToString() const {
+  if (element_type_ == nullptr) {
+    return "IndexedSlices";
+  }
+  return "IndexedSlices[" + element_type_->ToString() + "]";
+}
+
+std::string IndexedSlicesType::DumpText() const {
+  if (element_type_ == nullptr) {
+    return "IndexedSlices";
+  }
+  return "IndexedSlices[" + element_type_->DumpText() + "]";
+}
+
+bool IndexedSlicesType::operator==(const Type &other) const {
+  if (!IsSameObjectType(*this, other)) {
+    return false;
+  }
+  auto other_elem_type = static_cast<const IndexedSlicesType &>(other).element_type_;
+  if (element_type_ == nullptr && other_elem_type == nullptr) {
+    return true;
+  } else if (element_type_ == nullptr || other_elem_type == nullptr) {
+    return false;
+  }
+  return *element_type_ == *other_elem_type;
+}
+
 Function::Function() : Object(kObjectTypeFunction) {
   args_ = std::vector<TypePtr>();
   retval_ = nullptr;

mindspore/ccsrc/ir/dtype.h

@@ -108,10 +108,34 @@ class Slice : public Object {
 };
 using SlicePtr = std::shared_ptr<Slice>;
 
+class UndeterminedType : public Object {
+ public:
+  UndeterminedType() : Object(kObjectTypeUndeterminedType) {}
+  explicit UndeterminedType(const TypePtr &ele)
+      : Object(kObjectTypeUndeterminedType, kMetaTypeObject, false), element_type_(ele) {}
+  ~UndeterminedType() override = default;
+  MS_DECLARE_PARENT(UndeterminedType, Object)
+
+  TypeId generic_type_id() const override { return kObjectTypeUndeterminedType; }
+  const TypePtr element() const { return element_type_; }
+  void set_element(const TypePtr &element_type) { element_type_ = element_type; }
+
+  TypePtr DeepCopy() const override;
+  std::string ToString() const override;
+  std::string ToReprString() const override;
+  std::string DumpText() const override;
+  bool operator==(const Type &other) const override;
+
+ protected:
+  TypePtr element_type_;
+};
+using MetaTensorTypePtr = std::shared_ptr<UndeterminedType>;
+
 class TensorType : public Object {
  public:
-  TensorType() : Object(kObjectTypeTensorType) {}
+  TensorType() : Object(kObjectTypeTensorType, kObjectTypeUndeterminedType) {}
-  explicit TensorType(const TypePtr &ele) : Object(kObjectTypeTensorType, false), element_type_(ele) {}
+  explicit TensorType(const TypePtr &ele)
+      : Object(kObjectTypeTensorType, kObjectTypeUndeterminedType, false), element_type_(ele) {}
   ~TensorType() override = default;
   MS_DECLARE_PARENT(TensorType, Object)
 
@@ -130,6 +154,29 @@ class TensorType : public Object {
 };
 using TensorTypePtr = std::shared_ptr<TensorType>;
 
+class IndexedSlicesType : public Object {
+ public:
+  IndexedSlicesType() : Object(kObjectTypeIndexedSlicesType, kObjectTypeUndeterminedType) {}
+  explicit IndexedSlicesType(const TypePtr &ele)
+      : Object(kObjectTypeIndexedSlicesType, kObjectTypeUndeterminedType, false), element_type_(ele) {}
+  ~IndexedSlicesType() override = default;
+  MS_DECLARE_PARENT(IndexedSlicesType, Object)
+
+  TypeId generic_type_id() const override { return kObjectTypeIndexedSlicesType; }
+  const TypePtr element() const { return element_type_; }
+  void set_element(const TypePtr &element_type) { element_type_ = element_type; }
+
+  TypePtr DeepCopy() const override;
+  std::string ToString() const override;
+  std::string ToReprString() const override;
+  std::string DumpText() const override;
+  bool operator==(const Type &other) const override;
+
+ private:
+  TypePtr element_type_;
+};
+using IndexedSlicesTypePtr = std::shared_ptr<IndexedSlicesType>;
+
 class Function : public Object {
  public:
   Function();
@@ -255,6 +302,8 @@ TypePtr StringToType(const std::string &type_name);
 // Judge whether x is predicate or is a subclass of predicate.
 bool IsIdentidityOrSubclass(TypePtr const &x, TypePtr const &base_type);
 
+bool IsParentOrChildrenType(TypePtr const &x, TypePtr const &base_type);
+
 // Whether t1 is identity or a subclass of t2.
 bool IsSubType(TypePtr const &t1, TypePtr const &t2 = nullptr);
 

mindspore/ccsrc/ir/dtype/type.cc

@@ -115,6 +115,10 @@ const char *ObjectIdLabel(const TypeId &v) {
       return "kObjectTypeKeyword";
     case kObjectTypeTensorType:
       return "kObjectTypeTensorType";
+    case kObjectTypeIndexedSlicesType:
+      return "kObjectTypeIndexedSlicesType";
+    case kObjectTypeUndeterminedType:
+      return "kObjectTypeUndeterminedType";
     case kObjectTypeDictionary:
       return "kObjectTypeDictionary";
     case kObjectTypeClass:

mindspore/ccsrc/ir/dtype/type.h

@@ -67,6 +67,7 @@ class Type : public Value {
   virtual bool equal(const TypePtr other) const { return *this == *other; }
 
   virtual TypeId object_type() const { return kTypeUnknown; }
+  virtual TypeId parent_type() const { return kTypeUnknown; }
   virtual TypeId number_type() const { return kTypeUnknown; }
   virtual TypePtr DeepCopy() const = 0;
   virtual TypePtr Clone() const { return DeepCopy(); }
@@ -97,13 +98,16 @@ using TypePtrList = std::vector<TypePtr>;
 //
 class Object : public Type {
  public:
-  Object() : Type(kMetaTypeObject), object_type_(kMetaTypeObject) {}
+  Object() : Type(kMetaTypeObject), object_type_(kMetaTypeObject), parent_type_(kMetaTypeObject) {}
   explicit Object(const TypeId object_type, bool is_generic = true)
-      : Type(kMetaTypeObject, is_generic), object_type_(object_type) {}
+      : Type(kMetaTypeObject, is_generic), object_type_(object_type), parent_type_(kMetaTypeObject) {}
+  explicit Object(const TypeId object_type, const TypeId parent_type, bool is_generic = true)
+      : Type(kMetaTypeObject, is_generic), object_type_(object_type), parent_type_(parent_type) {}
   ~Object() override = default;
   MS_DECLARE_PARENT(Object, Type)
 
   TypeId object_type() const override { return object_type_; }
+  TypeId parent_type() const override { return parent_type_; }
   TypeId type_id() const override { return object_type_; }
   TypeId generic_type_id() const override { return kMetaTypeObject; }
   bool equal(const TypePtr other) const override;
@@ -114,6 +118,7 @@ class Object : public Type {
 
  private:
   const TypeId object_type_;
+  const TypeId parent_type_;
 };
 
 std::ostream &operator<<(std::ostream &os, const TypePtrList &types);

mindspore/ccsrc/ir/dtype/type_id.h

@@ -50,6 +50,8 @@ enum TypeId : int {
   kObjectTypeSlice,
   kObjectTypeKeyword,
   kObjectTypeTensorType,
+  kObjectTypeIndexedSlicesType,
+  kObjectTypeUndeterminedType,
   kObjectTypeClass,
   kObjectTypeDictionary,
   kObjectTypeFunction,

mindspore/ccsrc/ir/dtype_extends.cc

@@ -192,6 +192,40 @@ TypePtr TensorStrToType(const std::string &type_name) {
   return type;
 }
 
+TypePtr IndexedSlicesStrToType(const std::string &type_name) {
+  if (type_name == "IndexedSlices") {
+    return std::make_shared<IndexedSlicesType>();
+  }
+  auto start = type_name.find_first_of('[') + 1;
+  auto end = type_name.find_last_of(']');
+  if (start >= type_name.size()) {
+    return nullptr;
+  }
+  auto element_str = type_name.substr(start, end - start);
+  auto element_type = StringToType(element_str);
+  if (element_type == nullptr) {
+    return nullptr;
+  }
+  return std::make_shared<IndexedSlicesType>(element_type);
+}
+
+TypePtr UndeterminedStrToType(const std::string &type_name) {
+  if (type_name == "Undetermined") {
+    return std::make_shared<UndeterminedType>();
+  }
+  auto start = type_name.find_first_of('[') + 1;
+  auto end = type_name.find_last_of(']');
+  if (start >= type_name.size()) {
+    return nullptr;
+  }
+  auto element_str = type_name.substr(start, end - start);
+  auto element_type = StringToType(element_str);
+  if (element_type == nullptr) {
+    return nullptr;
+  }
+  return std::make_shared<UndeterminedType>(element_type);
+}
+
 TypePtr ListStrToType(const std::string &type_name) {
   TypePtr type = nullptr;
   if (type_name == "List") {
@@ -313,6 +347,10 @@ TypePtr StringToType(const std::string &type_name) {
     type = StringToNumberType<Float>(type_name, "Float");
   } else if (type_name.compare(0, strlen("Tensor"), "Tensor") == 0) {
     type = TensorStrToType(type_name);
+  } else if (type_name.compare(0, strlen("Undetermined"), "Undetermined") == 0) {
+    type = UndeterminedStrToType(type_name);
+  } else if (type_name.compare(0, strlen("IndexedSlices"), "IndexedSlices") == 0) {
+    type = IndexedSlicesStrToType(type_name);
   } else if (type_name.compare(0, strlen("List"), "List") == 0) {
     type = ListStrToType(type_name);
   } else if (type_name.compare(0, strlen("Tuple"), "Tuple") == 0) {
@@ -340,6 +378,20 @@ TypePtr StringToType(const std::string &type_name) {
   return type;
 }
 
+bool IsParentOrChildrenType(TypePtr const &x, TypePtr const &base_type) {
+  if (x == nullptr || base_type == nullptr) {
+    MS_LOG(ERROR) << "Type is nullptr.";
+    return false;
+  }
+  if (base_type->type_id() == kTypeUnknown || x->type_id() == kTypeUnknown) {
+    return false;
+  }
+  if (base_type->type_id() == x->parent_type() || x->type_id() == base_type->parent_type()) {
+    return true;
+  }
+  return false;
+}
+
 bool IsIdentidityOrSubclass(TypePtr const &x, TypePtr const &base_type) {
   if (x == nullptr || base_type == nullptr) {
     MS_LOG(ERROR) << "Type is nullptr.";
@@ -481,6 +533,10 @@ REGISTER_PYBIND_DEFINE(
           TensorType data(TypeIdToType(TypeId(static_cast<int>(t[0].cast<py::int_>()))));
           return data;
         }));
+    (void)py::class_<IndexedSlicesType, Type, std::shared_ptr<IndexedSlicesType>>(m_sub, "IndexedSlicesType")
+      .def(py::init());
+    (void)py::class_<UndeterminedType, Type, std::shared_ptr<UndeterminedType>>(m_sub, "UndeterminedType")
+      .def(py::init());
     (void)py::class_<Function, Type, std::shared_ptr<Function>>(m_sub, "Function")
       .def(py::init())
       .def(py::init<std::vector<TypePtr>, TypePtr>(), py::arg("args"), py::arg("retval"));
@@ -501,6 +557,8 @@ const TypePtr kTypeExternal = std::make_shared<External>();
 const TypePtr kTypeEnv = std::make_shared<EnvType>();
 const TypePtr kTypeType = std::make_shared<TypeType>();
 const TypePtr kTensorType = std::make_shared<TensorType>();
+const TypePtr kIndexedSlicesType = std::make_shared<IndexedSlicesType>();
+const TypePtr kUndeterminedType = std::make_shared<UndeterminedType>();
 const TypePtr kString = std::make_shared<String>();
 const TypePtr kList = std::make_shared<List>();
 const TypePtr kTuple = std::make_shared<Tuple>();

mindspore/ccsrc/operator/composite/multitype_funcgraph.cc

@@ -93,15 +93,17 @@ static TypePtr UnwrapRef(const TypePtr &type) {
   }
   return type;
 }
-FuncGraphPtr MultitypeFuncGraph::GenerateFromTypes(const TypePtrList &types) {
+
-  bool find_fn = false;
+// Return Exact match if exists,  else return non ambiguous sub class match
-  py::function py_fn;
+// Return py::none() if matching is ambiguous
+const py::function MultitypeFuncGraph::SignMatch(const TypePtrList &types) {
+  // Exact match
   for (auto &item : fn_cache_py_) {
     TypePtrList sign = item.first;
     if (sign.size() != types.size()) {
       continue;
     }
-    bool match = true;
+    auto match = true;
     for (size_t i = 0; i < sign.size(); ++i) {
       if (!IsIdentidityOrSubclass(UnwrapRef(types[i]), sign[i])) {
         match = false;
@@ -111,13 +113,45 @@ FuncGraphPtr MultitypeFuncGraph::GenerateFromTypes(const TypePtrList &types) {
     if (!match) {
       continue;
     }
-    find_fn = true;
+    return item.second;
-    py_fn = item.second;
-    break;
   }
+  // Try best match
+  py::function py_fn_subclass;
+  size_t subclass_match_cnt = 0;
+  for (auto &item : fn_cache_py_) {
+    TypePtrList sign = item.first;
+    if (sign.size() != types.size()) {
+      continue;
+    }
+    auto match = true;
+    for (size_t i = 0; i < sign.size(); ++i) {
+      if (!IsIdentidityOrSubclass(UnwrapRef(types[i]), sign[i]) &&
+          !IsParentOrChildrenType(UnwrapRef(types[i]), sign[i])) {
+        match = false;
+        break;
+      }
+    }
+    if (!match) {
+      continue;
+    }
+    py_fn_subclass = item.second;
+    subclass_match_cnt++;
+  }
+  if (subclass_match_cnt > 1) {
+    MS_LOG(EXCEPTION) << "There are more than one prototypes for overload function match by subclass";
+  }
+  if (subclass_match_cnt == 1) {
+    MS_LOG(DEBUG) << "Found one subclass match";
+    return py_fn_subclass;
+  }
+  return py::none();
+}
+
+FuncGraphPtr MultitypeFuncGraph::GenerateFromTypes(const TypePtrList &types) {
+  auto py_fn = SignMatch(types);
   std::ostringstream buffer;
   buffer << types;
-  if (find_fn) {
+  if (py_fn != py::none()) {
     FuncGraphPtr func_graph = parse::ParsePythonCode(py_fn);
     if (func_graph == nullptr) {
       MS_LOG(EXCEPTION) << "Fail to parse overload function " << buffer.str();

mindspore/ccsrc/operator/composite/multitype_funcgraph.h

@@ -54,6 +54,7 @@ class MultitypeFuncGraph : public MetaFuncGraph {
   }
 
  private:
+  const py::function SignMatch(const TypePtrList &types);
   std::unordered_map<TypePtrList, specialize_fn, TypeListHasher, TypeListEqual> fn_cache_;
   std::unordered_map<TypePtrList, py::function, TypeListHasher, TypeListEqual> fn_cache_py_;
 };

mindspore/ccsrc/operator/ops.cc

@@ -277,5 +277,12 @@ const PrimitivePtr kPrimImageSummary = std::make_shared<Primitive>("ImageSummary
 const PrimitivePtr kPrimTensorSummary = std::make_shared<Primitive>("TensorSummary");
 const PrimitivePtr kPrimHistogramSummary = std::make_shared<Primitive>("HistogramSummary");
 const PrimitivePtr kPrimDebug = std::make_shared<Primitive>("Debug");
+
+// IndexedSlices
+const PrimitivePtr kPrimMakeIndexedSlices = std::make_shared<Primitive>("MakeIndexedSlices");
+const PrimitivePtr kPrimIndexedSlicesGetValues = std::make_shared<Primitive>("IndexedSlicesGetValues");
+const PrimitivePtr kPrimIndexedSlicesGetIndices = std::make_shared<Primitive>("IndexedSlicesGetIndices");
+const PrimitivePtr kPrimIndexedSlicesGetDenseShape = std::make_shared<Primitive>("IndexedSlicesGetDenseShape");
+const PrimitivePtr kPrimIsIndexedSlices = std::make_shared<Primitive>("IsIndexedSlices");
 }  // namespace prim
 }  // namespace mindspore

mindspore/ccsrc/operator/ops.h

@@ -287,6 +287,13 @@ extern const PrimitivePtr kPrimMirror;
 extern const PrimitivePtr kPrimVirtualDiv;
 extern const PrimitivePtr kPrimVirtualDataset;
 
+// IndexedSlices
+extern const PrimitivePtr kPrimMakeIndexedSlices;
+extern const PrimitivePtr kPrimIndexedSlicesGetValues;
+extern const PrimitivePtr kPrimIndexedSlicesGetIndices;
+extern const PrimitivePtr kPrimIndexedSlicesGetDenseShape;
+extern const PrimitivePtr kPrimIsIndexedSlices;
+
 class DoSignaturePrimitive : public Primitive {
  public:
   explicit DoSignaturePrimitive(const std::string &name, const ValuePtr &function)

mindspore/ccsrc/operator/prim_others.cc

@@ -24,6 +24,7 @@
 #include "pipeline/static_analysis/prim.h"
 #include "pipeline/static_analysis/utils.h"
 #include "utils/symbolic.h"
+#include "utils/context/ms_context.h"
 
 namespace mindspore {
 namespace abstract {
@@ -173,6 +174,13 @@ AbstractBasePtr InferImplEnvGetItem(const AnalysisEnginePtr &, const PrimitivePt
     return std::make_shared<AbstractTuple>(sparse_list);
   }
 
+  auto context = MsContext::GetInstance();
+  MS_EXCEPTION_IF_NULL(context);
+  bool enable_sparse_flag = context->enable_sparse_flag();
+  if (enable_sparse_flag && key->has_indexed_slices_grad() && dflt->isa<AbstractTensor>()) {
+    auto dflt_tensor = dflt->cast<AbstractTensorPtr>();
+    return std::make_shared<AbstractUndetermined>(dflt_tensor->element()->Clone(), dflt_tensor->shape()->Clone());
+  }
   if (!key->GetValueTrack()->isa<SymbolicKeyInstance>()) {
     return dflt;
   }
@@ -236,6 +244,7 @@ AbstractBasePtr InferImplMakeRef(const AnalysisEnginePtr &, const PrimitivePtr &
   }
   auto ret = std::make_shared<AbstractRef>(args_spec_list[0], args_spec_list[1], args_spec_list[2]);
   ret->set_sparse_grad(args_spec_list[2]->sparse_grad());
+  ret->set_has_indexed_slices_grad(args_spec_list[2]->has_indexed_slices_grad());
   return ret;
 }
 
@@ -437,5 +446,72 @@ AbstractBasePtr InferImplControlDepend(const AnalysisEnginePtr &, const Primitiv
   }
   return std::make_shared<AbstractScalar>(kAnyValue, kBool);
 }
+
+AbstractBasePtr InferImplMakeIndexedSlices(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
+                                           const AbstractBasePtrList &args_spec_list) {
+  // Inputs: two tensors and a tuple.
+  const std::string op_name = primitive->name();
+  CheckArgsSize(op_name, args_spec_list, 3);
+  auto indices = CheckArg<AbstractTensor>(op_name, args_spec_list, 0);
+  auto values = CheckArg<AbstractTensor>(op_name, args_spec_list, 1);
+  auto dense_shape = CheckArg<AbstractTuple>(op_name, args_spec_list, 2);
+
+  auto dense_shape_value = dense_shape->BuildValue()->cast<ValueTuplePtr>();
+  MS_EXCEPTION_IF_NULL(dense_shape_value);
+  auto shp = dense_shape_value->value();
+  std::vector<int> dense_shape_vec;
+  (void)std::transform(std::begin(shp), std::end(shp), std::back_inserter(dense_shape_vec),
+                       [](const ValuePtr &e) -> int {
+                         auto elem = GetValue<int>(e);
+                         return elem;
+                       });
+  auto ret = std::make_shared<AbstractIndexedSlices>(values->element()->BuildType(), dense_shape_vec);
+  ret->set_indices(indices);
+  ret->set_values(values);
+  ret->set_dense_shape(dense_shape);
+  return ret;
+}
+
+AbstractBasePtr InferImplIndexedSlicesGetValues(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
+                                                const AbstractBasePtrList &args_spec_list) {
+  // Inputs: two tensors and a tuple.
+  const std::string op_name = primitive->name();
+  CheckArgsSize(op_name, args_spec_list, 1);
+  auto indexed_slices = CheckArg<AbstractIndexedSlices>(op_name, args_spec_list, 0);
+  MS_EXCEPTION_IF_NULL(indexed_slices->values());
+  return indexed_slices->values();
+}
+
+AbstractBasePtr InferImplIndexedSlicesGetIndices(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
+                                                 const AbstractBasePtrList &args_spec_list) {
+  // Inputs: two tensors and a tuple.
+  const std::string op_name = primitive->name();
+  CheckArgsSize(op_name, args_spec_list, 1);
+  auto indexed_slices = CheckArg<AbstractIndexedSlices>(op_name, args_spec_list, 0);
+  MS_EXCEPTION_IF_NULL(indexed_slices->indices());
+  return indexed_slices->indices();
+}
+
+AbstractBasePtr InferImplIndexedSlicesGetDenseShape(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
+                                                    const AbstractBasePtrList &args_spec_list) {
+  // Inputs: two tensors and a tuple.
+  const std::string op_name = primitive->name();
+  CheckArgsSize(op_name, args_spec_list, 1);
+  auto indexed_slices = CheckArg<AbstractIndexedSlices>(op_name, args_spec_list, 0);
+  MS_EXCEPTION_IF_NULL(indexed_slices->dense_shape());
+  return indexed_slices->dense_shape();
+}
+
+AbstractBasePtr InferImplIsIndexedSlices(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
+                                         const AbstractBasePtrList &args_spec_list) {
+  const std::string op_name = primitive->name();
+  CheckArgsSize(op_name, args_spec_list, 1);
+  bool ret = false;
+  if (args_spec_list[0]->isa<AbstractIndexedSlices>()) {
+    ret = true;
+  }
+  MS_LOG(DEBUG) << "IsIndexedSlices result: " << ret << ", input: " << args_spec_list[0]->ToString();
+  return std::make_shared<AbstractScalar>(ret);
+}
 }  // namespace abstract
 }  // namespace mindspore

mindspore/ccsrc/optimizer/clean.cc

@@ -36,6 +36,7 @@ using mindspore::abstract::AbstractJTagged;
 using mindspore::abstract::AbstractList;
 using mindspore::abstract::AbstractScalar;
 using mindspore::abstract::AbstractTuple;
+using mindspore::abstract::AbstractUndetermined;
 
 static AbstractBasePtr Reabs(const AbstractBasePtr &t) {
   if (t == nullptr) {
@@ -78,7 +79,7 @@ AnfNodePtr ConvertGetAttrToTupleGetItem(const CNodePtr &node) {
   MS_EXCEPTION_IF_NULL(cons);
 
   auto dt = data->abstract();
-  if (dt == nullptr) {
+  if (dt == nullptr || dt->BuildType()->type_id() == kObjectTypeUndeterminedType) {
     return nullptr;
   }
 

mindspore/ccsrc/optimizer/irpass.cc

@@ -42,6 +42,7 @@
 #include "optimizer/irpass/tile_eliminate.h"
 #include "optimizer/irpass/transpose_eliminate.h"
 #include "optimizer/opt.h"
+#include "optimizer/irpass/indexed_slices_eliminate.h"
 
 namespace mindspore {
 namespace opt {
@@ -153,6 +154,11 @@ OptimizeIRPassLib::OptimizeIRPassLib() {
   // Mark interface fusion
   mark_interface_fusion_ =
     MakeSubstitution(std::make_shared<MarkInterfaceFusion>(), "mark_interface_fusion", prim::kPrimSelect);
+
+  // IndexedSlices Eliminate
+  indexed_slices_eliminate_ = MakeSubstitution(
+    std::make_shared<IndexedSlicesEliminater>(), "indexed_slices_eliminate",
+    {prim::kPrimIndexedSlicesGetIndices, prim::kPrimIndexedSlicesGetValues, prim::kPrimIndexedSlicesGetDenseShape});
 }
 
 ResolveIRPassLib::ResolveIRPassLib() {

mindspore/ccsrc/optimizer/irpass.h

@@ -104,6 +104,9 @@ class OptimizeIRPassLib {
 
   // Fusion
   SubstitutionPtr mark_interface_fusion_;
+
+  // IndexedSlices Eliminate
+  SubstitutionPtr indexed_slices_eliminate_;
 };
 
 // the collection of irpass for resolve action

mindspore/ccsrc/optimizer/irpass/indexed_slices_eliminate.h

@@ -0,0 +1,75 @@
+/**
+ * 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_INDEXED_SLICES_ELIMINATE_H_
+#define MINDSPORE_CCSRC_OPTIMIZER_IRPASS_INDEXED_SLICES_ELIMINATE_H_
+
+#include <vector>
+#include <algorithm>
+
+#include "optimizer/irpass.h"
+#include "optimizer/optimizer.h"
+#include "ir/visitor.h"
+#include "operator/ops.h"
+
+namespace mindspore {
+namespace opt {
+namespace irpass {
+// {prim::kPrimIndexedSlicesGetIndices, {prim::kPrimMakeIndexedSlices, Xs}}
+// {prim::kPrimIndexedSlicesGetValues, {prim::kPrimMakeIndexedSlices, Xs}}
+// {prim::kPrimIndexedSlicesGetDenseShape, {prim::kPrimMakeIndexedSlices, Xs}}
+class IndexedSlicesEliminater : public AnfVisitor {
+ public:
+  AnfNodePtr operator()(const OptimizerPtr &, const AnfNodePtr &node) override {
+    Reset();
+    AnfVisitor::Match(prim::kPrimIndexedSlicesGetIndices, {IsCNode})(node);
+
+    if (is_match_) {
+      return tuple_->input(1);
+    }
+    AnfVisitor::Match(prim::kPrimIndexedSlicesGetValues, {IsCNode})(node);
+
+    if (is_match_) {
+      return tuple_->input(2);
+    }
+    AnfVisitor::Match(prim::kPrimIndexedSlicesGetDenseShape, {IsCNode})(node);
+
+    if (is_match_) {
+      return tuple_->input(3);
+    }
+    return nullptr;
+  }
+
+  void Visit(const CNodePtr &cnode) override {
+    if (IsPrimitiveCNode(cnode, prim::kPrimMakeIndexedSlices)) {
+      tuple_ = cnode;
+      is_match_ = true;
+    }
+  }
+
+  void Reset() {
+    tuple_ = nullptr;
+    is_match_ = false;
+  }
+
+ private:
+  bool is_match_{false};
+  CNodePtr tuple_{nullptr};
+};
+}  // namespace irpass
+}  // namespace opt
+}  // namespace mindspore
+#endif  // MINDSPORE_CCSRC_OPTIMIZER_IRPASS_INDEXED_SLICES_ELIMINATE_H_

mindspore/ccsrc/pipeline/action.cc

@@ -232,6 +232,9 @@ bool AbstractSpecializeAction(const ResourcePtr &res) {
       auto sparse_grad =
         py::cast<std::string>(parse::python_adapter::GetPyObjAttr(param_value->value(), "sparse_grad"));
       ptr->set_sparse_grad(sparse_grad);
+      auto has_indexed_slices_grad =
+        py::cast<bool>(parse::python_adapter::GetPyObjAttr(param_value->value(), "has_indexed_slices_grad"));
+      ptr->set_has_indexed_slices_grad(has_indexed_slices_grad);
 
       parallel::ParallelParameterContextRestoreInNoTraining(func_graph, param_node, ptr);
       args_spec.push_back(ptr);

mindspore/ccsrc/pipeline/init.cc

@@ -154,7 +154,9 @@ PYBIND11_MODULE(_c_expression, m) {
     .def("set_print_file_path", &mindspore::MsContext::set_print_file_path, "Set path to print.")
     .def("set_enable_graph_kernel", &mindspore::MsContext::set_enable_graph_kernel,
          "Set the GraphKernel switch to on or off.")
-    .def("get_enable_graph_kernel", &mindspore::MsContext::enable_graph_kernel, "Get the value of GraphKernel switch.");
+    .def("get_enable_graph_kernel", &mindspore::MsContext::enable_graph_kernel, "Get the value of GraphKernel switch.")
+    .def("get_enable_sparse_flag", &mindspore::MsContext::enable_sparse_flag, "Get whether to enable sparse.")
+    .def("set_enable_sparse_flag", &mindspore::MsContext::set_enable_sparse_flag, "Set whether to enable sparse.");
 
   (void)py::class_<mindspore::MpiConfig, std::shared_ptr<mindspore::MpiConfig>>(m, "MpiConfig")
     .def_static("get_instance", &mindspore::MpiConfig::GetInstance, "Get mpi config instance.")

mindspore/ccsrc/pipeline/pass.cc

@@ -156,6 +156,7 @@ OptPassGroupMap GetOptPassesB(const opt::irpass::OptimizeIRPassLib &irpass) {
     irpass.replace_refkey_by_param_,
     irpass.make_ref_eliminate_,
     irpass.get_ref_param_eliminate_,
+    irpass.indexed_slices_eliminate_,
   });
   OptPassGroupMap map({
     {"b_1", b_1},

mindspore/ccsrc/pipeline/resource.cc

@@ -33,148 +33,157 @@ namespace mindspore {
 namespace pipeline {
 
 MethodMap &GetMethodMap() {
-  static MethodMap method_map = {{kObjectTypeString,
+  static MethodMap method_map = {
-                                  {
+    {kObjectTypeString,
-                                    {"__bool__", std::string("str_bool")}  // C.str_bool
+     {
-                                  }},
+       {"__bool__", std::string("str_bool")}  // C.str_bool
-                                 {kMetaTypeNone,
+     }},
-                                  {
+    {kMetaTypeNone,
-                                    {"__bool__", std::string("none_bool")}  // C.none_bool
+     {
-                                  }},
+       {"__bool__", std::string("none_bool")}  // C.none_bool
-                                 {kNumberTypeBool,
+     }},
-                                  {
+    {kNumberTypeBool,
-                                    {"__and__", prim::kPrimBoolAnd},     // P.bool_and
+     {
-                                    {"__or__", prim::kPrimBoolOr},       // P.bool_or
+       {"__and__", prim::kPrimBoolAnd},     // P.bool_and
-                                    {"__eq__", prim::kPrimBoolEq},       // P.bool_eq
+       {"__or__", prim::kPrimBoolOr},       // P.bool_or
-                                    {"__ne__", std::string("bool_ne")},  // C.bool_ne
+       {"__eq__", prim::kPrimBoolEq},       // P.bool_eq
-                                    {"__bool__", prim::kPrimIdentity}    // P.identity
+       {"__ne__", std::string("bool_ne")},  // C.bool_ne
-                                  }},
+       {"__bool__", prim::kPrimIdentity}    // P.identity
-                                 {kNumberTypeInt,
+     }},
-                                  {
+    {kNumberTypeInt,
-                                    {"__add__", prim::kPrimScalarAdd},              // P.scalar_add
+     {
-                                    {"__sub__", prim::kPrimScalarSub},              // P.scalar_sub
+       {"__add__", prim::kPrimScalarAdd},              // P.scalar_add
-                                    {"__mul__", prim::kPrimScalarMul},              // P.scalar_mul
+       {"__sub__", prim::kPrimScalarSub},              // P.scalar_sub
-                                    {"__floordiv__", std::string("int_floordiv")},  // C.int_floordiv
+       {"__mul__", prim::kPrimScalarMul},              // P.scalar_mul
-                                    {"__truediv__", std::string("int_truediv")},    // C.int_truediv
+       {"__floordiv__", std::string("int_floordiv")},  // C.int_floordiv
-                                    {"__mod__", prim::kPrimScalarMod},              // P.scalar_mod
+       {"__truediv__", std::string("int_truediv")},    // C.int_truediv
-                                    {"__pow__", prim::kPrimScalarPow},              // P.scalar_pow
+       {"__mod__", prim::kPrimScalarMod},              // P.scalar_mod
-                                    {"__floor__", prim::kPrimIdentity},             // P.identity
+       {"__pow__", prim::kPrimScalarPow},              // P.scalar_pow
-                                    {"__trunc__", prim::kPrimIdentity},             // P.identity
+       {"__floor__", prim::kPrimIdentity},             // P.identity
-                                    {"__pos__", prim::kPrimScalarUadd},             // P.scalar_uadd
+       {"__trunc__", prim::kPrimIdentity},             // P.identity
-                                    {"__neg__", prim::kPrimScalarUsub},             // P.scalar_usub
+       {"__pos__", prim::kPrimScalarUadd},             // P.scalar_uadd
-                                    {"__eq__", prim::kPrimScalarEq},                // P.scalar_eq
+       {"__neg__", prim::kPrimScalarUsub},             // P.scalar_usub
-                                    {"__ne__", prim::kPrimScalarNe},                // P.scalar_ne
+       {"__eq__", prim::kPrimScalarEq},                // P.scalar_eq
-                                    {"__lt__", prim::kPrimScalarLt},                // P.scalar_lt
+       {"__ne__", prim::kPrimScalarNe},                // P.scalar_ne
-                                    {"__gt__", prim::kPrimScalarGt},                // P.scalar_gt
+       {"__lt__", prim::kPrimScalarLt},                // P.scalar_lt
-                                    {"__le__", prim::kPrimScalarLe},                // P.scalar_le
+       {"__gt__", prim::kPrimScalarGt},                // P.scalar_gt
-                                    {"__ge__", prim::kPrimScalarGe},                // P.scalar_ge
+       {"__le__", prim::kPrimScalarLe},                // P.scalar_le
-                                    {"__bool__", std::string("int_bool")},          // C.int_bool
+       {"__ge__", prim::kPrimScalarGe},                // P.scalar_ge
-                                    {"__ms_to_array__", prim::kPrimScalarToArray},  // P.scalar_to_array
+       {"__bool__", std::string("int_bool")},          // C.int_bool
-                                  }},
+       {"__ms_to_array__", prim::kPrimScalarToArray},  // P.scalar_to_array
-                                 {kNumberTypeUInt,
+     }},
-                                  {
+    {kNumberTypeUInt,
-                                    {"__add__", prim::kPrimScalarAdd},              // P.scalar_add,
+     {
-                                    {"__sub__", prim::kPrimScalarSub},              // P.scalar_sub,
+       {"__add__", prim::kPrimScalarAdd},              // P.scalar_add,
-                                    {"__mul__", prim::kPrimScalarMul},              // P.scalar_mul,
+       {"__sub__", prim::kPrimScalarSub},              // P.scalar_sub,
-                                    {"__floordiv__", prim::kPrimScalarDiv},         // P.scalar_div,
+       {"__mul__", prim::kPrimScalarMul},              // P.scalar_mul,
-                                    {"__truediv__", std::string("int_truediv")},    // C.int_truediv
+       {"__floordiv__", prim::kPrimScalarDiv},         // P.scalar_div,
-                                    {"__mod__", prim::kPrimScalarMod},              // P.scalar_mod,
+       {"__truediv__", std::string("int_truediv")},    // C.int_truediv
-                                    {"__pow__", prim::kPrimScalarPow},              // P.scalar_pow,
+       {"__mod__", prim::kPrimScalarMod},              // P.scalar_mod,
-                                    {"__floor__", prim::kPrimIdentity},             // P.identity,
+       {"__pow__", prim::kPrimScalarPow},              // P.scalar_pow,
-                                    {"__trunc__", prim::kPrimIdentity},             // P.identity,
+       {"__floor__", prim::kPrimIdentity},             // P.identity,
-                                    {"__pos__", prim::kPrimScalarUadd},             // P.scalar_uadd,
+       {"__trunc__", prim::kPrimIdentity},             // P.identity,
-                                    {"__neg__", prim::kPrimScalarUsub},             // P.scalar_usub,
+       {"__pos__", prim::kPrimScalarUadd},             // P.scalar_uadd,
-                                    {"__eq__", prim::kPrimScalarEq},                // P.scalar_eq,
+       {"__neg__", prim::kPrimScalarUsub},             // P.scalar_usub,
-                                    {"__ne__", prim::kPrimScalarNe},                // P.scalar_ne,
+       {"__eq__", prim::kPrimScalarEq},                // P.scalar_eq,
-                                    {"__lt__", prim::kPrimScalarLt},                // P.scalar_lt,
+       {"__ne__", prim::kPrimScalarNe},                // P.scalar_ne,
-                                    {"__gt__", prim::kPrimScalarGt},                // P.scalar_gt,
+       {"__lt__", prim::kPrimScalarLt},                // P.scalar_lt,
-                                    {"__le__", prim::kPrimScalarLe},                // P.scalar_le,
+       {"__gt__", prim::kPrimScalarGt},                // P.scalar_gt,
-                                    {"__ge__", prim::kPrimScalarGe},                // P.scalar_ge,
+       {"__le__", prim::kPrimScalarLe},                // P.scalar_le,
-                                    {"__bool__", std::string("int_bool")},          // C.int_bool
+       {"__ge__", prim::kPrimScalarGe},                // P.scalar_ge,
-                                    {"__ms_to_array__", prim::kPrimScalarToArray},  // P.scalar_to_array,
+       {"__bool__", std::string("int_bool")},          // C.int_bool
-                                  }},
+       {"__ms_to_array__", prim::kPrimScalarToArray},  // P.scalar_to_array,
-                                 {kNumberTypeFloat,
+     }},
-                                  {
+    {kNumberTypeFloat,
-                                    {"__add__", prim::kPrimScalarAdd},                // P.scalar_add,
+     {
-                                    {"__sub__", prim::kPrimScalarSub},                // P.scalar_sub,
+       {"__add__", prim::kPrimScalarAdd},                // P.scalar_add,
-                                    {"__mul__", prim::kPrimScalarMul},                // P.scalar_mul,
+       {"__sub__", prim::kPrimScalarSub},                // P.scalar_sub,
-                                    {"__floordiv__", std::string("float_floordiv")},  // C.float_floordiv
+       {"__mul__", prim::kPrimScalarMul},                // P.scalar_mul,
-                                    {"__truediv__", prim::kPrimScalarDiv},            // P.scalar_div,
+       {"__floordiv__", std::string("float_floordiv")},  // C.float_floordiv
-                                    {"__mod__", prim::kPrimScalarMod},                // P.scalar_mod,
+       {"__truediv__", prim::kPrimScalarDiv},            // P.scalar_div,
-                                    {"__pow__", prim::kPrimScalarPow},                // P.scalar_pow,
+       {"__mod__", prim::kPrimScalarMod},                // P.scalar_mod,
-                                    {"__floor__", prim::kPrimScalarFloor},            // P.scalar_floor,
+       {"__pow__", prim::kPrimScalarPow},                // P.scalar_pow,
-                                    {"__trunc__", prim::kPrimScalarTrunc},            // P.scalar_trunc,
+       {"__floor__", prim::kPrimScalarFloor},            // P.scalar_floor,
-                                    {"__pos__", prim::kPrimScalarUadd},               // P.scalar_uadd,
+       {"__trunc__", prim::kPrimScalarTrunc},            // P.scalar_trunc,
-                                    {"__neg__", prim::kPrimScalarUsub},               // P.scalar_usub,
+       {"__pos__", prim::kPrimScalarUadd},               // P.scalar_uadd,
-                                    {"__eq__", prim::kPrimScalarEq},                  // P.scalar_eq,
+       {"__neg__", prim::kPrimScalarUsub},               // P.scalar_usub,
-                                    {"__ne__", prim::kPrimScalarNe},                  // P.scalar_ne,
+       {"__eq__", prim::kPrimScalarEq},                  // P.scalar_eq,
-                                    {"__lt__", prim::kPrimScalarLt},                  // P.scalar_lt,
+       {"__ne__", prim::kPrimScalarNe},                  // P.scalar_ne,
-                                    {"__gt__", prim::kPrimScalarGt},                  // P.scalar_gt,
+       {"__lt__", prim::kPrimScalarLt},                  // P.scalar_lt,
-                                    {"__le__", prim::kPrimScalarLe},                  // P.scalar_le,
+       {"__gt__", prim::kPrimScalarGt},                  // P.scalar_gt,
-                                    {"__ge__", prim::kPrimScalarGe},                  // P.scalar_ge,
+       {"__le__", prim::kPrimScalarLe},                  // P.scalar_le,
-                                    {"__bool__", std::string("float_bool")},          // C.float_bool
+       {"__ge__", prim::kPrimScalarGe},                  // P.scalar_ge,
-                                    {"__ms_to_array__", prim::kPrimScalarToArray},    // P.scalar_to_array,
+       {"__bool__", std::string("float_bool")},          // C.float_bool
-                                  }},
+       {"__ms_to_array__", prim::kPrimScalarToArray},    // P.scalar_to_array,
-                                 {kObjectTypeTuple,
+     }},
-                                  {
+    {kObjectTypeTuple,
-                                    {"__len__", prim::kPrimTupleLen},                  // P.tuple_len,
+     {
-                                    {"__getitem__", prim::kPrimTupleGetItem},          // P.tuple_getitem,
+       {"__len__", prim::kPrimTupleLen},                  // P.tuple_len,
-                                    {"__setitem__", prim::kPrimTupleSetItem},          // P.tuple_setitem,
+       {"__getitem__", prim::kPrimTupleGetItem},          // P.tuple_getitem,
-                                    {"__ms_iter__", prim::kPrimIdentity},              // P.identity,
+       {"__setitem__", prim::kPrimTupleSetItem},          // P.tuple_setitem,
-                                    {"__ms_next__", std::string("tuple_next")},        // C.tuple_next,
+       {"__ms_iter__", prim::kPrimIdentity},              // P.identity,
-                                    {"__ms_hasnext__", std::string("tuple_hasnext")},  // C.tuple_hasnext
+       {"__ms_next__", std::string("tuple_next")},        // C.tuple_next,
-                                    {"__bool__", std::string("tuple_bool")}            // C.tuple_bool
+       {"__ms_hasnext__", std::string("tuple_hasnext")},  // C.tuple_hasnext
-                                  }},
+       {"__bool__", std::string("tuple_bool")}            // C.tuple_bool
-                                 {kObjectTypeList,
+     }},
-                                  {
+    {kObjectTypeList,
-                                    {"__len__", prim::kPrimListLen},            // P.list_len,
+     {
-                                    {"__getitem__", prim::kPrimListGetItem},    // P.list_getitem,
+       {"__len__", prim::kPrimListLen},            // P.list_len,
-                                    {"__setitem__", prim::kPrimListSetItem},    // P.list_setitem,
+       {"__getitem__", prim::kPrimListGetItem},    // P.list_getitem,
-                                    {"__ms_iter__", prim::kPrimIdentity},       // P.identity
+       {"__setitem__", prim::kPrimListSetItem},    // P.list_setitem,
-                                    {"__ms_next__", std::string("list_next")},  // C.list_next
+       {"__ms_iter__", prim::kPrimIdentity},       // P.identity
-                                    {"append", std::string("list_append")},     // C.list_next
+       {"__ms_next__", std::string("list_next")},  // C.list_next
-                                    {"__bool__", std::string("list_bool")},     // C.list_bool
+       {"append", std::string("list_append")},     // C.list_next
-                                    {"__ms_hasnext__", std::string("list_hasnext")},
+       {"__bool__", std::string("list_bool")},     // C.list_bool
-                                  }},
+       {"__ms_hasnext__", std::string("list_hasnext")},
-                                 {kObjectTypeDictionary,
+     }},
-                                  {
+    {kObjectTypeDictionary,
-                                    {"__len__", prim::kPrimDictLen},          // P.dict_len
+     {
-                                    {"__getitem__", prim::kPrimDictGetItem},  // P.dict_getitem
+       {"__len__", prim::kPrimDictLen},          // P.dict_len
-                                    {"__setitem__", prim::kPrimDictSetItem},  // P.dict_setitem,
+       {"__getitem__", prim::kPrimDictGetItem},  // P.dict_getitem
-                                    {"__bool__", std::string("dict_bool")}    // C.dict_bool
+       {"__setitem__", prim::kPrimDictSetItem},  // P.dict_setitem,
-                                  }},
+       {"__bool__", std::string("dict_bool")}    // C.dict_bool
-                                 {kObjectTypeTensorType,
+     }},
-                                  {
+    {kObjectTypeTensorType,
-                                    {"__add__", std::string("add")},             // C.add
+     {
-                                    {"__sub__", std::string("sub")},             // C.sub
+       {"__add__", std::string("add")},                    // C.add
-                                    {"__mul__", std::string("mul")},             // C.mul
+       {"__sub__", std::string("sub")},                    // C.sub
-                                    {"__truediv__", std::string("truediv")},     // C.truediv
+       {"__mul__", std::string("mul")},                    // C.mul
-                                    {"__floordiv__", std::string("floordiv")},   // C.floordiv
+       {"__truediv__", std::string("truediv")},            // C.truediv
-                                    {"__mod__", std::string("mod")},             // C.mod
+       {"__floordiv__", std::string("floordiv")},          // C.floordiv
-                                    {"__pow__", std::string("pow_")},            // C.pow
+       {"__mod__", std::string("mod")},                    // C.mod
-                                    {"__floor__", std::string("array_floor")},   // C.array_floor
+       {"__pow__", std::string("pow_")},                   // C.pow
-                                    {"__trunc__", std::string("array_trunc")},   // C.array_trunc
+       {"__floor__", std::string("array_floor")},          // C.array_floor
-                                    {"__pos__", std::string("array_uadd")},      // C.array_uadd
+       {"__trunc__", std::string("array_trunc")},          // C.array_trunc
-                                    {"__neg__", std::string("array_usub")},      // C.array_usub
+       {"__pos__", std::string("array_uadd")},             // C.array_uadd
-                                    {"__eq__", std::string("eq")},               // C.eq
+       {"__neg__", std::string("array_usub")},             // C.array_usub
-                                    {"__ne__", std::string("ne")},               // C.ne
+       {"__eq__", std::string("eq")},                      // C.eq
-                                    {"__lt__", std::string("lt")},               // C.lt
+       {"__ne__", std::string("ne")},                      // C.ne
-                                    {"__gt__", std::string("gt")},               // C.gt
+       {"__lt__", std::string("lt")},                      // C.lt
-                                    {"__le__", std::string("le")},               // C.le
+       {"__gt__", std::string("gt")},                      // C.gt
-                                    {"__ge__", std::string("ge")},               // C.ge
+       {"__le__", std::string("le")},                      // C.le
-                                    {"__matmul__", prim::kPrimDot},              // P.dot,
+       {"__ge__", std::string("ge")},                      // C.ge
-                                    {"__len__", prim::kPrimArrayLen},            // P.array_len,
+       {"__matmul__", prim::kPrimDot},                     // P.dot,
-                                    {"__getitem__", prim::kPrimArrayGetItem},    // P.array_getitem,
+       {"__len__", prim::kPrimArrayLen},                   // P.array_len,
-                                    {"__setitem__", prim::kPrimArraySetItem},    // P.array_setitem,
+       {"__getitem__", prim::kPrimArrayGetItem},           // P.array_getitem,
-                                    {"__ms_iter__", std::string("array_iter")},  // C.array_iter
+       {"__setitem__", prim::kPrimArraySetItem},           // P.array_setitem,
-                                    {"__ms_to_array__", prim::kPrimIdentity},    // P.identity,
+       {"__ms_iter__", std::string("array_iter")},         // C.array_iter
-                                    {"item", prim::kPrimArrayToScalar},          // P.array_to_scalar,
+       {"__ms_to_array__", prim::kPrimIdentity},           // P.identity,
-                                    {"transpose", std::string("transpose")},     // P.transpose
+       {"item", prim::kPrimArrayToScalar},                 // P.array_to_scalar,
-                                    {"__bool__", std::string("tensor_bool")},    // C.tensor_bool
+       {"transpose", std::string("transpose")},            // P.transpose
-                                  }},
+       {"__bool__", std::string("tensor_bool")},           // C.tensor_bool
-                                 {kObjectTypeJTagged, {}},
+       {"is_indexed_slices", prim::kPrimIsIndexedSlices},  // F.is_indexed_slices
-                                 {kObjectTypeSymbolicKeyType, {}},
+     }},
-                                 {kObjectTypeEnvType, {}}};
+    {kObjectTypeIndexedSlicesType,
+     {
+       {"is_indexed_slices", prim::kPrimIsIndexedSlices},       // F.is_indexed_slices
+       {"values", prim::kPrimIndexedSlicesGetValues},           // F.indexed_slices_get_values
+       {"indices", prim::kPrimIndexedSlicesGetIndices},         // F.indexed_slices_get_indices
+       {"dense_shape", prim::kPrimIndexedSlicesGetDenseShape},  // F.indexed_slices_get_dense_shape
+     }},
+    {kObjectTypeJTagged, {}},
+    {kObjectTypeSymbolicKeyType, {}},
+    {kObjectTypeEnvType, {}}};
   return method_map;
 }
 

mindspore/ccsrc/pipeline/static_analysis/abstract_value.cc

@@ -30,6 +30,10 @@ bool AbstractBase::operator==(const AbstractBase &other) const {
   if (tid() != other.tid()) {
     return false;
   }
+  if (BuildType()->type_id() == kObjectTypeUndeterminedType &&
+      other.BuildType()->type_id() == kObjectTypeUndeterminedType) {
+    return true;
+  }
   if (value_ == nullptr || other.value_ == nullptr) {
     MS_LOG(EXCEPTION) << "If value_ is nullptr, AbstractBase::operator== should not be called. this: "
                       << this->ToString() << ", other: " << other.ToString();
@@ -65,7 +69,7 @@ std::string AbstractBase::ToString() const {
   MS_EXCEPTION_IF_NULL(shape_);
   buffer << type_name() << "("
          << "Type: " << type_->ToString() << " Value: " << value << " Shape: " << shape_->ToString()
-         << " sparse_grad: " << sparse_grad_ << ")";
+         << " sparse_grad: " << sparse_grad_ << " has_indexed_slices_grad: " << has_indexed_slices_grad_ << ")";
   return buffer.str();
 }
 
@@ -76,6 +80,7 @@ AbstractBasePtr AbstractScalar::Join(const AbstractBasePtr &other) {
   if (*this == *other) {
     auto ret = shared_from_base<AbstractBase>();
     ret->set_sparse_grad(sparse_grad());
+    ret->set_has_indexed_slices_grad(has_indexed_slices_grad());
     return ret;
   }
   auto value_self = GetValueTrack();
@@ -85,10 +90,12 @@ AbstractBasePtr AbstractScalar::Join(const AbstractBasePtr &other) {
   if (res_value == value_self) {
     auto ret = shared_from_base<AbstractBase>();
     ret->set_sparse_grad(sparse_grad());
+    ret->set_has_indexed_slices_grad(has_indexed_slices_grad());
     return ret;
   }
   auto ret = std::make_shared<AbstractScalar>(res_value, res_type);
   ret->set_sparse_grad(sparse_grad());
+  ret->set_has_indexed_slices_grad(has_indexed_slices_grad());
   return ret;
 }
 
@@ -409,6 +416,14 @@ std::size_t AbstractSlice::hash() const {
   return hash_combine({tid(), start_->hash(), stop_->hash(), step_->hash()});
 }
 
+ShapePtr AbstractUndetermined::shape() const {
+  auto shp = dyn_cast<Shape>(GetShapeTrack());
+  if (shp == nullptr) {
+    MS_LOG(EXCEPTION) << "Tensor should have a shape.";
+  }
+  return shp;
+}
+
 TypePtr AbstractTensor::BuildType() const {
   MS_EXCEPTION_IF_NULL(element_);
   TypePtr element_type = element_->BuildType();
@@ -425,6 +440,13 @@ BaseShapePtr AbstractTensor::BuildShape() const {
 }
 
 AbstractBasePtr AbstractTensor::Join(const AbstractBasePtr &other) {
+  if (other->BuildType()->type_id() == kObjectTypeUndeterminedType) {
+    auto other_tensor = dyn_cast<AbstractUndetermined>(other);
+    auto element = element_->Join(other_tensor->element());
+    auto shape = ShapeJoin(this->shape(), other_tensor->shape());
+    auto ret = std::make_shared<AbstractUndetermined>(element, shape);
+    return ret;
+  }
   auto other_tensor = dyn_cast<AbstractTensor>(other);
   if (other_tensor == nullptr) {
     MS_LOG(EXCEPTION) << "Join failed as type mismatch, this: " << ToString() << ", other: " << other->ToString();
@@ -433,6 +455,7 @@ AbstractBasePtr AbstractTensor::Join(const AbstractBasePtr &other) {
   auto shape = ShapeJoin(this->shape(), other_tensor->shape());
   auto ret = std::make_shared<AbstractTensor>(element, shape);
   ret->set_sparse_grad(sparse_grad());
+  ret->set_has_indexed_slices_grad(has_indexed_slices_grad());
   return ret;
 }
 
@@ -474,6 +497,7 @@ AbstractBasePtr AbstractTensor::Clone() const {
   clone->set_shape(shp->Clone());
   clone->set_value(GetValueTrack());
   clone->set_sparse_grad(sparse_grad());
+  clone->set_has_indexed_slices_grad(has_indexed_slices_grad());
   return clone;
 }
 
@@ -484,6 +508,7 @@ AbstractBasePtr AbstractTensor::Broaden() const {
   broaden->set_shape(shp->Clone());
   broaden->set_value(kAnyValue);
   broaden->set_sparse_grad(sparse_grad());
+  broaden->set_has_indexed_slices_grad(has_indexed_slices_grad());
   return broaden;
 }
 
@@ -495,17 +520,10 @@ AbstractBasePtr AbstractTensor::BroadenWithShape() const {
   broaden->set_shape(shp);
   broaden->set_value(kAnyValue);
   broaden->set_sparse_grad(sparse_grad());
+  broaden->set_has_indexed_slices_grad(has_indexed_slices_grad());
   return broaden;
 }
 
-ShapePtr AbstractTensor::shape() const {
-  auto shp = dyn_cast<Shape>(GetShapeTrack());
-  if (shp == nullptr) {
-    MS_LOG(EXCEPTION) << "Tensor should have a shape.";
-  }
-  return shp;
-}
-
 std::string AbstractTensor::ToString() const {
   std::ostringstream buffer;
   BaseShapePtr shape_track = GetShapeTrack();
@@ -516,7 +534,7 @@ std::string AbstractTensor::ToString() const {
   buffer << type_name() << "("
          << "shape: " << shape_track->ToString() << ", element: " << element_->ToString()
          << ", value_ptr: " << value_track << ", value: " << value_track->ToString() << " sparse_grad " << sparse_grad()
-         << ")";
+         << " has_indexed_slices_grad " << has_indexed_slices_grad() << ")";
   return buffer.str();
 }
 
@@ -1019,5 +1037,64 @@ std::size_t AbstractBasePtrListHasher::operator()(const AbstractBasePtrList &arg
 bool AbstractBasePtrListEqual::operator()(const AbstractBasePtrList &lhs, const AbstractBasePtrList &rhs) const {
   return AbstractBasePtrListDeepEqual(lhs, rhs);
 }
+
+// IndexedSlices
+TypePtr AbstractIndexedSlices::BuildType() const {
+  MS_EXCEPTION_IF_NULL(element());
+  TypePtr element_type = element()->BuildType();
+  return std::make_shared<IndexedSlicesType>(element_type);
+}
+
+AbstractBasePtr AbstractIndexedSlices::Clone() const {
+  MS_EXCEPTION_IF_NULL(element());
+  auto clone = std::make_shared<AbstractIndexedSlices>(element()->Clone());
+  ShapePtr shp = shape();
+  clone->set_shape(shp->Clone());
+  clone->set_value(GetValueTrack());
+  clone->set_indices(indices_->Clone()->cast<AbstractTensorPtr>());
+  clone->set_values(values_->Clone()->cast<AbstractTensorPtr>());
+  clone->set_dense_shape(dense_shape_->Clone()->cast<AbstractTuplePtr>());
+  return clone;
+}
+
+AbstractBasePtr AbstractIndexedSlices::Broaden() const {
+  MS_EXCEPTION_IF_NULL(element());
+  auto broaden = std::make_shared<AbstractIndexedSlices>(element()->Broaden());
+  auto shp = shape();
+  broaden->set_shape(shp->Clone());
+  broaden->set_value(kAnyValue);
+  broaden->set_indices(indices_->Clone()->cast<AbstractTensorPtr>());
+  broaden->set_values(values_->Clone()->cast<AbstractTensorPtr>());
+  broaden->set_dense_shape(dense_shape_->Clone()->cast<AbstractTuplePtr>());
+  return broaden;
+}
+
+AbstractBasePtr AbstractIndexedSlices::BroadenWithShape() const {
+  MS_EXCEPTION_IF_NULL(element());
+  auto broaden = std::make_shared<AbstractIndexedSlices>(element()->Broaden());
+  auto shp = shape()->Clone();
+  shp->Broaden();
+  broaden->set_shape(shp);
+  broaden->set_value(kAnyValue);
+  broaden->set_indices(indices_->Clone()->cast<AbstractTensorPtr>());
+  broaden->set_values(values_->Clone()->cast<AbstractTensorPtr>());
+  broaden->set_dense_shape(dense_shape_->Clone()->cast<AbstractTuplePtr>());
+  return broaden;
+}
+
+std::string AbstractIndexedSlices::ToString() const {
+  std::ostringstream buffer;
+  BaseShapePtr shape_track = GetShapeTrack();
+  MS_EXCEPTION_IF_NULL(shape_track);
+  MS_EXCEPTION_IF_NULL(element());
+  auto value_track = GetValueTrack();
+  MS_EXCEPTION_IF_NULL(value_track);
+  buffer << type_name() << "("
+         << "shape: " << shape_track->ToString() << ", element: " << element()->ToString()
+         << ", value_ptr: " << value_track << ", value: " << value_track->ToString() << ")"
+         << ", indices: " << indices_->ToString() << ", values" << values_->ToString()
+         << ", dense_shape: " << dense_shape_->ToString();
+  return buffer.str();
+}
 }  // namespace abstract
 }  // namespace mindspore

mindspore/ccsrc/pipeline/static_analysis/abstract_value.h

@@ -44,7 +44,7 @@ class AbstractBase : public Base {
  public:
   explicit AbstractBase(const ValuePtr &value = nullptr, const TypePtr &type = kAnyType,
                         const BaseShapePtr &shape = kNoShape)
-      : value_(value), type_(type), shape_(shape), sparse_grad_("") {}
+      : value_(value), type_(type), shape_(shape), sparse_grad_(""), has_indexed_slices_grad_(false) {}
   ~AbstractBase() override = default;
   MS_DECLARE_PARENT(AbstractBase, Base)
 
@@ -54,12 +54,16 @@ class AbstractBase : public Base {
   virtual bool operator==(const AbstractBase &other) const;
   void set_value(const ValuePtr &value) { value_ = value; }
   void set_sparse_grad(const std::string &sparse_grad) { sparse_grad_ = sparse_grad; }
+  void set_has_indexed_slices_grad(const bool &has_indexed_slices_grad) {
+    has_indexed_slices_grad_ = has_indexed_slices_grad;
+  }
   void set_type(const TypePtr &type) { type_ = type; }
   void set_shape(const BaseShapePtr &shape) { shape_ = shape; }
   void set_value_desc(const std::string &desc) { value_desc_ = desc; }
   const std::string &value_desc() const { return value_desc_; }
   ValuePtr GetValueTrack() const { return value_; }
   const std::string &sparse_grad() const { return sparse_grad_; }
+  const bool &has_indexed_slices_grad() const { return has_indexed_slices_grad_; }
   TypePtr GetTypeTrack() const { return type_; }
   BaseShapePtr GetShapeTrack() const { return shape_; }
 
@@ -88,6 +92,7 @@ class AbstractBase : public Base {
   BaseShapePtr shape_;
   std::string value_desc_;  // store initial value description for error report
   std::string sparse_grad_;
+  bool has_indexed_slices_grad_;
 };
 
 class AbstractScalar : public AbstractBase {
@@ -231,35 +236,49 @@ class AbstractKeywordArg : public AbstractBase {
 };
 using AbstractKeywordArgPtr = std::shared_ptr<AbstractKeywordArg>;
 
-class AbstractTensor : public AbstractBase {
+class AbstractUndetermined : public AbstractBase {
  public:
+  // shape and type are all unknown
+  AbstractUndetermined() : AbstractBase(kAnyValue) {}
   // only element_ and value, shape track are valid member, type track are unknown.
-  explicit AbstractTensor(const AbstractBasePtr &element, const BaseShapePtr &shape = std::make_shared<Shape>())
+  explicit AbstractUndetermined(const AbstractBasePtr &element, const BaseShapePtr &shape = std::make_shared<Shape>())
       : AbstractBase(kAnyValue), element_(element) {
     if (element == nullptr) {
       MS_LOG(EXCEPTION) << "element is nullptr";
     }
-    if (element->isa<AbstractTensor>()) {
+    if (element->isa<AbstractUndetermined>()) {
       MS_LOG(EXCEPTION) << "element type error";
     }
     set_shape(shape);
   }
-  AbstractTensor(const TypePtr &element_type, const std::vector<int> &shape)
+  AbstractUndetermined(const TypePtr &element_type, const std::vector<int> &shape)
       : AbstractBase(kAnyValue), element_(std::make_shared<AbstractScalar>(kAnyValue, element_type)) {
     if (element_type == nullptr) {
       MS_LOG(EXCEPTION) << "element_type is nullptr";
     }
     set_shape(std::make_shared<Shape>(shape));
   }
-  explicit AbstractTensor(const tensor::TensorPtr &tensor)
+  ~AbstractUndetermined() override = default;
-      : AbstractBase(tensor), element_(std::make_shared<AbstractScalar>(kAnyValue, tensor->Dtype())) {
+  MS_DECLARE_PARENT(AbstractUndetermined, AbstractBase)
-    if (tensor == nullptr) {
+  TypePtr BuildType() const override { return std::make_shared<UndeterminedType>(); }
-      MS_LOG(EXCEPTION) << "tensor is nullptr";
+  AbstractBasePtr Clone() const override { return std::make_shared<AbstractUndetermined>(); }
-    }
+  const AbstractBasePtr element() const { return element_; }
-    set_shape(std::make_shared<Shape>(tensor->shape()));
+  ShapePtr shape() const;
-  }
+
+ protected:
+  AbstractBasePtr element_;
+};
+
+class AbstractTensor : public AbstractUndetermined {
+ public:
+  // only element_ and value, shape track are valid member, type track are unknown.
+  explicit AbstractTensor(const AbstractBasePtr &element, const BaseShapePtr &shape = std::make_shared<Shape>())
+      : AbstractUndetermined(element, shape) {}
+  AbstractTensor(const TypePtr &element_type, const std::vector<int> &shape)
+      : AbstractUndetermined(element_type, shape) {}
+  explicit AbstractTensor(const tensor::TensorPtr &tensor) : AbstractUndetermined(tensor->Dtype(), tensor->shape()) {}
   ~AbstractTensor() override = default;
-  MS_DECLARE_PARENT(AbstractTensor, AbstractBase)
+  MS_DECLARE_PARENT(AbstractTensor, AbstractUndetermined)
 
   TypePtr BuildType() const override;
   BaseShapePtr BuildShape() const override;
@@ -271,9 +290,7 @@ class AbstractTensor : public AbstractBase {
   bool operator==(const AbstractTensor &other) const;
   bool operator==(const AbstractBase &other) const override;
 
-  ShapePtr shape() const;
   std::string ToString() const override;
-  const AbstractBasePtr element() const { return element_; }
   std::size_t hash() const override {
     auto value = GetValueTrack();
     auto hash_sum = hash_combine(tid(), element_->hash());
@@ -285,9 +302,6 @@ class AbstractTensor : public AbstractBase {
     }
     return hash_sum;
   }
-
- private:
-  AbstractBasePtr element_;
 };
 using AbstractTensorPtr = std::shared_ptr<AbstractTensor>;
 using AbstractTensorPtrList = std::vector<AbstractTensorPtr>;
@@ -585,6 +599,35 @@ struct AbstractBasePtrListEqual {
 
 std::size_t AbstractBasePtrListHash(const AbstractBasePtrList &args_spec_list);
 bool AbstractBasePtrListDeepEqual(const AbstractBasePtrList &lhs, const AbstractBasePtrList &rhs);
+
+// IndexedSlices
+class AbstractIndexedSlices : public AbstractUndetermined {
+ public:
+  explicit AbstractIndexedSlices(const AbstractBasePtr &element, const BaseShapePtr &shape = std::make_shared<Shape>())
+      : AbstractUndetermined(element, shape) {}
+  AbstractIndexedSlices(const TypePtr &element_type, const std::vector<int> &shape)
+      : AbstractUndetermined(element_type, shape) {}
+  ~AbstractIndexedSlices() override = default;
+  MS_DECLARE_PARENT(AbstractIndexedSlices, AbstractUndetermined)
+
+  const AbstractTensorPtr indices() const { return indices_; }
+  const AbstractTensorPtr values() const { return values_; }
+  const AbstractTuplePtr dense_shape() const { return dense_shape_; }
+  void set_indices(const AbstractTensorPtr &indices) { indices_ = indices; }
+  void set_values(const AbstractTensorPtr &values) { values_ = values; }
+  void set_dense_shape(const AbstractTuplePtr &dense_shape) { dense_shape_ = dense_shape; }
+  TypePtr BuildType() const override;
+  AbstractBasePtr Clone() const override;
+  AbstractBasePtr Broaden() const override;
+  AbstractBasePtr BroadenWithShape() const;
+
+  std::string ToString() const override;
+
+ private:
+  AbstractTensorPtr indices_;
+  AbstractTensorPtr values_;
+  AbstractTuplePtr dense_shape_;
+};
 }  // namespace abstract
 }  // namespace mindspore
 #endif  // PIPELINE_STATIC_ANALYSIS_ABSTRACT_VALUE_H_

mindspore/ccsrc/pipeline/static_analysis/evaluator.h

@@ -58,6 +58,20 @@ class Evaluator : public Base {
     return args_spec_list;
   }
 
+  virtual EvalResultPtr AbstractEval(const AbstractBasePtrList &args_spec_list) {
+    auto is_abstract = std::any_of(args_spec_list.begin(), args_spec_list.end(), [](auto &arg) {
+      if (arg->BuildType()->type_id() == kObjectTypeUndeterminedType) {
+        return true;
+      }
+      return false;
+    });
+    if (is_abstract) {
+      MS_LOG(DEBUG) << "Eval " << identifier_ << " return abstract result";
+      return std::make_shared<EvalResult>(std::make_shared<AbstractUndetermined>(), std::make_shared<AttrValueMap>());
+    }
+    return nullptr;
+  }
+
   std::string ToString() const override { return identifier_; }
 
   virtual AnfNodePtr bound_node() const { return bound_node_.lock(); }

mindspore/ccsrc/pipeline/static_analysis/param_validator.h

@@ -66,6 +66,7 @@ ABSTRACT_REPORT_NAME_TRAITS(Function)
 ABSTRACT_REPORT_NAME_TRAITS(Type)
 ABSTRACT_REPORT_NAME_TRAITS(KeywordArg)
 ABSTRACT_REPORT_NAME_TRAITS(Class)
+ABSTRACT_REPORT_NAME_TRAITS(IndexedSlices)
 
 template <typename T>
 std::shared_ptr<T> CheckArg(const std::string &op, const AbstractBasePtrList &args_spec_list, size_t index) {

mindspore/ccsrc/pipeline/static_analysis/prim.cc

@@ -36,6 +36,7 @@
 #include "pipeline/parse/resolve.h"
 #include "ir/tensor.h"
 #include "utils/convert_utils.h"
+#include "utils/context/ms_context.h"
 #include "pipeline/parse/data_converter.h"
 #include "pipeline/static_analysis/param_validator.h"
 #include "common/utils.h"
@@ -132,6 +133,12 @@ PrimitiveEvalImplMap &GetPrimitiveToEvalImplMap() {
     {prim::kPrimControlDepend, {InferImplControlDepend, true}},
     // Debug
     {prim::kPrimDebug, {InferImplDebug, true}},
+    // IndexedSlices
+    {prim::kPrimMakeIndexedSlices, {InferImplMakeIndexedSlices, true}},
+    {prim::kPrimIndexedSlicesGetValues, {InferImplIndexedSlicesGetValues, true}},
+    {prim::kPrimIndexedSlicesGetIndices, {InferImplIndexedSlicesGetIndices, true}},
+    {prim::kPrimIndexedSlicesGetDenseShape, {InferImplIndexedSlicesGetDenseShape, true}},
+    {prim::kPrimIsIndexedSlices, {InferImplIsIndexedSlices, true}},
   };
   return prim_eval_implement_map;
 }
@@ -139,6 +146,16 @@ PrimitiveEvalImplMap &GetPrimitiveToEvalImplMap() {
 using mindspore::parse::PyObjectWrapper;
 
 EvalResultPtr StandardPrimEvaluator::EvalPrim(const AnalysisEnginePtr &engine, const AbstractBasePtrList &args) {
+  auto context = MsContext::GetInstance();
+  MS_EXCEPTION_IF_NULL(context);
+  bool enable_sparse_flag = context->enable_sparse_flag();
+  if (enable_sparse_flag && prim_ != prim::kPrimMakeTuple && prim_ != prim::kPrimSwitch) {
+    auto ret_abstract = AbstractEval(args);
+    if (ret_abstract != nullptr) {
+      MS_LOG(DEBUG) << "StandardPrimEvaluator eval Undetermined";
+      return ret_abstract;
+    }
+  }
   prim_->BeginRecordAddAttr();
   AbstractBasePtr abs_base = eval_impl_(engine, prim_, args);
   prim_->EndRecordAddAttr();
@@ -485,6 +502,16 @@ AbstractBasePtr PyInferRes2Abstract(const PrimitivePyPtr &prim_py, const py::dic
 }  // end anonymous namespace
 
 EvalResultPtr PythonPrimEvaluator::EvalPrim(const AnalysisEnginePtr &, const AbstractBasePtrList &args) {
+  auto context = MsContext::GetInstance();
+  MS_EXCEPTION_IF_NULL(context);
+  bool enable_sparse_flag = context->enable_sparse_flag();
+  if (enable_sparse_flag) {
+    auto ret_abstract = AbstractEval(args);
+    if (ret_abstract != nullptr) {
+      MS_LOG(DEBUG) << "PythonPrimEvaluator eval Undetermined";
+      return ret_abstract;
+    }
+  }
   MS_LOG(DEBUG) << "Eval for:" << prim_py_->ToString();
 
   const auto &iter = cache_->find(args);
@@ -512,6 +539,16 @@ EvalResultPtr PythonPrimEvaluator::EvalPrim(const AnalysisEnginePtr &, const Abs
 }
 
 EvalResultPtr UniformPrimEvaluator::EvalPrim(const AnalysisEnginePtr &, const AbstractBasePtrList &args) {
+  auto context = MsContext::GetInstance();
+  MS_EXCEPTION_IF_NULL(context);
+  bool enable_sparse_flag = context->enable_sparse_flag();
+  if (enable_sparse_flag) {
+    auto ret_abstract = AbstractEval(args);
+    if (ret_abstract != nullptr) {
+      MS_LOG(DEBUG) << "UniformPrimEvaluator eval Undetermined";
+      return ret_abstract;
+    }
+  }
   // if func_desc_.retval type is super class of parameter type, then make the retval type as parameter type.
   if (nargs_ != args.size()) {
     MS_LOG(ERROR) << "UniformPrimEvaluator expect " << nargs_ << " args, but got " << args.size() << " inputs";
@@ -871,6 +908,7 @@ class RefToEmbedEvaluator : public SymbolicPrimEvaluator {
       auto ref_value = ref_abs->ref();
       MS_EXCEPTION_IF_NULL(ref_value);
       ret->set_sparse_grad(ref_value->sparse_grad());
+      ret->set_has_indexed_slices_grad(ref_value->has_indexed_slices_grad());
       return std::make_shared<EvalResult>(ret, std::make_shared<AttrValueMap>());
     }
 
@@ -886,6 +924,7 @@ class RefToEmbedEvaluator : public SymbolicPrimEvaluator {
     std::shared_ptr<SymbolicKeyInstance> key = std::make_shared<SymbolicKeyInstance>(node, x);
     std::shared_ptr<AbstractScalar> abs_scalar = std::make_shared<AbstractScalar>(key, type);
     abs_scalar->set_sparse_grad(x->sparse_grad());
+    abs_scalar->set_has_indexed_slices_grad(x->has_indexed_slices_grad());
     return std::make_shared<EvalResult>(abs_scalar, std::make_shared<AttrValueMap>());
   }
 };
@@ -897,6 +936,16 @@ class GetAttrEvaluator : public TransitionPrimEvaluator {
   MS_DECLARE_PARENT(GetAttrEvaluator, TransitionPrimEvaluator);
   EvalResultPtr EvalPrim(const AnalysisEnginePtr &engine, const AbstractBasePtrList &args_spec_list,
                          const ConfigPtr &in_conf0, const AnfNodeConfigPtr &out_conf) override {
+    auto context = MsContext::GetInstance();
+    MS_EXCEPTION_IF_NULL(context);
+    bool enable_sparse_flag = context->enable_sparse_flag();
+    if (enable_sparse_flag) {
+      auto ret_abstract = AbstractEval(args_spec_list);
+      if (ret_abstract != nullptr) {
+        MS_LOG(DEBUG) << "GetAttrEvaluator eval Undetermined";
+        return ret_abstract;
+      }
+    }
     // Inputs: data, item
     if (args_spec_list.size() != 2) {
       MS_LOG(EXCEPTION) << "Expected args_spec_list size = 2, but has size:" << args_spec_list.size();

mindspore/ccsrc/pipeline/static_analysis/prim.h

@@ -350,6 +350,17 @@ AbstractBasePtr InferImplControlDepend(const AnalysisEnginePtr &, const Primitiv
 AbstractBasePtr InferImplDebug(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
                                const AbstractBasePtrList &args_spec_list);
 void InitUndeterminedFromEnv(const std::string &sparse_shape_types);
+
+AbstractBasePtr InferImplMakeIndexedSlices(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
+                                           const AbstractBasePtrList &args_spec_list);
+AbstractBasePtr InferImplIndexedSlicesGetValues(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
+                                                const AbstractBasePtrList &args_spec_list);
+AbstractBasePtr InferImplIndexedSlicesGetIndices(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
+                                                 const AbstractBasePtrList &args_spec_list);
+AbstractBasePtr InferImplIndexedSlicesGetDenseShape(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
+                                                    const AbstractBasePtrList &args_spec_list);
+AbstractBasePtr InferImplIsIndexedSlices(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
+                                         const AbstractBasePtrList &args_spec_list);
 }  // namespace abstract
 }  // namespace mindspore
 

mindspore/ccsrc/pipeline/static_analysis/static_analysis.cc

@@ -228,6 +228,10 @@ EvalResultPtr AnalysisEngine::EvalCNode(const CNodePtr &cnode, const AnfNodeConf
     MS_LOG(EXCEPTION) << "func_conf.GetEvaluatedValue() return null, func_conf: " << func_conf->ToString()
                       << " NodeInfo: " << trace::GetDebugInfo(cnode->debug_info());
   }
+  if (maybe_func->BuildType()->type_id() == kObjectTypeUndeterminedType) {
+    MS_LOG(DEBUG) << "EvalCNode eval Undetermined";
+    return std::make_shared<EvalResult>(maybe_func->Clone(), std::make_shared<AttrValueMap>());
+  }
   AbstractFunctionPtr func = dyn_cast<AbstractFunction>(maybe_func);
   if (func == nullptr) {
     MS_LOG(EXCEPTION) << "func_conf.GetEvaluatedValue() return not AbstractFunction: " << maybe_func->ToString()

mindspore/ccsrc/pipeline/validator.cc

@@ -32,6 +32,7 @@ using mindspore::abstract::AbstractBase;
 using mindspore::abstract::AbstractClass;
 using mindspore::abstract::AbstractError;
 using mindspore::abstract::AbstractFunction;
+using mindspore::abstract::AbstractIndexedSlices;
 using mindspore::abstract::AbstractJTagged;
 using mindspore::abstract::AbstractList;
 using mindspore::abstract::AbstractScalar;
@@ -93,7 +94,8 @@ void ValidateAbstract(const AnfNodePtr &node) {
   }
 
   if (ptrBase->isa<AbstractType>() || ptrBase->isa<AbstractFunction>() || ptrBase->isa<AbstractTuple>() ||
-      ptrBase->isa<AbstractList>() || ptrBase->isa<AbstractTensor>() || ptrBase->isa<abstract::AbstractRefKey>()) {
+      ptrBase->isa<AbstractList>() || ptrBase->isa<AbstractTensor>() || ptrBase->isa<AbstractIndexedSlices>() ||
+      ptrBase->isa<abstract::AbstractRefKey>()) {
     return;
   }
 

mindspore/ccsrc/utils/context/ms_context.cc

@@ -89,6 +89,7 @@ MsContext::MsContext(const std::string &policy, const std::string &target) {
   max_device_memory_ = kDefaultMaxDeviceMemory;
   print_file_path_ = "";
   enable_graph_kernel_ = false;
+  enable_sparse_flag_ = false;
 }
 
 std::shared_ptr<MsContext> MsContext::GetInstance() {

mindspore/ccsrc/utils/context/ms_context.h

@@ -161,6 +161,9 @@ class MsContext {
   void set_enable_graph_kernel(bool enable_graph_kernel) { enable_graph_kernel_ = enable_graph_kernel; }
   bool enable_graph_kernel() const { return enable_graph_kernel_; }
 
+  bool enable_sparse_flag() const { return enable_sparse_flag_; }
+  void set_enable_sparse_flag(bool enable_sparse_flag) { enable_sparse_flag_ = enable_sparse_flag; }
+
  private:
   MsContext(const std::string &backend_policy, const std::string &target);
   void GetGeOptions(std::map<std::string, std::string> *ge_options) const;
@@ -204,6 +207,7 @@ class MsContext {
   float max_device_memory_;
   std::string print_file_path_;
   bool enable_graph_kernel_;
+  bool enable_sparse_flag_;
 };
 
 }  // namespace mindspore

mindspore/common/__init__.py

@@ -17,10 +17,10 @@ from . import dtype
 from .api import ms_function
 from .dtype import *
 from .parameter import Parameter, ParameterTuple
-from .tensor import MetaTensor, Tensor
+from .tensor import MetaTensor, Tensor, IndexedSlices
 
 __all__ = [
-    "MetaTensor", "Tensor",  # tensor
+    "MetaTensor", "Tensor", "IndexedSlices",  # tensor
     'ms_function',  # api
     'Parameter', 'ParameterTuple',  # parameter
     "dtype"

mindspore/common/parameter.py

@@ -52,13 +52,16 @@ class Parameter:
         layerwise_parallel (bool): A kind of model parallel mode. When layerwise_parallel is true in paralle mode,
             broadcast and gradients communication would not be applied on parameters. Default: False.
         sparse_grad (str): Set if the parameter's gradient is sparse. Default: empty.
+        has_indexed_slices (bool): Set if the parameter's gradient is indexed_slices. Default: false.
     """
-    def __init__(self, default_input, name, requires_grad=True, layerwise_parallel=False, sparse_grad=""):
+    def __init__(self, default_input, name, requires_grad=True, layerwise_parallel=False,
+                 sparse_grad="", has_indexed_slices_grad=False):
         self.set_parameter_data(default_input)
         self.name = name
         self.requires_grad = requires_grad
         self.layerwise_parallel = layerwise_parallel
         self.sparse_grad = sparse_grad
+        self.has_indexed_slices_grad = has_indexed_slices_grad
         self._is_init = False
         self._sliced = False
         self.clone_info = _CloneInfo()
@@ -186,6 +189,17 @@ class Parameter:
             raise TypeError("`sparse_grad` parameter must be str type")
         self._sparse_grad = value
 
+    @property
+    def has_indexed_slices_grad(self):
+        """Return whether the parameter's gradient is indexed_slices."""
+        return self._has_indexed_slices_grad
+
+    @has_indexed_slices_grad.setter
+    def has_indexed_slices_grad(self, value=False):
+        if not isinstance(value, bool):
+            raise TypeError("`has_indexed_slices_grad` parameter must be bool type")
+        self._has_indexed_slices_grad = value
+
     @property
     def data(self):
         return self.default_input

mindspore/common/tensor.py

@@ -21,7 +21,7 @@ from .._checkparam import check_type, check_typename
 from . import dtype as mstype
 from ._register_for_tensor import tensor_operator_registry
 
-__all__ = ['Tensor', 'MetaTensor']
+__all__ = ['Tensor', 'MetaTensor', 'IndexedSlices']
 np_types = (np.int8, np.int16, np.int32, np.int64,
             np.uint8, np.uint16, np.uint32, np.uint64, np.float16,
             np.float32, np.float64, np.bool_)
@@ -214,3 +214,8 @@ class Tensor(Tensor_):
             raise TypeError("init_flag must be bool.")
         self.set_init_flag(value)
         self._init_flag = value
+
+
+class IndexedSlices:
+    def __init__(self, indices, values, dense_shape):
+        raise NotImplementedError

mindspore/context.py

@@ -355,6 +355,14 @@ class _Context:
     def check_bprop(self, check_bprop_flag):
         self._context_handle.set_check_bprop_flag(check_bprop_flag)
 
+    @property
+    def enable_sparse(self):
+        return self._context_handle.get_enable_sparse_flag()
+
+    @enable_sparse.setter
+    def enable_sparse(self, enable_sparse_flag):
+        self._context_handle.set_enable_sparse_flag(enable_sparse_flag)
+
     @property
     def max_device_memory(self):
         return self._context_handle.get_max_device_memory()
@@ -510,7 +518,8 @@ def reset_auto_parallel_context():
                  save_graphs_path=str, save_ms_model=bool, save_ms_model_path=str, enable_dump=bool,
                  save_dump_path=str, enable_reduce_precision=bool, variable_memory_max_size=str,
                  enable_profiling=bool, profiling_options=str, enable_auto_mixed_precision=bool,
-                 enable_graph_kernel=bool, check_bprop=bool, max_device_memory=str, print_file_path=str)
+                 enable_graph_kernel=bool, check_bprop=bool, max_device_memory=str, print_file_path=str,
+                 enable_sparse=bool)
 def set_context(**kwargs):
     """
     Sets context for running environment.
@@ -567,6 +576,7 @@ def set_context(**kwargs):
             The format is "xxGB". Default: "1024GB".
         print_file_path (str): The path of print data to save. If this parameter is set, print data is saved to
             a file by default, and turn off printing to the screen.
+        enable_sparse (bool): Whether to enable sparse feature. Default: False.
 
     Raises:
         ValueError: If input key is not an attribute in context.

mindspore/ops/functional.py

@@ -153,6 +153,14 @@ shape_mul = Primitive("shape_mul")
 # a primitive to compare between tuple.
 stop_gradient = Primitive("stop_gradient")
 
+
+make_indexed_slices = Primitive('MakeIndexedSlices')
+indexed_slices_get_values = Primitive('IndexedSlicesGetValues')
+indexed_slices_get_indices = Primitive('IndexedSlicesGetIndices')
+indexed_slices_get_dense_shape = Primitive('IndexedSlicesGetDenseShape')
+is_indexed_slices = Primitive('IsIndexedSlices')
+
+
 tensor_operator_registry.register('__add__', tensor_add)
 tensor_operator_registry.register('__sub__', tensor_sub)
 tensor_operator_registry.register('__mul__', tensor_mul)

mindspore/ops/operations/array_ops.py

@@ -564,7 +564,7 @@ class SparseGatherV2(GatherV2):
         >>> input_params = Tensor(np.array([[1, 2, 7, 42], [3, 4, 54, 22], [2, 2, 55, 3]]), mindspore.float32)
         >>> input_indices = Tensor(np.array([1, 2]), mindspore.int32)
         >>> axis = 1
-        >>> out = P.GatherV2()(input_params, input_indices, axis)
+        >>> out = P.SparseGatherV2()(input_params, input_indices, axis)
     """
 
 

tests/ut/cpp/optimizer/lib_test.cc

@@ -603,5 +603,18 @@ TEST_F(TestOptLib, test_adjust_allreduce_mul_add) {
   ASSERT_TRUE(CheckOpt(before2l, after2, patterns));
   ASSERT_TRUE(CheckOpt(before2r, after2, patterns));
 }
+
+TEST_F(TestOptLib, test_indexed_slices) {
+  FuncGraphPtr before_get_indices = getPyFun.CallAndParseRet("test_indexed_slices", "before_get_indices");
+  FuncGraphPtr after_get_indices = getPyFun.CallAndParseRet("test_indexed_slices", "after_get_indices");
+  FuncGraphPtr before_get_values = getPyFun.CallAndParseRet("test_indexed_slices", "before_get_values");
+  FuncGraphPtr after_get_values = getPyFun.CallAndParseRet("test_indexed_slices", "after_get_values");
+  FuncGraphPtr before_get_dense_shape = getPyFun.CallAndParseRet("test_indexed_slices", "before_get_dense_shape");
+  FuncGraphPtr after_get_dense_shape = getPyFun.CallAndParseRet("test_indexed_slices", "after_get_dense_shape");
+  auto patterns = std::vector<SubstitutionPtr>({irpass.indexed_slices_eliminate_});
+  ASSERT_TRUE(CheckOpt(before_get_indices, after_get_indices, patterns));
+  ASSERT_TRUE(CheckOpt(before_get_values, after_get_values, patterns));
+  ASSERT_TRUE(CheckOpt(before_get_dense_shape, after_get_dense_shape, patterns));
+}
 }  // namespace opt
 }  // namespace mindspore

tests/ut/cpp/python_input/gtest_input/optimizer/opt_test.py

@@ -1130,3 +1130,38 @@ def test_adjust_allreduce_mul_add(tag):
         return Mul(AllReduce(AddN((Mul(z, z), x))), y)
 
     return fns[tag]
+
+
+def test_indexed_slices(tag):
+    """ test_add_zero """
+    fns = FnDict()
+    make_indexed_slices = Primitive('MakeIndexedSlices')
+    indexed_slices_get_values = Primitive('IndexedSlicesGetValues')
+    indexed_slices_get_indices = Primitive('IndexedSlicesGetIndices')
+    indexed_slices_get_dense_shape = Primitive('IndexedSlicesGetDenseShape')
+
+    @fns
+    def before_get_indices(x, y, z):
+        return indexed_slices_get_indices(make_indexed_slices(x, y, z))
+
+    @fns
+    def after_get_indices(x, y, z):
+        return x
+
+    @fns
+    def before_get_values(x, y, z):
+        return indexed_slices_get_values(make_indexed_slices(x, y, z))
+
+    @fns
+    def after_get_values(x, y, z):
+        return y
+
+    @fns
+    def before_get_dense_shape(x, y, z):
+        return indexed_slices_get_dense_shape(make_indexed_slices(x, y, z))
+
+    @fns
+    def after_get_dense_shape(x, y, z):
+        return z
+
+    return fns[tag]

tests/ut/python/ir/test_indexed_slices.py

@@ -0,0 +1,290 @@
+# 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.
+# ============================================================================
+"""
+@File  : test_indexed_slices.py
+@Author:
+@Date  : 2020-06-08
+@Desc  : test mindspore indexed_slices's operation
+"""
+import numpy as np
+
+import mindspore as ms
+import mindspore.nn as nn
+from mindspore.ops import composite as C
+from mindspore.ops import functional as F
+from mindspore.ops import operations as P
+from mindspore.ops.composite.multitype_ops.zeros_like_impl import zeros_like
+from mindspore.ops.primitive import constexpr
+from mindspore.ops._grad.grad_base import bprop_getters
+from mindspore import Tensor, IndexedSlices, context
+from mindspore.common.parameter import Parameter, ParameterTuple
+from mindspore.common import dtype as mstype
+from mindspore._checkparam import Validator as validator
+from mindspore._checkparam import Rel
+from mindspore.nn import Optimizer
+from mindspore.nn import TrainOneStepCell, WithLossCell
+
+reduce_sum = P.ReduceSum()
+unsorted_segment_sum = P.UnsortedSegmentSum()
+transpose = P.Transpose()
+shape_op = P.Shape()
+reshape = P.Reshape()
+size_op = P.Size()
+invert_permutation = P.InvertPermutation()
+logical_and = P.LogicalAnd()
+context.set_context(mode=context.GRAPH_MODE, enable_sparse=True)
+
+@constexpr
+def _generate_shape_index(out_shape, indices_shape, axis):
+    out_rank = len(out_shape)
+    ind_rank = len(indices_shape)
+    if axis < 0:
+        axis += out_rank - ind_rank + 1
+    perm_part1 = tuple(range(axis, axis + ind_rank))
+    index = tuple(range(out_rank))
+    perm = perm_part1 + index[:axis] + index[axis + ind_rank:]
+    return perm
+
+@constexpr
+def _generate_inverse_index(x_shape, axis):
+    x_rank = len(x_shape)
+    index = tuple(range(x_rank))
+    if axis < 0:
+        axis += x_rank
+    perm = index[1:1 + axis] + (0,) + index[1 + axis:]
+    return perm
+
+class MySparseGatherV2(P.GatherV2):
+    """
+    For test
+    """
+
+@bprop_getters.register(MySparseGatherV2)
+def get_bprop_sparse_gather_v2(self):
+    """Generate bprop for MySparseGatherV2"""
+
+    def bprop(x, indices, axis, out, dout):
+        x_shp = shape_op(x)
+        if axis == 0:
+            indices_size = (size_op(indices),)
+            x_tail_shp = x_shp[1:]
+            values_shape = indices_size + x_tail_shp
+            values = reshape(dout, values_shape)
+            indices = reshape(indices, indices_size)
+            return IndexedSlices(indices, values, x_shp), zeros_like(indices), zeros_like(axis)
+        if F.rank(dout) == 0:
+            dout = P.ExpandDims()(dout, -1)
+        if F.rank(indices) == 0:
+            indices = P.ExpandDims()(indices, -1)
+        out_shp = shape_op(dout)
+        ind_shp = shape_op(indices)
+        # Example: out_shape:(3,2,3) axis 1 -> (1,0,2)
+        perm_1 = _generate_shape_index(out_shp, ind_shp, axis)
+        values_transpose = transpose(dout, perm_1)
+        params_grad = unsorted_segment_sum(values_transpose, indices, shape_op(x)[axis])
+        # Example: out_shape:(3,2,3) axis 2 -> (1,2,0)
+        perm_2 = _generate_inverse_index(x_shp, axis)
+        params_grad = transpose(params_grad, perm_2)
+        return params_grad, zeros_like(indices), zeros_like(axis)
+
+    return bprop
+
+adam_opt_for_map = C.MultitypeFuncGraph("adam_opt_for_map")
+@adam_opt_for_map.register("Tensor", "Tensor", "Tensor", "Tensor", "Tensor",
+                           "Tensor", "Tensor", "Tensor", "Undetermined", "Bool")
+def _update_run_op_for_map(beta1, beta2, eps, lr, weight_decay_tensor, param, m, v, gradient, decay_flag):
+    if gradient.is_indexed_slices():
+        return gradient.values()
+    op_mul = P.Mul()
+    op_square = P.Square()
+    op_sqrt = P.Sqrt()
+    op_cast = P.Cast()
+    op_reshape = P.Reshape()
+    op_shape = P.Shape()
+
+    param_fp32 = op_cast(param, mstype.float32)
+    m_fp32 = op_cast(m, mstype.float32)
+    v_fp32 = op_cast(v, mstype.float32)
+    gradient_fp32 = op_cast(gradient, mstype.float32)
+
+    next_m = op_mul(beta1, m_fp32) + op_mul(op_cast(F.tuple_to_array((1.0,)), mstype.float32) - beta1, gradient_fp32)
+
+    next_v = op_mul(beta2, v_fp32) + op_mul(op_cast(F.tuple_to_array((1.0,)), mstype.float32)
+                                            - beta2, op_square(gradient_fp32))
+
+    update = next_m / (op_sqrt(next_v) + eps)
+    if decay_flag:
+        update = update + op_mul(weight_decay_tensor, param_fp32)
+
+    update_with_lr = op_mul(lr, update)
+    next_param = param_fp32 - op_reshape(update_with_lr, op_shape(param_fp32))
+
+    next_v = F.depend(next_v, F.assign(param, next_param))
+    next_v = F.depend(next_v, F.assign(m, next_m))
+    next_v = F.depend(next_v, F.assign(v, next_v))
+    return next_v
+
+
+def _check_param_value(beta1, beta2, eps, weight_decay, prim_name):
+    """Check the type of inputs."""
+    validator.check_value_type("beta1", beta1, [float], prim_name)
+    validator.check_value_type("beta2", beta2, [float], prim_name)
+    validator.check_value_type("eps", eps, [float], prim_name)
+    validator.check_value_type("weight_dacay", weight_decay, [float], prim_name)
+    validator.check_number_range("beta1", beta1, 0.0, 1.0, Rel.INC_NEITHER, prim_name)
+    validator.check_number_range("beta2", beta2, 0.0, 1.0, Rel.INC_NEITHER, prim_name)
+    validator.check_number_range("eps", eps, 0.0, float("inf"), Rel.INC_NEITHER, prim_name)
+    validator.check_number_range("weight_decay", weight_decay, 0.0, float("inf"), Rel.INC_LEFT, prim_name)
+
+
+class AdamWeightDecaySparse(Optimizer):
+    def __init__(self, params, learning_rate=1e-3, beta1=0.9, beta2=0.999, eps=1e-6, weight_decay=0.0,
+                 decay_filter=lambda x: 'beta' not in x.name and 'gamma' not in x.name):
+        super(AdamWeightDecaySparse, self).__init__(learning_rate, params)
+        if self.is_group:
+            raise RuntimeError(f"The {self.cls_name} optimizer cannot support group setting.")
+        _check_param_value(beta1, beta2, eps, weight_decay, self.cls_name)
+        self.beta1 = Tensor(np.array([beta1]).astype(np.float32))
+        self.beta2 = Tensor(np.array([beta2]).astype(np.float32))
+        self.eps = Tensor(np.array([eps]).astype(np.float32))
+        self.weight_decay_tensor = Tensor(np.array([weight_decay]).astype(np.float32))
+
+        self.params = self.parameters
+        self.moments1 = self.params.clone(prefix="adam_m", init='zeros')
+        self.moments2 = self.params.clone(prefix="adam_v", init='zeros')
+        self.decay_flag = tuple(decay_filter(x) for x in self.params)
+        self.map = C.Map()
+
+    def construct(self, gradients):
+        lr = self.get_lr()
+        updated_velocity = self.map(F.partial(adam_opt_for_map, self.beta1, self.beta2, self.eps, lr,
+                                              self.weight_decay_tensor),
+                                    self.params, self.moments1, self.moments2, gradients, self.decay_flag)
+        return updated_velocity
+
+
+def test_indexed_slices_make_indexed_slices():
+    class MakeIndexedSlices(nn.Cell):
+        def __init__(self):
+            super(MakeIndexedSlices, self).__init__()
+            self.dense_shape = (3, 4)
+        def construct(self, indices, values):
+            ret = (IndexedSlices(indices, values, self.dense_shape),)
+            return ret[0].is_indexed_slices()
+    indices = Tensor([[0, 0], [1, 2]])
+    values = Tensor([1, 2], dtype=ms.float32)
+    MakeIndexedSlices()(indices, values)
+
+
+def test_indexed_slices_attr():
+    class IndexedSlicesGetAttr(nn.Cell):
+        def __init__(self):
+            super(IndexedSlicesGetAttr, self).__init__()
+            self.dense_shape = (3, 4)
+        def construct(self, indices, values):
+            x = IndexedSlices(indices, values, self.dense_shape)
+            return x.values(), x.indices(), x.dense_shape()
+    indices = Tensor([[0, 0], [1, 2]])
+    values = Tensor([1, 2], dtype=ms.float32)
+    IndexedSlicesGetAttr()(indices, values)
+
+
+def test_indexed_slices_sparse_gatherv2_grad_all():
+    grad_all = C.GradOperation('get_all', get_all=True)
+    class GradWrap(nn.Cell):
+        def __init__(self, network):
+            super(GradWrap, self).__init__()
+            self.network = network
+        def construct(self, x, y):
+            grad = grad_all(self.network)(x, y)
+            return grad, grad[0].is_indexed_slices(), grad[1].is_indexed_slices()
+    class SparseGatherV2(nn.Cell):
+        def __init__(self):
+            super(SparseGatherV2, self).__init__()
+            self.sparse_gatherv2 = MySparseGatherV2()
+            self.axis = 0
+        def construct(self, params, indices):
+            return self.sparse_gatherv2(params, indices, self.axis)
+    params = Tensor(np.ones([3, 1, 2]).astype(np.int32))
+    indices = Tensor(np.array([0, 1]).astype(np.int32))
+    GradWrap(SparseGatherV2())(params, indices)
+
+
+def test_indexed_slices_sparse_gatherv2_grad_with_pram():
+    grad_by_list = C.GradOperation('get_by_list', get_by_list=True)
+    class GradWrap(nn.Cell):
+        def __init__(self, network):
+            super(GradWrap, self).__init__()
+            self.network = network
+            self.weights = ParameterTuple(filter(lambda x: x.requires_grad, network.get_parameters()))
+        def construct(self, x):
+            weights = self.weights
+            grad = grad_by_list(self.network, weights)(x)
+            x = grad[0]
+            return x.is_indexed_slices(), x.values(), x.indices(), x.dense_shape()
+    class SparseGatherV2(nn.Cell):
+        def __init__(self):
+            super(SparseGatherV2, self).__init__()
+            self.sparse_gatherv2 = MySparseGatherV2()
+            self.axis = 0
+            self.params = Parameter(Tensor(np.ones([3, 1, 2]).astype(np.int32)),
+                                    name="params", has_indexed_slices_grad=True)
+        def construct(self, indices):
+            return self.sparse_gatherv2(self.params, indices, self.axis)
+    indices = Tensor(np.array([0, 1]).astype(np.int32))
+    network = GradWrap(SparseGatherV2())
+    network(indices)
+
+
+def test_indexed_slices_is_indexed_slices():
+    class MakeIndexedSlices(nn.Cell):
+        def __init__(self):
+            super(MakeIndexedSlices, self).__init__()
+            self.dense_shape = (3, 4)
+        def construct(self, indices, values):
+            indexed_slices = IndexedSlices(indices, values, self.dense_shape)
+            ret = indexed_slices.is_indexed_slices()
+            return ret
+    indices = Tensor([[0, 0], [1, 2]])
+    values = Tensor([1, 2], dtype=ms.float32)
+    MakeIndexedSlices()(indices, values)
+
+
+def test_indexed_slices_env_get():
+    class Loss(nn.Cell):
+        def __init__(self):
+            super(Loss, self).__init__()
+        def construct(self, base, target):
+            return base
+    class NetWithSparseGatherV2(nn.Cell):
+        def __init__(self):
+            super(NetWithSparseGatherV2, self).__init__()
+            self.w1 = Parameter(Tensor(np.ones([3, 1, 2]).astype(np.float32)), name="w1", has_indexed_slices_grad=True)
+            self.w2 = Parameter(Tensor(np.ones([2, 1, 2]).astype(np.float32)), name="w2")
+            self.gatherv2 = MySparseGatherV2()
+            self.axis = 0
+        def construct(self, indices):
+            return self.gatherv2(self.w1, indices, self.axis) * self.w2
+
+    inputs = Tensor(np.array([0, 1]).astype(np.int32))
+    label = Tensor(np.zeros([2, 1, 2]).astype(np.float32))
+    net = NetWithSparseGatherV2()
+    net.set_train()
+    loss = Loss()
+    optimizer = AdamWeightDecaySparse(net.trainable_params())
+
+    net_with_loss = WithLossCell(net, loss)
+    train_network = TrainOneStepCell(net_with_loss, optimizer)
+    train_network(inputs, label)

tests/ut/python/nn/optim/test_adam_with_tuple_grad.py

@@ -155,7 +155,7 @@ def test_AdamWeightDecaySparse():
         def __init__(self):
             super(NetWithSparseGatherV2, self).__init__()
             self.w1 = Parameter(Tensor(np.ones([3, 1, 2]).astype(np.float32)), name="w1", sparse_grad="sparse_key_w1")
-            self.w2 = Parameter(Tensor(np.ones([2, 1, 2]).astype(np.float32)), name="w2", sparse_grad="sparse_key_w2")
+            self.w2 = Parameter(Tensor(np.ones([2, 1, 2]).astype(np.float32)), name="w2")
             self.gatherv2 = P.SparseGatherV2()
             self.axis = 0
         def construct(self, indices):