!4031 fix cell bprop

Merge pull request !4031 from riemann_penn/fix_cell_bprop

mindspore/_extends/parse/resources.py

@@ -17,7 +17,7 @@
 """Resources for ast tree parse."""
 import ast
 import math
-from mindspore import IndexedSlices, SparseTensor
+from mindspore import RowTensor, SparseTensor
 from mindspore.ops.composite import multitype_ops
 from mindspore.ops import functional as F, composite as C
 from . import standard_method as M
@@ -140,6 +140,6 @@ convert_object_map = {
     math.tan:       NO_IMPLEMENT,
 
     # user defined
-    IndexedSlices:  F.make_indexed_slices,
+    RowTensor:  F.make_row_tensor,
     SparseTensor:   F.make_sparse_tensor,
 }

mindspore/ccsrc/debug/dump_proto.cc

@@ -120,7 +120,7 @@ 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>()) {
+  } else if (type->isa<RowTensorType>()) {
     // Do Nothing
   } else if (type->isa<UndeterminedType>()) {
     // Do Nothing

mindspore/ccsrc/frontend/operator/ops.h

@@ -174,12 +174,11 @@ inline const PrimitivePtr kPrimVirtualDiv = std::make_shared<Primitive>("_Virtua
 inline const PrimitivePtr kPrimVirtualDataset = std::make_shared<Primitive>("_VirtualDataset");
 inline const PrimitivePtr kPrimAllReduce = std::make_shared<Primitive>("AllReduce");
 
-// IndexedSlices
-inline const PrimitivePtr kPrimMakeIndexedSlices = std::make_shared<Primitive>("MakeIndexedSlices");
-inline const PrimitivePtr kPrimIndexedSlicesGetValues = std::make_shared<Primitive>("IndexedSlicesGetValues");
-inline const PrimitivePtr kPrimIndexedSlicesGetIndices = std::make_shared<Primitive>("IndexedSlicesGetIndices");
-inline const PrimitivePtr kPrimIndexedSlicesGetDenseShape = std::make_shared<Primitive>("IndexedSlicesGetDenseShape");
-inline const PrimitivePtr kPrimIsIndexedSlices = std::make_shared<Primitive>("IsIndexedSlices");
+// RowTensor
+inline const PrimitivePtr kPrimMakeRowTensor = std::make_shared<Primitive>("MakeRowTensor");
+inline const PrimitivePtr kPrimRowTensorGetValues = std::make_shared<Primitive>("RowTensorGetValues");
+inline const PrimitivePtr kPrimRowTensorGetIndices = std::make_shared<Primitive>("RowTensorGetIndices");
+inline const PrimitivePtr kPrimRowTensorGetDenseShape = std::make_shared<Primitive>("RowTensorGetDenseShape");
 
 // SparseTensor
 inline const PrimitivePtr kPrimMakeSparseTensor = std::make_shared<Primitive>("MakeSparseTensor");

mindspore/ccsrc/frontend/operator/prim_others.cc

@@ -340,8 +340,8 @@ 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) {
+AbstractBasePtr InferImplMakeRowTensor(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);
@@ -393,41 +393,41 @@ AbstractBasePtr InferImplMakeIndexedSlices(const AnalysisEnginePtr &, const Prim
                               << "th dimension of values " << values_shp[i] << ", but got " << dense_shape_vec[i];
     }
   }
-  auto ret = std::make_shared<AbstractIndexedSlices>(values->element()->BuildType(), dense_shape_vec);
+  auto ret = std::make_shared<AbstractRowTensor>(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) {
+AbstractBasePtr InferImplRowTensorGetValues(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();
+  auto row_tensor = CheckArg<AbstractRowTensor>(op_name, args_spec_list, 0);
+  MS_EXCEPTION_IF_NULL(row_tensor->values());
+  return row_tensor->values();
 }
 
-AbstractBasePtr InferImplIndexedSlicesGetIndices(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
-                                                 const AbstractBasePtrList &args_spec_list) {
+AbstractBasePtr InferImplRowTensorGetIndices(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();
+  auto row_tensor = CheckArg<AbstractRowTensor>(op_name, args_spec_list, 0);
+  MS_EXCEPTION_IF_NULL(row_tensor->indices());
+  return row_tensor->indices();
 }
 
-AbstractBasePtr InferImplIndexedSlicesGetDenseShape(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
-                                                    const AbstractBasePtrList &args_spec_list) {
+AbstractBasePtr InferImplRowTensorGetDenseShape(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();
+  auto row_tensor = CheckArg<AbstractRowTensor>(op_name, args_spec_list, 0);
+  MS_EXCEPTION_IF_NULL(row_tensor->dense_shape());
+  return row_tensor->dense_shape();
 }
 
 AbstractBasePtr InferImplMakeSparseTensor(const AnalysisEnginePtr &, const PrimitivePtr &primitive,

mindspore/ccsrc/frontend/optimizer/clean.cc

@@ -32,9 +32,9 @@ namespace opt {
 using mindspore::abstract::AbstractAttribute;
 using mindspore::abstract::AbstractClass;
 using mindspore::abstract::AbstractDictionary;
-using mindspore::abstract::AbstractIndexedSlices;
 using mindspore::abstract::AbstractJTagged;
 using mindspore::abstract::AbstractList;
+using mindspore::abstract::AbstractRowTensor;
 using mindspore::abstract::AbstractScalar;
 using mindspore::abstract::AbstractSparseTensor;
 using mindspore::abstract::AbstractTuple;
@@ -81,10 +81,10 @@ static AbstractBasePtr AdaptAbs(const AbstractBasePtr &t) {
     return std::make_shared<AbstractTuple>(abstract_list);
   }
 
-  if (t->isa<AbstractIndexedSlices>()) {
-    auto abs_indexed_slices = dyn_cast<AbstractIndexedSlices>(t);
-    std::vector<AbstractBasePtr> abstract_list{abs_indexed_slices->indices(), abs_indexed_slices->values(),
-                                               abs_indexed_slices->dense_shape()};
+  if (t->isa<AbstractRowTensor>()) {
+    auto abs_row_tensor = dyn_cast<AbstractRowTensor>(t);
+    std::vector<AbstractBasePtr> abstract_list{abs_row_tensor->indices(), abs_row_tensor->values(),
+                                               abs_row_tensor->dense_shape()};
     return std::make_shared<AbstractTuple>(abstract_list);
   }
 
@@ -455,16 +455,16 @@ bool CleanAfterOptA(const FuncGraphPtr &root, const FuncGraphManagerPtr &manager
     } else if (IsValueNode<ValueList>(node)) {
       new_node = ConvertValueListNodeToValueTupleNode(node->cast<ValueNodePtr>());
     } else if (IsPrimitiveCNode(node, prim::kPrimMakeSparseTensor) ||
-               IsPrimitiveCNode(node, prim::kPrimMakeIndexedSlices)) {
+               IsPrimitiveCNode(node, prim::kPrimMakeRowTensor)) {
       new_node = ConvertMakeSparseToMakeTuple(cnode);
     } else if (IsPrimitiveCNode(node, prim::kPrimSparseTensorGetIndices) ||
-               IsPrimitiveCNode(node, prim::kPrimIndexedSlicesGetIndices)) {
+               IsPrimitiveCNode(node, prim::kPrimRowTensorGetIndices)) {
       new_node = ConvertSparseGetAttrToTupleGetItem(cnode, 0);
     } else if (IsPrimitiveCNode(node, prim::kPrimSparseTensorGetValues) ||
-               IsPrimitiveCNode(node, prim::kPrimIndexedSlicesGetValues)) {
+               IsPrimitiveCNode(node, prim::kPrimRowTensorGetValues)) {
       new_node = ConvertSparseGetAttrToTupleGetItem(cnode, 1);
     } else if (IsPrimitiveCNode(node, prim::kPrimSparseTensorGetDenseShape) ||
-               IsPrimitiveCNode(node, prim::kPrimIndexedSlicesGetDenseShape)) {
+               IsPrimitiveCNode(node, prim::kPrimRowTensorGetDenseShape)) {
       new_node = ConvertSparseGetAttrToTupleGetItem(cnode, 2);
     }
 

mindspore/ccsrc/frontend/optimizer/irpass.cc

@@ -43,7 +43,7 @@
 #include "frontend/optimizer/irpass/transpose_eliminate.h"
 #include "frontend/optimizer/irpass/value_based_eliminate.h"
 #include "frontend/optimizer/opt.h"
-#include "frontend/optimizer/irpass/indexed_slices_eliminate.h"
+#include "frontend/optimizer/irpass/row_tensor_eliminate.h"
 #include "frontend/optimizer/irpass/sparse_tensor_eliminate.h"
 
 namespace mindspore {
@@ -157,10 +157,10 @@ OptimizeIRPassLib::OptimizeIRPassLib() {
   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});
+  // RowTensor Eliminate
+  row_tensor_eliminate_ = MakeSubstitution(
+    std::make_shared<RowTensorEliminater>(), "row_tensor_eliminate",
+    {prim::kPrimRowTensorGetIndices, prim::kPrimRowTensorGetValues, prim::kPrimRowTensorGetDenseShape});
 
   // SparseTensor Eliminate
   sparse_tensor_eliminate_ = MakeSubstitution(

mindspore/ccsrc/frontend/optimizer/irpass.h

@@ -105,8 +105,8 @@ class OptimizeIRPassLib {
   // Fusion
   SubstitutionPtr mark_interface_fusion_;
 
-  // IndexedSlices Eliminate
-  SubstitutionPtr indexed_slices_eliminate_;
+  // RowTensor Eliminate
+  SubstitutionPtr row_tensor_eliminate_;
 
   // SparseTensor Eliminate
   SubstitutionPtr sparse_tensor_eliminate_;

mindspore/ccsrc/frontend/optimizer/irpass/indexed_slices_eliminate.h → mindspore/ccsrc/frontend/optimizer/irpass/row_tensor_eliminate.h

@@ -14,8 +14,8 @@
  * limitations under the License.
  */
 
-#ifndef MINDSPORE_CCSRC_FRONTEND_OPTIMIZER_IRPASS_INDEXED_SLICES_ELIMINATE_H_
-#define MINDSPORE_CCSRC_FRONTEND_OPTIMIZER_IRPASS_INDEXED_SLICES_ELIMINATE_H_
+#ifndef MINDSPORE_CCSRC_FRONTEND_OPTIMIZER_IRPASS_ROW_TENSOR_ELIMINATE_H_
+#define MINDSPORE_CCSRC_FRONTEND_OPTIMIZER_IRPASS_ROW_TENSOR_ELIMINATE_H_
 
 #include <vector>
 #include <algorithm>
@@ -28,24 +28,24 @@
 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 {
+// {prim::kPrimRowTensorGetIndices, {prim::kPrimMakeRowTensor, Xs}}
+// {prim::kPrimRowTensorGetValues, {prim::kPrimMakeRowTensor, Xs}}
+// {prim::kPrimRowTensorGetDenseShape, {prim::kPrimMakeRowTensor, Xs}}
+class RowTensorEliminater : public AnfVisitor {
  public:
   AnfNodePtr operator()(const OptimizerPtr &, const AnfNodePtr &node) override {
     Reset();
-    AnfVisitor::Match(prim::kPrimIndexedSlicesGetIndices, {IsCNode})(node);
+    AnfVisitor::Match(prim::kPrimRowTensorGetIndices, {IsCNode})(node);
 
     if (is_match_) {
       return tuple_->input(1);
     }
-    AnfVisitor::Match(prim::kPrimIndexedSlicesGetValues, {IsCNode})(node);
+    AnfVisitor::Match(prim::kPrimRowTensorGetValues, {IsCNode})(node);
 
     if (is_match_) {
       return tuple_->input(2);
     }
-    AnfVisitor::Match(prim::kPrimIndexedSlicesGetDenseShape, {IsCNode})(node);
+    AnfVisitor::Match(prim::kPrimRowTensorGetDenseShape, {IsCNode})(node);
 
     if (is_match_) {
       return tuple_->input(3);
@@ -54,7 +54,7 @@ class IndexedSlicesEliminater : public AnfVisitor {
   }
 
   void Visit(const CNodePtr &cnode) override {
-    if (IsPrimitiveCNode(cnode, prim::kPrimMakeIndexedSlices)) {
+    if (IsPrimitiveCNode(cnode, prim::kPrimMakeRowTensor)) {
       tuple_ = cnode;
       is_match_ = true;
     }
@@ -72,4 +72,4 @@ class IndexedSlicesEliminater : public AnfVisitor {
 }  // namespace irpass
 }  // namespace opt
 }  // namespace mindspore
-#endif  // MINDSPORE_CCSRC_FRONTEND_OPTIMIZER_IRPASS_INDEXED_SLICES_ELIMINATE_H_
+#endif  // MINDSPORE_CCSRC_FRONTEND_OPTIMIZER_IRPASS_ROW_TENSOR_ELIMINATE_H_

mindspore/ccsrc/pipeline/jit/pass.cc

@@ -170,7 +170,7 @@ 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_,
+    irpass.row_tensor_eliminate_,
   });
   OptPassGroupMap map({
     {"b_1", b_1},

mindspore/ccsrc/pipeline/jit/static_analysis/prim.cc

@@ -132,11 +132,11 @@ 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}},
+    // RowTensor
+    {prim::kPrimMakeRowTensor, {InferImplMakeRowTensor, true}},
+    {prim::kPrimRowTensorGetValues, {InferImplRowTensorGetValues, true}},
+    {prim::kPrimRowTensorGetIndices, {InferImplRowTensorGetIndices, true}},
+    {prim::kPrimRowTensorGetDenseShape, {InferImplRowTensorGetDenseShape, true}},
     // SparseTensor
     {prim::kPrimMakeSparseTensor, {InferImplMakeSparseTensor, true}},
     {prim::kPrimSparseTensorGetValues, {InferImplSparseTensorGetValues, true}},
@@ -402,8 +402,8 @@ py::dict ConvertAbstractToPython(const AbstractBasePtr &abs_base) {
     }
     dic["dtype"] = arg_tensor->BuildType();
     dic["value"] = BuildValue(arg_tensor->BuildValue());
-  } else if (abs_base->isa<AbstractIndexedSlices>()) {
-    auto arg = dyn_cast<AbstractIndexedSlices>(abs_base);
+  } else if (abs_base->isa<AbstractRowTensor>()) {
+    auto arg = dyn_cast<AbstractRowTensor>(abs_base);
     dic["shape"] = arg->shape()->shape();
     dic["dtype"] = arg->BuildType();
     dic["value"] = BuildValue(arg->BuildValue());

mindspore/ccsrc/pipeline/jit/static_analysis/prim.h

@@ -348,14 +348,14 @@ AbstractBasePtr InferImplControlDepend(const AnalysisEnginePtr &, const Primitiv
 AbstractBasePtr InferImplDebug(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
                                const AbstractBasePtrList &args_spec_list);
 
-AbstractBasePtr InferImplMakeIndexedSlices(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
-                                           const AbstractBasePtrList &args_spec_list);
-AbstractBasePtr InferImplIndexedSlicesGetValues(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
+AbstractBasePtr InferImplMakeRowTensor(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
+                                       const AbstractBasePtrList &args_spec_list);
+AbstractBasePtr InferImplRowTensorGetValues(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
+                                            const AbstractBasePtrList &args_spec_list);
+AbstractBasePtr InferImplRowTensorGetIndices(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
+                                             const AbstractBasePtrList &args_spec_list);
+AbstractBasePtr InferImplRowTensorGetDenseShape(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 InferImplMakeSparseTensor(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
                                           const AbstractBasePtrList &args_spec_list);
 AbstractBasePtr InferImplSparseTensorGetValues(const AnalysisEnginePtr &, const PrimitivePtr &primitive,

mindspore/ccsrc/pipeline/jit/validator.cc

@@ -32,9 +32,9 @@ 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::AbstractRowTensor;
 using mindspore::abstract::AbstractScalar;
 using mindspore::abstract::AbstractSparseTensor;
 using mindspore::abstract::AbstractTensor;
@@ -95,7 +95,7 @@ void ValidateAbstract(const AnfNodePtr &node) {
   }
 
   if (ptrBase->isa<AbstractType>() || ptrBase->isa<AbstractFunction>() || ptrBase->isa<AbstractTuple>() ||
-      ptrBase->isa<AbstractList>() || ptrBase->isa<AbstractTensor>() || ptrBase->isa<AbstractIndexedSlices>() ||
+      ptrBase->isa<AbstractList>() || ptrBase->isa<AbstractTensor>() || ptrBase->isa<AbstractRowTensor>() ||
       ptrBase->isa<AbstractSparseTensor>() || ptrBase->isa<abstract::AbstractRefKey>()) {
     return;
   }

mindspore/ccsrc/utils/dtype_py.cc

@@ -136,8 +136,7 @@ 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_<RowTensorType, Type, std::shared_ptr<RowTensorType>>(m_sub, "RowTensorType").def(py::init());
     (void)py::class_<SparseTensorType, Type, std::shared_ptr<SparseTensorType>>(m_sub, "SparseTensorType")
       .def(py::init());
     (void)py::class_<UndeterminedType, Type, std::shared_ptr<UndeterminedType>>(m_sub, "UndeterminedType")

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, IndexedSlices, SparseTensor
+from .tensor import MetaTensor, Tensor, RowTensor, SparseTensor
 
 __all__ = [
-    "MetaTensor", "Tensor", "IndexedSlices", "SparseTensor",  # tensor
+    "MetaTensor", "Tensor", "RowTensor", "SparseTensor",  # tensor
     'ms_function',  # api
     'Parameter', 'ParameterTuple',  # parameter
     "dtype"

mindspore/common/dtype.py

@@ -99,7 +99,7 @@ slice_type = typing.Slice
 ellipsis_type = typing.TypeEllipsis
 list_type = typing.List
 tuple_type = typing.Tuple
-index_slices = typing.IndexedSlicesType()
+index_slices = typing.RowTensorType()
 sparse_tensor = typing.SparseTensorType()
 undetermined = typing.UndeterminedType()
 

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', 'IndexedSlices', 'SparseTensor']
+__all__ = ['Tensor', 'MetaTensor', 'RowTensor', 'SparseTensor']
 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_)
@@ -267,20 +267,20 @@ class Tensor(Tensor_):
         return tensor_operator_registry.get('any')(keep_dims)(self, axis)
 
 
-class IndexedSlices:
+class RowTensor:
     """
     A sparse representation of a set of tensor slices at given indices.
 
-    An IndexedSlices is typically used to represent a subset of a larger
+    An RowTensor is typically used to represent a subset of a larger
     tensor dense of shape [L0, D1, .. , DN] where L0 >> D0.
 
     The values in indices are the indices in the first dimension of the slices
     that have been extracted from the larger tensor.
 
-    The dense tensor dense represented by an IndexedSlices slices has
+    The dense tensor dense represented by an RowTensor slices has
     `dense[slices.indices[i], :, :, :, ...] = slices.values[i, :, :, :, ...]`.
 
-    IndexedSlices can only be used in the `Cell`'s construct method.
+    RowTensor can only be used in the `Cell`'s contruct method.
 
     It is not supported in pynative mode at the moment.
 
@@ -291,7 +291,7 @@ class IndexedSlices:
             of the corresponding dense tensor.
 
     Returns:
-        IndexedSlices, composed of `indices`, `values`, and `dense_shape`.
+        RowTensor, composed of `indices`, `values`, and `dense_shape`.
 
     Examples:
         >>> class Net(nn.Cell):
@@ -299,8 +299,8 @@ class IndexedSlices:
         >>>         super(Net, self).__init__()
         >>>         self.dense_shape = dense_shape
         >>>     def construct(self, indices, values):
-        >>>         x = IndexedSlices(indices, values, self.dense_shape)
-        >>>         return x.values(), x.indices(), x.dense_shape()
+        >>>         x = RowTensor(indices, values, self.dense_shape)
+        >>>         return x.values, x.indices, x.dense_shape
         >>>
         >>> indices = Tensor([0])
         >>> values = Tensor([[1, 2]], dtype=ms.float32)
@@ -308,17 +308,20 @@ class IndexedSlices:
     """
 
     def __init__(self, indices, values, dense_shape):
-        "Init IndexedSlices"
+        "Init RowTensor"
         self.__indices = indices
         self.__values = values
         self.__dense_shape = dense_shape
 
+    @property
     def indices(self):
         return self.__indices
 
+    @property
     def values(self):
         return self.__values
 
+    @property
     def dense_shape(self):
         return self.__dense_shape
 
@@ -353,7 +356,7 @@ class SparseTensor:
         >>>         self.dense_shape = dense_shape
         >>>     def construct(self, indices, values):
         >>>         x = SparseTensor(indices, values, self.dense_shape)
-        >>>         return x.values(), x.indices(), x.dense_shape()
+        >>>         return x.values, x.indices, x.dense_shape
         >>>
         >>> indices = Tensor([[0, 1], [1, 2]])
         >>> values = Tensor([1, 2], dtype=ms.float32)
@@ -366,11 +369,14 @@ class SparseTensor:
         self.__values = values
         self.__dense_shape = dense_shape
 
+    @property
     def indices(self):
         return self.__indices
 
+    @property
     def values(self):
         return self.__values
 
+    @property
     def dense_shape(self):
         return self.__dense_shape

mindspore/core/abstract/abstract_value.cc

@@ -1050,16 +1050,16 @@ bool AbstractBasePtrListEqual::operator()(const AbstractBasePtrList &lhs, const
   return AbstractBasePtrListDeepEqual(lhs, rhs);
 }
 
-// IndexedSlices
-TypePtr AbstractIndexedSlices::BuildType() const {
+// RowTensor
+TypePtr AbstractRowTensor::BuildType() const {
   MS_EXCEPTION_IF_NULL(element());
   TypePtr element_type = element()->BuildType();
-  return std::make_shared<IndexedSlicesType>(element_type);
+  return std::make_shared<RowTensorType>(element_type);
 }
 
-AbstractBasePtr AbstractIndexedSlices::Clone() const {
+AbstractBasePtr AbstractRowTensor::Clone() const {
   MS_EXCEPTION_IF_NULL(element());
-  auto clone = std::make_shared<AbstractIndexedSlices>(element()->Clone());
+  auto clone = std::make_shared<AbstractRowTensor>(element()->Clone());
   ShapePtr shp = shape();
   clone->set_shape(shp->Clone());
   clone->set_value(GetValueTrack());
@@ -1069,9 +1069,9 @@ AbstractBasePtr AbstractIndexedSlices::Clone() const {
   return clone;
 }
 
-AbstractBasePtr AbstractIndexedSlices::Broaden() const {
+AbstractBasePtr AbstractRowTensor::Broaden() const {
   MS_EXCEPTION_IF_NULL(element());
-  auto broaden = std::make_shared<AbstractIndexedSlices>(element()->Broaden());
+  auto broaden = std::make_shared<AbstractRowTensor>(element()->Broaden());
   auto shp = shape();
   broaden->set_shape(shp->Clone());
   broaden->set_value(kAnyValue);
@@ -1081,9 +1081,9 @@ AbstractBasePtr AbstractIndexedSlices::Broaden() const {
   return broaden;
 }
 
-AbstractBasePtr AbstractIndexedSlices::BroadenWithShape() const {
+AbstractBasePtr AbstractRowTensor::BroadenWithShape() const {
   MS_EXCEPTION_IF_NULL(element());
-  auto broaden = std::make_shared<AbstractIndexedSlices>(element()->Broaden());
+  auto broaden = std::make_shared<AbstractRowTensor>(element()->Broaden());
   auto shp = shape()->Clone();
   shp->Broaden();
   broaden->set_shape(shp);
@@ -1094,7 +1094,7 @@ AbstractBasePtr AbstractIndexedSlices::BroadenWithShape() const {
   return broaden;
 }
 
-std::string AbstractIndexedSlices::ToString() const {
+std::string AbstractRowTensor::ToString() const {
   std::ostringstream buffer;
   BaseShapePtr shape_track = GetShapeTrack();
   MS_EXCEPTION_IF_NULL(shape_track);

mindspore/core/abstract/abstract_value.h

@@ -593,15 +593,15 @@ struct AbstractBasePtrListEqual {
 std::size_t AbstractBasePtrListHash(const AbstractBasePtrList &args_spec_list);
 bool AbstractBasePtrListDeepEqual(const AbstractBasePtrList &lhs, const AbstractBasePtrList &rhs);
 
-// IndexedSlices
-class AbstractIndexedSlices : public AbstractUndetermined {
+// RowTensor
+class AbstractRowTensor : public AbstractUndetermined {
  public:
-  explicit AbstractIndexedSlices(const AbstractBasePtr &element, const BaseShapePtr &shape = std::make_shared<Shape>())
+  explicit AbstractRowTensor(const AbstractBasePtr &element, const BaseShapePtr &shape = std::make_shared<Shape>())
       : AbstractUndetermined(element, shape) {}
-  AbstractIndexedSlices(const TypePtr &element_type, const std::vector<int> &shape)
+  AbstractRowTensor(const TypePtr &element_type, const std::vector<int> &shape)
       : AbstractUndetermined(element_type, shape) {}
-  ~AbstractIndexedSlices() override = default;
-  MS_DECLARE_PARENT(AbstractIndexedSlices, AbstractUndetermined)
+  ~AbstractRowTensor() override = default;
+  MS_DECLARE_PARENT(AbstractRowTensor, AbstractUndetermined)
 
   const AbstractTensorPtr indices() const { return indices_; }
   void set_indices(const AbstractTensorPtr &indices) { indices_ = indices; }

mindspore/core/abstract/param_validator.h

@@ -66,7 +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)
+ABSTRACT_REPORT_NAME_TRAITS(RowTensor)
 ABSTRACT_REPORT_NAME_TRAITS(SparseTensor)
 ABSTRACT_REPORT_NAME_TRAITS(Sequeue)
 

mindspore/core/ir/dtype.cc

@@ -179,40 +179,40 @@ bool TensorType::operator==(const Type &other) const {
   return *element_type_ == *other_elem_type;
 }
 
-TypePtr IndexedSlicesType::DeepCopy() const {
+TypePtr RowTensorType::DeepCopy() const {
   MS_EXCEPTION_IF_NULL(element_type_);
   if (IsGeneric()) {
-    return std::make_shared<IndexedSlicesType>();
+    return std::make_shared<RowTensorType>();
   }
-  return std::make_shared<IndexedSlicesType>(element_type_->DeepCopy());
+  return std::make_shared<RowTensorType>(element_type_->DeepCopy());
 }
 
-std::string IndexedSlicesType::ToReprString() const {
+std::string RowTensorType::ToReprString() const {
   if (element_type_ == nullptr) {
-    return "IndexedSlices";
+    return "RowTensor";
   }
-  return "IndexedSlices[" + element_type_->ToReprString() + "]";
+  return "RowTensor[" + element_type_->ToReprString() + "]";
 }
 
-std::string IndexedSlicesType::ToString() const {
+std::string RowTensorType::ToString() const {
   if (element_type_ == nullptr) {
-    return "IndexedSlices";
+    return "RowTensor";
   }
-  return "IndexedSlices[" + element_type_->ToString() + "]";
+  return "RowTensor[" + element_type_->ToString() + "]";
 }
 
-std::string IndexedSlicesType::DumpText() const {
+std::string RowTensorType::DumpText() const {
   if (element_type_ == nullptr) {
-    return "IndexedSlices";
+    return "RowTensor";
   }
-  return "IndexedSlices[" + element_type_->DumpText() + "]";
+  return "RowTensor[" + element_type_->DumpText() + "]";
 }
 
-bool IndexedSlicesType::operator==(const Type &other) const {
+bool RowTensorType::operator==(const Type &other) const {
   if (!IsSameObjectType(*this, other)) {
     return false;
   }
-  auto other_elem_type = static_cast<const IndexedSlicesType &>(other).element_type_;
+  auto other_elem_type = static_cast<const RowTensorType &>(other).element_type_;
   if (element_type_ == nullptr && other_elem_type == nullptr) {
     return true;
   } else if (element_type_ == nullptr || other_elem_type == nullptr) {

mindspore/core/ir/dtype.h

@@ -154,15 +154,15 @@ class TensorType : public Object {
 };
 using TensorTypePtr = std::shared_ptr<TensorType>;
 
-class IndexedSlicesType : public Object {
+class RowTensorType : 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)
+  RowTensorType() : Object(kObjectTypeRowTensorType, kObjectTypeUndeterminedType) {}
+  explicit RowTensorType(const TypePtr &ele)
+      : Object(kObjectTypeRowTensorType, kObjectTypeUndeterminedType, false), element_type_(ele) {}
+  ~RowTensorType() override = default;
+  MS_DECLARE_PARENT(RowTensorType, Object)
 
-  TypeId generic_type_id() const override { return kObjectTypeIndexedSlicesType; }
+  TypeId generic_type_id() const override { return kObjectTypeRowTensorType; }
   const TypePtr element() const { return element_type_; }
   void set_element(const TypePtr &element_type) { element_type_ = element_type; }
 
@@ -175,7 +175,7 @@ class IndexedSlicesType : public Object {
  private:
   TypePtr element_type_;
 };
-using IndexedSlicesTypePtr = std::shared_ptr<IndexedSlicesType>;
+using RowTensorTypePtr = std::shared_ptr<RowTensorType>;
 
 class SparseTensorType : public Object {
  public:

mindspore/core/ir/dtype/type.cc

@@ -115,8 +115,8 @@ const char *ObjectIdLabel(const TypeId &v) {
       return "kObjectTypeKeyword";
     case kObjectTypeTensorType:
       return "kObjectTypeTensorType";
-    case kObjectTypeIndexedSlicesType:
-      return "kObjectTypeIndexedSlicesType";
+    case kObjectTypeRowTensorType:
+      return "kObjectTypeRowTensorType";
     case kObjectTypeSparseTensorType:
       return "kObjectTypeSparseTensorType";
     case kObjectTypeUndeterminedType:

mindspore/core/ir/dtype/type_id.h

@@ -50,7 +50,7 @@ enum TypeId : int {
   kObjectTypeSlice,
   kObjectTypeKeyword,
   kObjectTypeTensorType,
-  kObjectTypeIndexedSlicesType,
+  kObjectTypeRowTensorType,
   kObjectTypeSparseTensorType,
   kObjectTypeUndeterminedType,
   kObjectTypeClass,

mindspore/core/ir/dtype_extends.cc

@@ -190,9 +190,9 @@ 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>();
+TypePtr RowTensorStrToType(const std::string &type_name) {
+  if (type_name == "RowTensor") {
+    return std::make_shared<RowTensorType>();
   }
   auto start = type_name.find_first_of('[') + 1;
   auto end = type_name.find_last_of(']');
@@ -204,7 +204,7 @@ TypePtr IndexedSlicesStrToType(const std::string &type_name) {
   if (element_type == nullptr) {
     return nullptr;
   }
-  return std::make_shared<IndexedSlicesType>(element_type);
+  return std::make_shared<RowTensorType>(element_type);
 }
 
 TypePtr SparseTensorStrToType(const std::string &type_name) {
@@ -364,8 +364,8 @@ TypePtr StringToType(const std::string &type_name) {
     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("RowTensor"), "RowTensor") == 0) {
+    type = RowTensorStrToType(type_name);
   } else if (type_name.compare(0, strlen("SparseTensor"), "SparseTensor") == 0) {
     type = SparseTensorStrToType(type_name);
   } else if (type_name.compare(0, strlen("List"), "List") == 0) {
@@ -446,7 +446,7 @@ 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 kRowTensorType = std::make_shared<RowTensorType>();
 const TypePtr kSparseTensorType = std::make_shared<SparseTensorType>();
 const TypePtr kUndeterminedType = std::make_shared<UndeterminedType>();
 const TypePtr kString = std::make_shared<String>();

mindspore/nn/optim/adam.py

@@ -85,13 +85,13 @@ def _update_run_op(beta1, beta2, eps, lr, weight_decay, param, m, v, gradient, d
 
 
 @_adam_opt.register("Function", "Function", "Function", "Function", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor",
-                    "Tensor", "IndexedSlices", "Tensor", "Tensor", "Tensor", "Bool")
+                    "Tensor", "RowTensor", "Tensor", "Tensor", "Tensor", "Bool")
 def _run_opt_with_sparse(opt, sparse_opt, push, pull, beta1_power, beta2_power, beta1, beta2, eps, lr,
                          gradient, params, moment1, moment2, ps_parameter):
     """Apply sparse adam optimizer to the weight parameter when the gradient is sparse."""
     success = True
-    indices = gradient.indices()
-    values = gradient.values()
+    indices = gradient.indices
+    values = gradient.values
     if ps_parameter:
         op_shape = P.Shape()
         shapes = (op_shape(params), op_shape(moment1), op_shape(moment2),

mindspore/nn/optim/ftrl.py

@@ -24,13 +24,13 @@ _ftrl_opt = C.MultitypeFuncGraph("ftrl_opt")
 
 
 @_ftrl_opt.register("Function", "Function", "Function", "Function", "Number", "Number", "Number", "Tensor", "Tensor",
-                    "IndexedSlices", "Tensor", "Tensor", "Bool")
+                    "RowTensor", "Tensor", "Tensor", "Bool")
 def _tensor_run_opt_with_sparse(opt, spars_opt, push, pull, l1, l2, lr_power, learning_rate, linear,
                                 gradient, weight, moment, ps_parameter):
     """Apply sparse ftrl optimizer to the weight parameter when the gradient is sparse."""
     success = True
-    indices = gradient.indices()
-    values = gradient.values()
+    indices = gradient.indices
+    values = gradient.values
     if ps_parameter:
         op_shape = P.Shape()
         shapes = (op_shape(weight), op_shape(moment), op_shape(linear), op_shape(values), op_shape(indices))

mindspore/nn/optim/lazyadam.py

@@ -28,13 +28,13 @@ _lazy_adam_opt = C.MultitypeFuncGraph("lazy_adam_opt")
 
 
 @_lazy_adam_opt.register("Function", "Function", "Tensor", "Tensor", "Tensor", "Tensor", "Number", "Tensor",
-                         "IndexedSlices", "Tensor", "Tensor", "Tensor")
+                         "RowTensor", "Tensor", "Tensor", "Tensor")
 def _run_opt_with_sparse(opt, sparse_opt, beta1_power, beta2_power, beta1, beta2, eps, lr, gradient, params,
                          moment1, moment2):
     """Apply sparse lazy adam optimizer to the weight parameter when the gradient is sparse."""
     success = True
     success = F.depend(success, sparse_opt(params, moment1, moment2, beta1_power, beta2_power, lr, beta1, beta2,
-                                           eps, gradient.values(), gradient.indices()))
+                                           eps, gradient.values, gradient.indices))
     return success
 
 

mindspore/nn/optim/optimizer.py

@@ -23,7 +23,7 @@ from mindspore.nn.cell import Cell
 from mindspore.nn.layer.container import CellList
 from mindspore.common.parameter import Parameter, ParameterTuple
 from mindspore.common.initializer import initializer
-from mindspore.common.tensor import Tensor, IndexedSlices
+from mindspore.common.tensor import Tensor, RowTensor
 import mindspore.common.dtype as mstype
 from mindspore._checkparam import Validator as validator
 from mindspore._checkparam import Rel
@@ -493,14 +493,14 @@ op_gather = P.GatherV2()
 _apply_decay = C.MultitypeFuncGraph("apply_decay")
 
 
-@_apply_decay.register("Number", "Bool", "Tensor", "IndexedSlices")
+@_apply_decay.register("Number", "Bool", "Tensor", "RowTensor")
 def _tensor_apply_decay_with_sparse(weight_decay, if_apply, weight, gradient):
     """Get grad with weight_decay."""
     if if_apply:
-        indices = gradient.indices()
-        values = op_add((op_gather(weight, indices, 0) * weight_decay, gradient.values()))
-        shape = gradient.dense_shape()
-        return IndexedSlices(indices, values, shape)
+        indices = gradient.indices
+        values = op_add((op_gather(weight, indices, 0) * weight_decay, gradient.values))
+        shape = gradient.dense_shape
+        return RowTensor(indices, values, shape)
     return gradient
 
 
@@ -523,12 +523,12 @@ def tensor_grad_scale(scale, grad):
     return grad * scale
 
 
-@_grad_scale.register("Number", "IndexedSlices")
+@_grad_scale.register("Number", "RowTensor")
 def tensor_grad_scale_with_sparse(scale, grad):
     """Get grad with scale."""
     if scale == 1.0:
         return grad
-    return IndexedSlices(grad.indices(), grad.values() * scale, grad.dense_shape())
+    return RowTensor(grad.indices, grad.values * scale, grad.dense_shape)
 
 
 class _ConvertToCell(LearningRateSchedule):

mindspore/nn/optim/proximal_ada_grad.py

@@ -22,12 +22,12 @@ from .optimizer import Optimizer
 
 _proximal_ada_grad_opt = C.MultitypeFuncGraph("proximal_ada_grad_opt")
 
-@_proximal_ada_grad_opt.register("Function", "Function", "Tensor", "Tensor", "Tensor", "IndexedSlices", "Tensor",
+@_proximal_ada_grad_opt.register("Function", "Function", "Tensor", "Tensor", "Tensor", "RowTensor", "Tensor",
                                  "Tensor")
 def _tensor_run_opt_with_sparse(opt, sparse_opt, l1, l2, learning_rate, gradient, weight, accum):
     """Apply sparse proximal_ada_grad optimizer to the weight parameter."""
     success = True
-    success = F.depend(success, sparse_opt(weight, accum, learning_rate, l1, l2, gradient.values(), gradient.indices()))
+    success = F.depend(success, sparse_opt(weight, accum, learning_rate, l1, l2, gradient.values, gradient.indices))
     return success
 
 

mindspore/nn/sparse/sparse.py

@@ -49,6 +49,6 @@ class SparseToDense(Cell):
         self.sparse_to_dense = P.SparseToDense()
 
     def construct(self, sparse_tensor):
-        return self.sparse_to_dense(sparse_tensor.indices(),
-                                    sparse_tensor.values(),
-                                    sparse_tensor.dense_shape())
+        return self.sparse_to_dense(sparse_tensor.indices,
+                                    sparse_tensor.values,
+                                    sparse_tensor.dense_shape)

mindspore/nn/wrap/grad_reducer.py

@@ -16,7 +16,7 @@
 from mindspore import context
 from mindspore.nn.cell import Cell
 from mindspore.communication.management import GlobalComm, get_group_size
-from mindspore.common.tensor import IndexedSlices
+from mindspore.common.tensor import RowTensor
 from mindspore.ops import functional as F, composite as C, operations as P
 from mindspore.ops.operations.comm_ops import AllReduce, AllGather
 from mindspore.parallel._auto_parallel_context import auto_parallel_context
@@ -103,7 +103,7 @@ def _tensors_allreduce_ps(degree, mean, allgather, allreduce, allreduce_filter,
     return grad
 
 
-@reduce_opt.register("Number", "Bool", "Function", "Function", "Bool", "IndexedSlices")
+@reduce_opt.register("Number", "Bool", "Function", "Function", "Bool", "RowTensor")
 def _tensors_allreduce_with_sparse(degree, mean, allgather, allreduce, allreduce_filter, grad):
     """
     Apply allgather on gradient instead of allreduce for sparse feature.
@@ -118,21 +118,21 @@ def _tensors_allreduce_with_sparse(degree, mean, allgather, allreduce, allreduce
         grad (tuple): The indices, gradient tensor and tensor_shape before operation.
 
     Returns:
-        IndexedSlices, the gradient after operation.
+        RowTensor, the gradient after operation.
     """
     if allreduce_filter:
-        indices = allgather(grad.indices())
-        dout = allgather(grad.values())
+        indices = allgather(grad.indices)
+        dout = allgather(grad.values)
         if mean:
-            degree = F.scalar_cast(degree, F.dtype(grad.values()))
+            degree = F.scalar_cast(degree, F.dtype(grad.values))
             cast_op = P.Cast()
             mul_op = P.Mul()
             dout = mul_op(dout, cast_op(F.scalar_to_array(1.0 / degree), F.dtype(dout)))
-        grad = IndexedSlices(indices, dout, grad.dense_shape())
+        grad = RowTensor(indices, dout, grad.dense_shape)
     return grad
 
 
-@reduce_opt.register("Number", "Bool", "Function", "Function", "Bool", "IndexedSlices", "Bool")
+@reduce_opt.register("Number", "Bool", "Function", "Function", "Bool", "RowTensor", "Bool")
 def _tensors_allreduce_with_sparse_ps(degree, mean, allgather, allreduce, allreduce_filter, grad, ps_parameter):
     """
     Apply allgather on gradient instead of allreduce for sparse feature.
@@ -148,20 +148,20 @@ def _tensors_allreduce_with_sparse_ps(degree, mean, allgather, allreduce, allred
         ps_parameter (bool): Use parameter server or not.
 
     Returns:
-        IndexedSlices, the gradient after operation.
+        RowTensor, the gradient after operation.
     """
     if ps_parameter:
         return grad
 
     if allreduce_filter:
-        indices = allgather(grad.indices())
-        dout = allgather(grad.values())
+        indices = allgather(grad.indices)
+        dout = allgather(grad.values)
         if mean:
-            degree = F.scalar_cast(degree, F.dtype(grad.values()))
+            degree = F.scalar_cast(degree, F.dtype(grad.values))
             cast_op = P.Cast()
             mul_op = P.Mul()
             dout = mul_op(dout, cast_op(F.scalar_to_array(1.0 / degree), F.dtype(dout)))
-        grad = IndexedSlices(indices, dout, grad.dense_shape())
+        grad = RowTensor(indices, dout, grad.dense_shape)
     return grad
 
 
@@ -182,18 +182,18 @@ def _tensors_get_datatype(grad):
     return F.dtype(grad)
 
 
-@_get_datatype.register("IndexedSlices")
+@_get_datatype.register("RowTensor")
 def _tensors_get_datatype_with_sparse(grad):
     """
     Acquire gradient datatype.
 
     Args:
-        grad (IndexedSlices): The gradient before operation.
+        grad (RowTensor): The gradient before operation.
 
     Returns:
         mstype, the datatype of gradient.
     """
-    return F.dtype(grad.values())
+    return F.dtype(grad.values)
 
 
 _cast_datatype = C.MultitypeFuncGraph("_cast_datatype")
@@ -214,20 +214,20 @@ def _tensors_cast_datatype(datatype, grad):
     return F.cast(grad, datatype)
 
 
-@_cast_datatype.register("TypeType", "IndexedSlices")
+@_cast_datatype.register("TypeType", "RowTensor")
 def _tensors_cast_datatype_with_sparse(datatype, grad):
     """
     Cast gradient to datatype.
 
     Args:
         datatype (mstype): the destination datatype of gradient.
-        grad (IndexedSlices): The gradient before operation.
+        grad (RowTensor): The gradient before operation.
 
     Returns:
-        IndexedSlices, the gradient after operation.
+        RowTensor, the gradient after operation.
     """
-    dout = F.cast(grad.values(), datatype)
-    return IndexedSlices(grad.indices(), dout, grad.dense_shape())
+    dout = F.cast(grad.values, datatype)
+    return RowTensor(grad.indices, dout, grad.dense_shape)
 
 
 class DistributedGradReducer(Cell):

mindspore/nn/wrap/loss_scale.py

@@ -18,7 +18,7 @@ from mindspore.nn.wrap.grad_reducer import DistributedGradReducer
 from mindspore.train.parallel_utils import ParallelMode
 from mindspore.parallel._utils import _get_device_num, _get_parallel_mode, _get_mirror_mean
 from ..cell import Cell
-from ...common import Tensor, IndexedSlices
+from ...common import Tensor, RowTensor
 from ...common.parameter import Parameter
 from ...ops import functional as F
 from ...ops import composite as C
@@ -35,11 +35,11 @@ reciprocal = P.Reciprocal()
 def tensor_grad_scale(scale, grad):
     return grad * F.cast(reciprocal(scale), F.dtype(grad))
 
-@_grad_scale.register("Tensor", "IndexedSlices")
-def tensor_grad_scale_indexed_slices(scale, grad):
-    return IndexedSlices(grad.indices(),
-                         grad.values() * F.cast(reciprocal(scale), F.dtype(grad.values())),
-                         grad.dense_shape())
+@_grad_scale.register("Tensor", "RowTensor")
+def tensor_grad_scale_row_tensor(scale, grad):
+    return RowTensor(grad.indices,
+                     grad.values * F.cast(reciprocal(scale), F.dtype(grad.values)),
+                     grad.dense_shape)
 
 _grad_overflow = C.MultitypeFuncGraph("_grad_overflow")
 grad_overflow = P.FloatStatus()

mindspore/ops/_grad/grad_array_ops.py

@@ -27,7 +27,7 @@ from .grad_base import bprop_getters
 from ..primitive import constexpr
 from ... import context
 from ...common import dtype as mstype
-from ...common.tensor import IndexedSlices
+from ...common.tensor import RowTensor
 
 reduce_sum = P.ReduceSum()
 unsorted_segment_sum = P.UnsortedSegmentSum()
@@ -75,12 +75,12 @@ def dout_cast_number(dout, x):
     dx = cast(dout, get_dtype(x))
     return dx
 
-@dout_cast.register("IndexedSlices", "Tensor")
-def dout_cast_indexed_slices(dout, x):
+@dout_cast.register("RowTensor", "Tensor")
+def dout_cast_row_tensor(dout, x):
     cast = P.Cast()
     get_dtype = P.DType()
-    values = cast(dout.values(), get_dtype(x))
-    return IndexedSlices(dout.indices(), values, dout.dense_shape())
+    values = cast(dout.values, get_dtype(x))
+    return RowTensor(dout.indices, values, dout.dense_shape)
 
 
 @bprop_getters.register(P.Cast)
@@ -240,7 +240,7 @@ def get_bprop_embedding_lookup(self):
         actual_dout_shape_changed = new_indices_shape_changed + x_shp_tail
         # Reshape the 'actual_dout' on device
         actual_dout = reshape_op(dout, actual_dout_shape_changed)
-        return IndexedSlices(new_indices, actual_dout, x_shp), zeros_like(indices), zeros_like(offset)
+        return RowTensor(new_indices, actual_dout, x_shp), zeros_like(indices), zeros_like(offset)
     return bprop_sparse
 
 
@@ -369,7 +369,7 @@ def get_bprop_sparse_gather_v2(self):
             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)
+            return RowTensor(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:

mindspore/ops/_grad/grad_comm_ops.py

@@ -17,7 +17,7 @@
 import mindspore.common.dtype as mstype
 from mindspore.ops import functional as F
 from .. import operations as P
-from ...common.tensor import IndexedSlices
+from ...common.tensor import RowTensor
 from ..composite.multitype_ops.zeros_like_impl import zeros_like
 from ..operations.comm_ops import (AllGather, _HostAllGather, AllReduce, _AlltoAll, Broadcast,
                                    _GetTensorSlice, _MirrorOperator, ReduceOp,
@@ -47,9 +47,9 @@ def get_bprop_all_reduce(self):
             if F.issubclass_(F.typeof(dout), mstype.tensor):
                 dx = all_reduce_grad(dout)
             else:
-                indices = all_gather(dout.indices())
-                grad = all_gather(dout.values())
-                dx = IndexedSlices(indices, grad, dout.dense_shape())
+                indices = all_gather(dout.indices)
+                grad = all_gather(dout.values)
+                dx = RowTensor(indices, grad, dout.dense_shape)
             return (dx,)
     else:
 
@@ -60,12 +60,12 @@ def get_bprop_all_reduce(self):
                 z = cast(z, dtype(dx))
                 dx = mul(dx, z)
             else:
-                indices = all_gather(dout.indices())
-                grad = all_gather(dout.values())
+                indices = all_gather(dout.indices)
+                grad = all_gather(dout.values)
                 z = equal(x, out)
                 z = cast(z, dtype(grad))
                 grad = mul(grad, z)
-                dx = IndexedSlices(indices, grad, dout.dense_shape())
+                dx = RowTensor(indices, grad, dout.dense_shape)
             return (dx,)
     return bprop
 
@@ -195,19 +195,19 @@ def get_bprop_mirror_operator(self):
                 num = F.scalar_cast(dev_num, F.dtype(dx))
                 dx = mul(dx, cast(F.scalar_to_array(float_one/num), F.dtype(dx)))
             else:
-                indices = all_gather(dout.indices())
-                grad = all_gather(dout.values())
+                indices = all_gather(dout.indices)
+                grad = all_gather(dout.values)
                 float_one = F.scalar_cast(1.0, F.dtype(grad))
                 num = F.scalar_cast(dev_num, F.dtype(grad))
                 grad = mul(grad, cast(F.scalar_to_array(float_one/num), F.dtype(grad)))
-                dx = IndexedSlices(indices, grad, dout.dense_shape())
+                dx = RowTensor(indices, grad, dout.dense_shape)
         else:
             if F.issubclass_(F.typeof(dout), mstype.tensor):
                 dx = all_reduce(dout)
             else:
-                indices = all_gather(dout.indices())
-                grad = all_gather(dout.values())
-                dx = IndexedSlices(indices, grad, dout.dense_shape())
+                indices = all_gather(dout.indices)
+                grad = all_gather(dout.values)
+                dx = RowTensor(indices, grad, dout.dense_shape)
 
         return (dx,)
     return bprop

mindspore/ops/functional.py

@@ -152,10 +152,10 @@ 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')
+make_row_tensor = Primitive('MakeRowTensor')
+row_tensor_get_values = Primitive('RowTensorGetValues')
+row_tensor_get_indices = Primitive('RowTensorGetIndices')
+row_tensor_get_dense_shape = Primitive('RowTensorGetDenseShape')
 
 make_sparse_tensor = Primitive('MakeSparseTensor')
 sparse_tensor_get_values = Primitive('SparseTensorGetValues')

mindspore/ops/operations/other_ops.py

@@ -389,8 +389,8 @@ class CheckBprop(PrimitiveWithInfer):
         validator.check_value_type('grads', xshapes, (tuple,), tips)
         validator.check_value_type('params', yshapes, (tuple,), tips)
         if len(xshapes) < len(yshapes):
-            raise TypeError(f"{tips}, the size of output should be {len(yshapes)},"
-                            f" but got {len(xshapes)}.")
+            raise ValueError(f"{tips}, the size of output should be {len(yshapes)},"
+                             f" but got {len(xshapes)}.")
         checking_range = len(yshapes)
         for i in range(checking_range):
             xshape = xshapes[i]
@@ -398,8 +398,8 @@ class CheckBprop(PrimitiveWithInfer):
             if not xshape or not yshape:
                 continue
             if xshape != yshape:
-                raise TypeError(f"{tips}, the shape of {i}th output should be {yshape},"
-                                f" but got {xshape}.")
+                raise ValueError(f"{tips}, the shape of {i}th output should be {yshape},"
+                                 f" but got {xshape}.")
         return xshapes
 
     def infer_dtype(self, xdtypes, ydtypes):
@@ -407,8 +407,8 @@ class CheckBprop(PrimitiveWithInfer):
         validator.check_value_type('grads', xdtypes, (tuple,), tips)
         validator.check_value_type('params', ydtypes, (tuple,), tips)
         if len(xdtypes) < len(ydtypes):
-            raise TypeError(f"{tips}, the size of output should be {len(ydtypes)},"
-                            f" but got {len(xdtypes)}.")
+            raise ValueError(f"{tips}, the size of output should be {len(ydtypes)},"
+                             f" but got {len(xdtypes)}.")
         checking_range = len(ydtypes)
         for i in range(checking_range):
             xdtype = xdtypes[i]

tests/ut/python/pipeline/parse/test_cell_bprop.py → tests/st/networks/test_cell_bprop.py

@@ -19,25 +19,16 @@ import pytest
 import mindspore as ms
 import mindspore.common.dtype as mstype
 import mindspore.nn as nn
-from mindspore import Parameter
+from mindspore import Parameter, ParameterTuple
 from mindspore import context
 from mindspore.common.initializer import initializer
 from mindspore.common.tensor import Tensor
 from mindspore.ops import composite as C
 from mindspore.ops import operations as P
-from .....mindspore_test_framework.utils.bprop_util import bprop
 
-
-def setup_module(module):
-    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
-
-def teardown_module(module):
-    context.set_context(device_target="Ascend")
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 
 class MulAdd(nn.Cell):
-    def __init__(self):
-        super(MulAdd, self).__init__()
-
     def construct(self, x, y):
         return 2 * x + y
 
@@ -45,7 +36,9 @@ class MulAdd(nn.Cell):
         # In this test case, The user defined bprop is wrong defined purposely to distinguish from ad result
         return 2 * dout, 2 * y
 
-
+@pytest.mark.level0
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
 def test_grad_mul_add():
     mul_add = MulAdd()
     x = Tensor(1, dtype=ms.int32)
@@ -62,7 +55,9 @@ class InlineMulADD(nn.Cell):
     def construct(self, x, y):
         return self.mul_add(x, y) + x + self.param * y
 
-
+@pytest.mark.level0
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
 def test_grad_inline_mul_add():
     inline_mul_add = InlineMulADD()
     x = Tensor(1, dtype=ms.int32)
@@ -83,7 +78,9 @@ class WithParameter(nn.Cell):
         # In this test case, The user defined bprop is wrong defined purposely to distinguish from ad result
         return self.param1 * self.param2 * dout, 2 * y
 
-
+@pytest.mark.level0
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
 def test_with_param():
     with_param = WithParameter()
     with pytest.raises(RuntimeError):
@@ -91,20 +88,21 @@ def test_with_param():
 
 
 class WithNoBprop(nn.Cell):
-    def __init__(self):
-        super(WithNoBprop, self).__init__()
-
     def construct(self, x, y):
         return 2 * x + y
 
-
+@pytest.mark.level0
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
 def test_with_no_bprop():
     with_no_bprop = WithNoBprop()
     x = Tensor(1, dtype=ms.int32)
     y = Tensor(2, dtype=ms.int32)
-    C.grad_all(with_no_bprop)(x, y)
-
+    assert C.grad_all(with_no_bprop)(x, y) == (2, 1)
 
+@pytest.mark.level0
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
 def test_grad_in_bprop_1():
     class GradInBprop_1(nn.Cell):
         def __init__(self):
@@ -140,7 +138,9 @@ def test_grad_in_bprop_1():
     assert (grads[0].asnumpy() == np.ones([2, 2]).astype(np.float32)).all()
     assert (grads[1].asnumpy() == np.zeros([2, 2]).astype(np.float32)).all()
 
-
+@pytest.mark.level0
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
 def test_grad_in_bprop_2():
     class GradInBprop_1(nn.Cell):
         def __init__(self):
@@ -179,7 +179,9 @@ def test_grad_in_bprop_2():
     assert (grads[0].asnumpy() == np.ones([2, 2]).astype(np.float32)).all()
     assert (grads[1].asnumpy() == np.array([[2, 2], [2, 2]]).astype(np.float32)).all()
 
-
+@pytest.mark.level0
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
 def test_grad_in_bprop_3():
     class GradInBprop_1(nn.Cell):
         def __init__(self):
@@ -230,7 +232,9 @@ class OneInputBprop(nn.Cell):
     def bprop(self, x, out, dout):
         return (5 * x,)
 
-
+@pytest.mark.level0
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
 def test_grad_one_input_bprop():
     net = OneInputBprop()
     input1 = Tensor(np.ones([2, 2]).astype(np.float32))
@@ -239,9 +243,6 @@ def test_grad_one_input_bprop():
 
 
 class TwoInput(nn.Cell):
-    def __init__(self):
-        super().__init__()
-
     def construct(self, x, y):
         return x * y
 
@@ -258,12 +259,17 @@ class InlineBpropTwoInput(nn.Cell):
         grads = C.grad_all(self.f)(x, y)
         return grads[0] * 2, grads[1] * 2
 
-
+@pytest.mark.level0
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
 def test_grad_inline_bprop_two_input():
     net = InlineBpropTwoInput()
     input1 = Tensor(np.ones([2, 2]).astype(np.float32))
     input2 = Tensor(np.ones([2, 2]).astype(np.float32))
-    C.grad_all(net)(input1, input2)
+    grads = C.grad_all(net)(input1, input2)
+    assert (grads[0].asnumpy() == np.array([2, 2]).astype(np.float32)).all()
+    assert (grads[1].asnumpy() == np.array([2, 2]).astype(np.float32)).all()
+    assert len(grads) == 2
 
 
 class TwoInputBprop(nn.Cell):
@@ -314,7 +320,9 @@ class InlineMutilTwoInputParameterCell(nn.Cell):
         output = self.f1(x, y) + self.f2(x, y) + self.f3(x, y) + self.f4(x, y)
         return output
 
-
+@pytest.mark.level0
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
 def test_grad_inline_bprop_multi_input():
     net = InlineMutilTwoInputParameterCell()
     input1 = Tensor(np.ones([2, 2]).astype(np.float32))
@@ -335,29 +343,54 @@ class MulAddWithParam(nn.Cell):
     def construct(self, x):
         return self.mul_add(self.param, x)
 
-
+@pytest.mark.level0
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
 def test_refkey_bprop():
-    net = MulAddWithParam()
+    grad_by_list = C.GradOperation('get_by_list', get_all=True, 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
+            grads = grad_by_list(self.network, weights)(x)
+            return grads
+    network = GradWrap(MulAddWithParam())
     input_data = Tensor(np.array([2, 2], np.float32))
-    grads = bprop(net, input_data,
-                  grads_wrt_outputs=(Tensor(np.ones([1, 2]).astype(np.float32))),
-                  wrt=['params', 'inputs'],
-                  params=net.trainable_params())
+    grads = network(input_data)
     assert (grads[0][0].asnumpy() == np.array([4, 4]).astype(np.float32)).all()
     assert (grads[1][0].asnumpy() == np.array([2, 2]).astype(np.float32)).all()
 
 
-class MulAddWithWrongOutputType(nn.Cell):
-    def __init__(self):
-        super(MulAddWithWrongOutputType, self).__init__()
+class MulAddWithWrongOutputNum(nn.Cell):
+    def construct(self, x, y):
+        return 2 * x + y
 
+    def bprop(self, x, y, out, dout):
+        return (2 * dout,)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_grad_mul_add_with_wrong_output_num():
+    context.set_context(check_bprop=True)
+    mul_add = MulAddWithWrongOutputNum()
+    with pytest.raises(TypeError):
+        C.grad_all(mul_add)(1, 2)
+
+
+class MulAddWithWrongOutputType(nn.Cell):
     def construct(self, x, y):
         return 2 * x + y
 
     def bprop(self, x, y, out, dout):
         return 2 * dout, 2
 
-
+@pytest.mark.level0
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
 def test_grad_mul_add_with_wrong_output_type():
     context.set_context(check_bprop=True)
     mul_add = MulAddWithWrongOutputType()
@@ -376,7 +409,9 @@ class MulAddWithWrongOutputShape(nn.Cell):
     def bprop(self, x, y, out, dout):
         return 2, self.ones
 
-
+@pytest.mark.level0
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
 def test_grad_mul_add_with_wrong_output_shape():
     context.set_context(check_bprop=True)
     mul_add = MulAddWithWrongOutputShape()

tests/ut/cpp/optimizer/lib_test.cc

@@ -606,14 +606,14 @@ TEST_F(TestOptLib, test_adjust_allreduce_mul_add) {
   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_});
+TEST_F(TestOptLib, test_row_tensor) {
+  FuncGraphPtr before_get_indices = getPyFun.CallAndParseRet("test_row_tensor", "before_get_indices");
+  FuncGraphPtr after_get_indices = getPyFun.CallAndParseRet("test_row_tensor", "after_get_indices");
+  FuncGraphPtr before_get_values = getPyFun.CallAndParseRet("test_row_tensor", "before_get_values");
+  FuncGraphPtr after_get_values = getPyFun.CallAndParseRet("test_row_tensor", "after_get_values");
+  FuncGraphPtr before_get_dense_shape = getPyFun.CallAndParseRet("test_row_tensor", "before_get_dense_shape");
+  FuncGraphPtr after_get_dense_shape = getPyFun.CallAndParseRet("test_row_tensor", "after_get_dense_shape");
+  auto patterns = std::vector<SubstitutionPtr>({irpass.row_tensor_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));

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

@@ -1130,17 +1130,17 @@ def test_adjust_allreduce_mul_add(tag):
     return fns[tag]
 
 
-def test_indexed_slices(tag):
+def test_row_tensor(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')
+    make_row_tensor = Primitive('MakeRowTensor')
+    row_tensor_get_values = Primitive('RowTensorGetValues')
+    row_tensor_get_indices = Primitive('RowTensorGetIndices')
+    row_tensor_get_dense_shape = Primitive('RowTensorGetDenseShape')
 
     @fns
     def before_get_indices(x, y, z):
-        return indexed_slices_get_indices(make_indexed_slices(x, y, z))
+        return row_tensor_get_indices(make_row_tensor(x, y, z))
 
     @fns
     def after_get_indices(x, y, z):
@@ -1148,7 +1148,7 @@ def test_indexed_slices(tag):
 
     @fns
     def before_get_values(x, y, z):
-        return indexed_slices_get_values(make_indexed_slices(x, y, z))
+        return row_tensor_get_values(make_row_tensor(x, y, z))
 
     @fns
     def after_get_values(x, y, z):
@@ -1156,7 +1156,7 @@ def test_indexed_slices(tag):
 
     @fns
     def before_get_dense_shape(x, y, z):
-        return indexed_slices_get_dense_shape(make_indexed_slices(x, y, z))
+        return row_tensor_get_dense_shape(make_row_tensor(x, y, z))
 
     @fns
     def after_get_dense_shape(x, y, z):

tests/ut/python/ir/test_indexed_slices.py → tests/ut/python/ir/test_row_tensor.py

@@ -13,10 +13,10 @@
 # limitations under the License.
 # ============================================================================
 """
-@File  : test_indexed_slices.py
+@File  : test_row_tensor.py
 @Author:
 @Date  : 2020-06-08
-@Desc  : test mindspore indexed_slices's operation
+@Desc  : test mindspore row_tensor's operation
 """
 import numpy as np
 import pytest
@@ -29,7 +29,7 @@ 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 import Tensor, RowTensor, context
 from mindspore.common.parameter import Parameter, ParameterTuple
 from mindspore.common import dtype as mstype
 from mindspore._checkparam import Validator as validator
@@ -122,7 +122,7 @@ def get_bprop_sparse_gather_v2(self):
             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)
+            return RowTensor(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:
@@ -142,10 +142,10 @@ def get_bprop_sparse_gather_v2(self):
 
 adam_opt_for_map = C.MultitypeFuncGraph("adam_opt_for_map")
 @adam_opt_for_map.register("Tensor", "Tensor", "Tensor", "Tensor", "Tensor",
-                           "Tensor", "Tensor", "Tensor", "IndexedSlices", "Bool")
-def _update_run_op_for_map_indexed_slices(beta1, beta2, eps, lr, weight_decay_tensor, param,
-                                          m, v, gradient, decay_flag):
-    return gradient.values()
+                           "Tensor", "Tensor", "Tensor", "RowTensor", "Bool")
+def _update_run_op_for_map_row_tensor(beta1, beta2, eps, lr, weight_decay_tensor, param,
+                                      m, v, gradient, decay_flag):
+    return gradient.values
 
 @adam_opt_for_map.register("Tensor", "Tensor", "Tensor", "Tensor", "Tensor",
                            "Tensor", "Tensor", "Tensor", "Tensor", "Bool")
@@ -219,35 +219,35 @@ class AdamWeightDecaySparse(Optimizer):
         return updated_velocity
 
 
-def test_indexed_slices_make_indexed_slices():
-    class MakeIndexedSlices(nn.Cell):
+def test_row_tensor_make_row_tensor():
+    class MakeRowTensor(nn.Cell):
         def __init__(self):
-            super(MakeIndexedSlices, self).__init__()
+            super(MakeRowTensor, self).__init__()
             self.dense_shape = (3, 2)
         def construct(self, indices, values):
-            ret = (IndexedSlices(indices, values, self.dense_shape),)
+            ret = (RowTensor(indices, values, self.dense_shape),)
             return ret[0]
     indices = Tensor([1, 2])
     values = Tensor([[0, 0], [1, 2]], dtype=ms.float32)
-    MakeIndexedSlices()(indices, values)
+    MakeRowTensor()(indices, values)
 
 
-class IndexedSlicesGetAttr(nn.Cell):
+class RowTensorGetAttr(nn.Cell):
     def __init__(self, dense_shape):
-        super(IndexedSlicesGetAttr, self).__init__()
+        super(RowTensorGetAttr, self).__init__()
         self.dense_shape = dense_shape
     def construct(self, indices, values):
-        x = IndexedSlices(indices, values, self.dense_shape)
-        return x.values(), x.indices(), x.dense_shape()
+        x = RowTensor(indices, values, self.dense_shape)
+        return x.values, x.indices, x.dense_shape
 
 
-def test_indexed_slices_attr():
+def test_row_tensor_attr():
     indices = Tensor([0])
     values = Tensor([[1, 2]], dtype=ms.float32)
-    IndexedSlicesGetAttr((3, 2))(indices, values)
+    RowTensorGetAttr((3, 2))(indices, values)
 
 
-def test_indexed_slices_sparse_gatherv2_grad_all():
+def test_row_tensor_sparse_gatherv2_grad_all():
     grad_all = C.GradOperation('get_all', get_all=True)
     class GradWrap(nn.Cell):
         def __init__(self, network):
@@ -255,7 +255,7 @@ def test_indexed_slices_sparse_gatherv2_grad_all():
             self.network = network
         def construct(self, x, y):
             grad = grad_all(self.network)(x, y)
-            return grad[0].indices(), grad[0].values(), grad[0].dense_shape()
+            return grad[0].indices, grad[0].values, grad[0].dense_shape
     class SparseGatherV2(nn.Cell):
         def __init__(self):
             super(SparseGatherV2, self).__init__()
@@ -268,7 +268,7 @@ def test_indexed_slices_sparse_gatherv2_grad_all():
     GradWrap(SparseGatherV2())(params, indices)
 
 
-def test_indexed_slices_sparse_gatherv2_grad_with_pram():
+def test_row_tensor_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):
@@ -279,7 +279,7 @@ def test_indexed_slices_sparse_gatherv2_grad_with_pram():
             weights = self.weights
             grad = grad_by_list(self.network, weights)(x)
             x = grad[0]
-            return x.values(), x.indices(), x.dense_shape()
+            return x.values, x.indices, x.dense_shape
     class SparseGatherV2(nn.Cell):
         def __init__(self):
             super(SparseGatherV2, self).__init__()
@@ -293,7 +293,7 @@ def test_indexed_slices_sparse_gatherv2_grad_with_pram():
     network(indices)
 
 
-def test_indexed_slices_env_get():
+def test_row_tensor_env_get():
     class Loss(nn.Cell):
         def __init__(self):
             super(Loss, self).__init__()
@@ -321,7 +321,7 @@ def test_indexed_slices_env_get():
     train_network(inputs, label)
 
 
-def test_indexed_slices_model_train():
+def test_row_tensor_model_train():
     class Net(nn.Cell):
         def __init__(self, in_features, out_features):
             super(Net, self).__init__()
@@ -347,76 +347,76 @@ def test_indexed_slices_model_train():
     model.train(2, dataset, dataset_sink_mode=False)
 
 
-def test_indexed_slices_values_dim_greater_than_dense_shape_dim():
+def test_row_tensor_values_dim_greater_than_dense_shape_dim():
     indices = Tensor(np.array([0, 1], dtype=np.int32))
     values = Tensor(np.random.randn(2, 4, 5).astype(np.float32))
     dense_shape = (3, 4)
     with pytest.raises(TypeError):
-        IndexedSlicesGetAttr(dense_shape)(indices, values)
+        RowTensorGetAttr(dense_shape)(indices, values)
 
 
-def test_indexed_slices_values_dim_less_than_dense_shape_dim():
+def test_row_tensor_values_dim_less_than_dense_shape_dim():
     indices = Tensor(np.array([0, 1], dtype=np.int32))
     values = Tensor(np.random.randn(2, 4).astype(np.float32))
     dense_shape = (3, 4, 5)
     with pytest.raises(TypeError):
-        IndexedSlicesGetAttr(dense_shape)(indices, values)
+        RowTensorGetAttr(dense_shape)(indices, values)
 
 
-def test_indexed_slices_value_and_dense_shape_illegal():
+def test_row_tensor_value_and_dense_shape_illegal():
     indices = Tensor(np.array([0, 1], dtype=np.int32))
     values = Tensor(np.random.randn(2, 4).astype(np.float32))
     dense_shape = (3, 5)
     with pytest.raises(TypeError):
-        IndexedSlicesGetAttr(dense_shape)(indices, values)
+        RowTensorGetAttr(dense_shape)(indices, values)
 
 
-class IndexedSlicesValuesDouble(nn.Cell):
+class RowTensorValuesDouble(nn.Cell):
     def __init__(self):
         super().__init__()
 
     def construct(self, x):
-        indices = x.indices()
-        values = x.values() * 2
-        dense_shape = x.dense_shape()
-        return IndexedSlices(indices, values, dense_shape)
+        indices = x.indices
+        values = x.values * 2
+        dense_shape = x.dense_shape
+        return RowTensor(indices, values, dense_shape)
 
 
-class IndexedSlicesValuesAdd2(nn.Cell):
+class RowTensorValuesAdd2(nn.Cell):
     def __init__(self):
         super().__init__()
 
     def construct(self, x):
-        indices = x.indices()
-        values = x.values() + 2
-        dense_shape = x.dense_shape()
-        return IndexedSlices(indices, values, dense_shape)
+        indices = x.indices
+        values = x.values + 2
+        dense_shape = x.dense_shape
+        return RowTensor(indices, values, dense_shape)
 
 
-class IndexedSlicesWithControlIf(nn.Cell):
+class RowTensorWithControlIf(nn.Cell):
     def __init__(self, dense_shape):
         super().__init__()
-        self.op1 = IndexedSlicesValuesDouble()
-        self.op2 = IndexedSlicesValuesAdd2()
+        self.op1 = RowTensorValuesDouble()
+        self.op2 = RowTensorValuesAdd2()
         self.dense_shape = dense_shape
 
     def construct(self, a, b, indices, values):
-        x = IndexedSlices(indices, values, self.dense_shape)
+        x = RowTensor(indices, values, self.dense_shape)
         if a > b:
             x = self.op1(x)
         else:
             x = self.op2(x)
-        return x.indices(), x.values()
+        return x.indices, x.values
 
 
-def test_indexed_slices_with_control_flow_if():
+def test_row_tensor_with_control_flow_if():
     a = Tensor(np.array(0).astype(np.int32))
     b = Tensor(np.array(2).astype(np.int32))
     indices = Tensor(np.array([0, 2]).astype(np.int32))
     values = Tensor(np.ones([2, 2]).astype(np.float32))
     dense_shape = (5, 2)
 
-    net = IndexedSlicesWithControlIf(dense_shape)
+    net = RowTensorWithControlIf(dense_shape)
     net(a, b, indices, values)
 
 

tests/ut/python/ir/test_sparse_tensor.py

@@ -52,7 +52,7 @@ def test_sparse_tensor_attr():
             self.dense_shape = (3, 4)
         def construct(self, indices, values):
             x = SparseTensor(indices, values, self.dense_shape)
-            return x.values(), x.indices(), x.dense_shape()
+            return x.values, x.indices, x.dense_shape
 
     indices = Tensor([[0, 1], [1, 2]])
     values = Tensor([1, 2], dtype=ms.float32)

tests/ut/python/pipeline/parse/test_parse.py

@@ -175,7 +175,7 @@ def test_bprop_with_wrong_output_num():
         def construct(self, x, y):
             return BpropWithWrongOutputNum()(x, y)
 
-    with pytest.raises(TypeError):
+    with pytest.raises(ValueError):
         C.grad_all(BpropWithWrongOutputNumCell())(1, 2)
 
 def test_bprop_with_wrong_output_type():
@@ -247,7 +247,7 @@ def test_bprop_with_wrong_output_shape():
         def construct(self, x):
             return BpropWithWrongOutputShape()(x)
 
-    with pytest.raises(TypeError):
+    with pytest.raises(ValueError):
         net = BpropWithWrongOutputShapeCell()
         net.set_grad()
         C.grad_all(net)(Tensor(np.ones([64, 10]).astype(np.int32)))

tests/ut/python/pynative_mode/test_sparse_pynative.py

@@ -20,7 +20,7 @@
 """
 import mindspore as ms
 import mindspore.nn as nn
-from mindspore import context, Tensor, IndexedSlices, SparseTensor
+from mindspore import context, Tensor, RowTensor, SparseTensor
 from mindspore.ops import composite as C
 
 context.set_context(mode=context.PYNATIVE_MODE, enable_sparse=True)
@@ -36,18 +36,18 @@ class GradWrap(nn.Cell):
         return grad
 
 
-def test_indexed_slices_attr():
-    class IndexedSlicesGetAttr(nn.Cell):
+def test_row_tensor_attr():
+    class RowTensorGetAttr(nn.Cell):
         def __init__(self, dense_shape):
-            super(IndexedSlicesGetAttr, self).__init__()
+            super(RowTensorGetAttr, self).__init__()
             self.dense_shape = dense_shape
         def construct(self, indices, values):
-            x = IndexedSlices(indices, values, self.dense_shape)
-            return x.values(), x.indices(), x.dense_shape()
+            x = RowTensor(indices, values, self.dense_shape)
+            return x.values, x.indices, x.dense_shape
     indices = Tensor([0])
     values = Tensor([[1, 2]], dtype=ms.float32)
-    IndexedSlicesGetAttr((3, 2))(indices, values)
-    GradWrap(IndexedSlicesGetAttr((3, 2)))(indices, values)
+    RowTensorGetAttr((3, 2))(indices, values)
+    GradWrap(RowTensorGetAttr((3, 2)))(indices, values)
 
 
 def test_sparse_tensor_attr():
@@ -57,7 +57,7 @@ def test_sparse_tensor_attr():
             self.dense_shape = (3, 4)
         def construct(self, indices, values):
             x = SparseTensor(indices, values, self.dense_shape)
-            return x.values(), x.indices(), x.dense_shape()
+            return x.values, x.indices, x.dense_shape
 
     indices = Tensor([[0, 1], [1, 2]])
     values = Tensor([1, 2], dtype=ms.float32)