syn code

.gitmodules

@@ -12,4 +12,4 @@
 	url = https://github.com/protocolbuffers/protobuf.git
 [submodule "graphengine"]
 	path = graphengine
-	url = https://gitee.com/mindspore/graphengine.git
+	url = https://gitee.com/ms-incubator/graphengine.git

graphengine

@@ -1 +1 @@
-Subproject commit 43f5d24337bf785251eefae2d810c7d5684194d6
+Subproject commit cfc99f95f722918025b0eaeb93440d92265f09fe

mindspore/ccsrc/operator/ops.cc

@@ -230,7 +230,6 @@ const PrimitivePtr kPrimNotInDict = std::make_shared<Primitive>("not_in_dict");
 const PrimitivePtr kPrimMirror = std::make_shared<Primitive>("_MirrorOperator");
 const PrimitivePtr kPrimVirtualDiv = std::make_shared<Primitive>("_VirtualDiv");
 const PrimitivePtr kPrimVirtualDataset = std::make_shared<Primitive>("_VirtualDataset");
-const PrimitivePtr kPrimAllReduce = std::make_shared<Primitive>("AllReduce");
 
 // Debug ops
 const PrimitivePtr kPrimScalarSummary = std::make_shared<Primitive>("ScalarSummary");

mindspore/ccsrc/operator/ops.h

@@ -234,7 +234,6 @@ extern const PrimitivePtr kPrimInDict;
 extern const PrimitivePtr kPrimNotInDict;
 
 // Comm ops
-extern const PrimitivePtr kPrimAllReduce;
 extern const PrimitivePtr kPrimMirror;
 extern const PrimitivePtr kPrimVirtualDiv;
 extern const PrimitivePtr kPrimVirtualDataset;

mindspore/ccsrc/optimizer/irpass.cc

@@ -48,7 +48,7 @@ namespace irpass {
 OptimizeIRPassLib::OptimizeIRPassLib() {
   arithmetic_simplify_ = MakeSubstitution(ArithmeticSimplify(), "arithmetic_simplify",
                                           {prim::kPrimScalarAdd, prim::kPrimScalarMul, prim::kPrimTensorAdd,
-                                           prim::kPrimAddN, prim::kPrimIdentity, prim::kPrimMomentum, prim::kPrimMul});
+                                           prim::kPrimIdentity, prim::kPrimMomentum, prim::kPrimMul});
   special_op_eliminate_ = MakeSubstitution(SpecialOpEliminater(), "special_op_eliminate",
                                            {prim::kPrimInsertGradientOf, prim::kPrimPrintShapeType,
                                             prim::kPrimGetRefKey, prim::kPrimMirror, prim::kPrimVirtualDiv});

mindspore/ccsrc/optimizer/irpass/arithmetic_simplify.h

@@ -228,86 +228,6 @@ class ConstantDuplicateMul : public AnfVisitor {
   CNodePtr cnode_;
 };
 
-// grad = AllReduce(grad) / worker_number
-// grad = grad + weight * decy
-// ->
-// grad = grad + weight * decy
-// grad = AllReduce(grad) / worker_number
-
-// {prim::kPrimAddN, {prim::kPrimMakeTuple, {prim::kPrimMul, {prim::kPrimAllReduce, X}, Y}, Z}} ->
-// {prim::kPrimMul, {prim::kPrimAllReduce, {prim::kPrimAddN,{prim::kPrimMakeTuple, Z, X}}}, Y}
-class AdjustAllReduceMulAdd : public AnfVisitor {
- public:
-  AnfNodePtr operator()(const OptimizerPtr &, const AnfNodePtr &node) override {
-    Reset();
-    // {prim::kPrimAddN, Zs}
-    if (!IsPrimitiveCNode(node, prim::kPrimAddN)) {
-      return nullptr;
-    }
-    auto addn = node->cast<CNodePtr>();
-    if (addn->size() != 2) {
-      return nullptr;
-    }
-    AnfVisitor::Match(prim::kPrimMakeTuple, {IsNode, IsNode})(addn->input(1));
-    if (x_ == nullptr || y_ == nullptr || z_ == nullptr) {
-      return nullptr;
-    }
-
-    auto addn_op_node = addn->input(0);
-    auto make_tuple_op_node = addn->input(1)->cast<CNodePtr>()->input(0);
-    auto fg = node->func_graph();
-    AnfNodePtr tuple = NewCNode({make_tuple_op_node, z_, x_}, fg);
-    AnfNodePtr add = NewCNode({addn_op_node, tuple}, fg);
-    AnfNodePtr all_reduce = NewCNode({all_reduce_, add}, fg);
-    return NewCNode({mul_, all_reduce, y_}, fg);
-  }
-
-  void Visit(const AnfNodePtr &node) override {
-    if (level_ == 0) {
-      level_ = 1;
-      is_reduce_match_ = false;
-      // {prim::kPrimMul, {prim::kPrimAllReduce, X}, Y}
-      AnfVisitor::Match(prim::kPrimMul)(node);
-      level_ = 0;
-      if (is_reduce_match_) {
-        mul_ = node->cast<CNodePtr>()->input(0);
-        y_ = tmp_;
-      } else {
-        z_ = node;
-      }
-    }
-
-    if (level_ == 1) {
-      // {prim::kPrimAllReduce, X}
-      if (IsPrimitiveCNode(node, prim::kPrimAllReduce)) {
-        auto cnode = node->cast<CNodePtr>();
-        if (cnode->size() > 1) {
-          all_reduce_ = cnode->input(0);
-          x_ = cnode->input(1);
-          is_reduce_match_ = true;
-        }
-      } else {
-        tmp_ = node;
-      }
-    }
-  }
-
-  void Reset() {
-    level_ = 0;
-    is_reduce_match_ = false;
-    x_ = nullptr;
-    y_ = nullptr;
-    z_ = nullptr;
-    tmp_ = nullptr;
-  }
-
- private:
-  int level_{0};
-  bool is_reduce_match_{false};
-  AnfNodePtr x_{nullptr}, y_{nullptr}, z_{nullptr}, tmp_{nullptr};
-  AnfNodePtr all_reduce_{nullptr}, mul_{nullptr};
-};
-
 class ArithmeticSimplify {
  public:
   ArithmeticSimplify()
@@ -323,7 +243,6 @@ class ArithmeticSimplify {
     eliminaters_.emplace_back(identity_);
     eliminaters_.emplace_back(opt_update_zero_tensor_);
     eliminaters_.emplace_back(constant_duplicate_mul_);
-    eliminaters_.emplace_back(adjust_allreduce_mul_add_);
   }
   ~ArithmeticSimplify() = default;
 
@@ -345,7 +264,6 @@ class ArithmeticSimplify {
   PrimEliminater identity_;
   OptUpdateZeroTensor opt_update_zero_tensor_;
   ConstantDuplicateMul constant_duplicate_mul_;
-  AdjustAllReduceMulAdd adjust_allreduce_mul_add_;
   std::vector<TransformFuncType> eliminaters_{};
 };
 }  // namespace irpass

mindspore/ccsrc/transform/convert.cc

@@ -96,7 +96,6 @@ const char kNameConfusionMatrix[] = "ConfusionMatrix";
 const char kNameResizeNearestNeighborD[] = "ResizeNearestNeighbor";
 const char kNameResizeNearestNeighborGrad[] = "ResizeNearestNeighborGrad";
 const char kNameApplyAdam[] = "Adam";
-const char kNameExtractImagePatches[] = "ExtractImagePatches";
 const char kNameReLU6[] = "ReLU6";
 const char kNameReLU6Grad[] = "ReLU6Grad";
 const char kNameElu[] = "Elu";
@@ -111,8 +110,6 @@ const char kNameSigmoidCrossEntropyWithLogits[] = "SigmoidCrossEntropyWithLogits
 const char kNameSigmoidCrossEntropyWithLogitsGrad[] = "SigmoidCrossEntropyWithLogitsGrad";
 const char kNameScatterNdD[] = "ScatterNd";
 const char kNamePadD[] = "Pad";
-const char kNameMirrorPad[] = "MirrorPad";
-const char kNameMirrorPadGrad[] = "MirrorPadGrad";
 const char kNameGatherNd[] = "GatherNd";
 const char kNameArgmax[] = "Argmax";
 const char kNameArgmin[] = "Argmin";
@@ -176,6 +173,7 @@ const char kNameAbsGrad[] = "AbsGrad";
 const char kNameBinaryCrossEntropy[] = "BinaryCrossEntropy";
 const char kNameBinaryCrossEntropyGrad[] = "BinaryCrossEntropyGrad";
 const char kNameSparseApplyAdagrad[] = "SparseApplyAdagrad";
+const char kNameSparseApplyFtrlD[] = "SparseApplyFtrlD";
 const char kNameAcosh[] = "Acosh";
 const char kNameFloorMod[] = "FloorMod";
 const char kNameSpaceToDepth[] = "SpaceToDepth";
@@ -204,7 +202,7 @@ std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_ma
     {string(kNameMaxPool), ADPT_DESC(MaxPool)},
     {string(kNameAvgPool), ADPT_DESC(AvgPool)},
     {string(kNameMaxPoolWithArgmax), ADPT_DESC(MaxPoolWithArgmax)},
-    {string(kNameTopK), ADPT_DESC(TopKV2)},
+    {string(kNameTopK), ADPT_DESC(TopK)},
     {string(kNamePack), ADPT_DESC(Pack)},
     {string(kNameUnpack), ADPT_DESC(Unpack)},
     {string(kNameSplitD), ADPT_DESC(SplitD)},
@@ -215,7 +213,6 @@ std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_ma
     {string(kNameMaxPoolGrad), ADPT_DESC(MaxPoolGrad)},
     {string(kNameAvgPoolGrad), ADPT_DESC(AvgPoolGrad)},
     {string(kNameMaxPoolGradWithArgmax), ADPT_DESC(MaxPoolGradWithArgmax)},
-    {string(kNameExtractImagePatches), ADPT_DESC(ExtractImagePatches)},
     {prim::kPrimAssign->name(), ADPT_DESC(Assign)},
     {prim::kPrimStateSetItem->name(), ADPT_DESC(Assign)},
     {prim::kPrimReluGrad->name(), ADPT_DESC(ReluGrad)},
@@ -240,15 +237,15 @@ std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_ma
     {string(kNameSquare), ADPT_DESC(Square)},
     {prim::kPrimTanh->name(), ADPT_DESC(Tanh)},
     {prim::kPrimTanhGrad->name(), ADPT_DESC(TanhGrad)},
-    {string(kNameResizeNearestNeighborD), ADPT_DESC(ResizeNearestNeighborD)},
-    {string(kNameResizeNearestNeighborGrad), ADPT_DESC(ResizeNearestNeighborGrad)},
+    {string(kNameResizeNearestNeighborD), ADPT_DESC(ResizeNearestNeighborV2D)},
+    {string(kNameResizeNearestNeighborGrad), ADPT_DESC(ResizeNearestNeighborV2Grad)},
     {string(kNameApplyAdam), ADPT_DESC(ApplyAdam)},
     {string(kNameReLU6), ADPT_DESC(Relu6)},
     {string(kNameReLU6Grad), ADPT_DESC(Relu6Grad)},
     {string(kNameElu), ADPT_DESC(Elu)},
     {string(kNameEluGrad), ADPT_DESC(EluGrad)},
-    {string(kNameResizeBilinearGrad), ADPT_DESC(ResizeBilinearGrad)},
-    {string(kNameResizeBilinear), ADPT_DESC(ResizeBilinearD)},
+    {string(kNameResizeBilinearGrad), ADPT_DESC(ResizeBilinearV2Grad)},
+    {string(kNameResizeBilinear), ADPT_DESC(ResizeBilinearV2D)},
     {string(kNameZerosLike), ADPT_DESC(ZerosLike)},
     {string(kNameOnesLike), ADPT_DESC(OnesLike)},
     {string(kNameScatterNdUpdate), ADPT_DESC(ScatterNdUpdate)},
@@ -260,8 +257,6 @@ std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_ma
     {string(kNameSigmoidCrossEntropyWithLogitsGrad), ADPT_DESC(SigmoidCrossEntropyWithLogitsGrad)},
     {string(kNameScatterNdD), ADPT_DESC(ScatterNdD)},
     {string(kNamePadD), ADPT_DESC(PadD)},
-    {string(kNameMirrorPad), ADPT_DESC(MirrorPad)},
-    {string(kNameMirrorPadGrad), ADPT_DESC(MirrorPadGrad)},
     {string(kNameGatherNd), ADPT_DESC(GatherNd)},
     {string(kNameArgmax), ADPT_DESC(ArgMaxD)},
     {string(kNameArgmin), ADPT_DESC(ArgMinD)},
@@ -329,7 +324,7 @@ std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_ma
     {prim::kPrimMinimum->name(), ADPT_DESC(Minimum)},
     {prim::kPrimSelect->name(), ADPT_DESC(Select)},
     {string(kNameLessEqual), ADPT_DESC(LessEqual)},
-    {prim::kPrimLogSoftmax->name(), ADPT_DESC(LogSoftmax)},
+    {prim::kPrimLogSoftmax->name(), ADPT_DESC(LogSoftmaxV2)},
     {string(kNameTruncatedNormal), ADPT_DESC(TruncatedNormal)},
     {string(kNameStridedSliceGrad), ADPT_DESC(StridedSliceGrad)},
     {prim::kPrimGelu->name(), ADPT_DESC(Gelu)},
@@ -363,7 +358,7 @@ std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_ma
     {prim::kPrimMatMul->name(), ADPT_DESC(MatMul)},
 
     {string(kNameConst), ADPT_DESC(Constant, Const)},
-    {string(kNameSoftmax), ADPT_DESC(Softmax)},
+    {string(kNameSoftmax), ADPT_DESC(SoftmaxV2)},
     {string(kNameSoftmaxGrad), ADPT_DESC(SoftmaxGrad)},
     {string(kNameParam), ADPT_DESC(Data)},
     {string(kNameROIAlign), ADPT_DESC(ROIAlign)},
@@ -373,6 +368,7 @@ std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_ma
     {string(kNameBinaryCrossEntropy), ADPT_DESC(BinaryCrossEntropy)},
     {string(kNameBinaryCrossEntropyGrad), ADPT_DESC(BinaryCrossEntropyGrad)},
     {string(kNameSparseApplyAdagrad), ADPT_DESC(SparseApplyAdagradD)},
+    {string(kNameSparseApplyFtrlD), ADPT_DESC(SparseApplyFtrlD)},
     {string(kNameAcosh), ADPT_DESC(Acosh)},
     {string(kNameFloorMod), ADPT_DESC(FloorMod)},
     {string(kNameSpaceToDepth), ADPT_DESC(SpaceToDepth)},
@@ -1126,8 +1122,8 @@ void DfGraphConvertor::UpdateDataOpDesc(const AnfNodePtr &it, const OperatorPtr
   if (desc == nullptr) {
     MS_LOG(ERROR) << "Update data op descriptor failed! TensorDesc is null.";
   } else {
-    (void)std::static_pointer_cast<Data>(op)->update_input_desc_data(*desc);
-    (void)std::static_pointer_cast<Data>(op)->update_output_desc_out(*desc);
+    (void)std::static_pointer_cast<Data>(op)->update_input_desc_x(*desc);
+    (void)std::static_pointer_cast<Data>(op)->update_output_desc_y(*desc);
   }
 }
 

mindspore/ccsrc/transform/op_declare.cc

@@ -138,11 +138,10 @@ OUTPUT_MAP(ApplyMomentum) = {{0, OUTPUT_DESC(var)}};
 INPUT_MAP(Summary) = {{2, INPUT_DESC(x)}};
 ATTR_MAP(Summary) = EMPTY_ATTR_MAP;
 
-// data
+// Data
 INPUT_MAP(Data) = EMPTY_INPUT_MAP;
 ATTR_MAP(Data) = EMPTY_ATTR_MAP;
 
-// resnet ops in ge
 // BatchNorm
 INPUT_MAP(BatchNorm) = {{1, INPUT_DESC(x)},
                         {2, INPUT_DESC(scale)},
@@ -193,10 +192,9 @@ ATTR_MAP(PRelu) = EMPTY_ATTR_MAP;
 OUTPUT_MAP(PRelu) = {{0, OUTPUT_DESC(y)}};
 
 // PReluGrad
-INPUT_MAP(PReluGrad) = {
-  {1, INPUT_DESC(input_gradients)}, {2, INPUT_DESC(input_features)}, {3, INPUT_DESC(input_weights)}};
+INPUT_MAP(PReluGrad) = {{1, INPUT_DESC(grads)}, {2, INPUT_DESC(features)}, {3, INPUT_DESC(weights)}};
 ATTR_MAP(PReluGrad) = EMPTY_ATTR_MAP;
-OUTPUT_MAP(PReluGrad) = {{0, OUTPUT_DESC(output_backprops_dx)}, {1, OUTPUT_DESC(output_backprops_da)}};
+OUTPUT_MAP(PReluGrad) = {{0, OUTPUT_DESC(dx)}, {1, OUTPUT_DESC(da)}};
 
 // Sigmoid
 INPUT_MAP(Sigmoid) = {{1, INPUT_DESC(x)}};
@@ -241,12 +239,12 @@ ATTR_MAP(CumsumD) = {{"exclusive", ATTR_DESC(exclusive, AnyTraits<bool>())},
                      {"reverse", ATTR_DESC(reverse, AnyTraits<bool>())}};
 OUTPUT_MAP(CumsumD) = {{0, OUTPUT_DESC(y)}};
 
-// softmax
-INPUT_MAP(Softmax) = {{1, INPUT_DESC(x)}};
-ATTR_MAP(Softmax) = {
-  {"axis", ATTR_DESC(axis, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
+// SoftmaxV2
+INPUT_MAP(SoftmaxV2) = {{1, INPUT_DESC(x)}};
+ATTR_MAP(SoftmaxV2) = {
+  {"axis", ATTR_DESC(axes, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
 };
-OUTPUT_MAP(Softmax) = {{0, OUTPUT_DESC(y)}};
+OUTPUT_MAP(SoftmaxV2) = {{0, OUTPUT_DESC(y)}};
 
 // SoftmaxGrad
 INPUT_MAP(SoftmaxGrad) = {{1, INPUT_DESC(softmax)}, {2, INPUT_DESC(grad_softmax)}};
@@ -268,17 +266,22 @@ INPUT_MAP(GatherV2) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(indices)}, {3, INPUT_D
 ATTR_MAP(GatherV2) = EMPTY_ATTR_MAP;
 OUTPUT_MAP(GatherV2) = {{0, OUTPUT_DESC(y)}};
 
+// ReduceSum
+INPUT_MAP(ReduceSum) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(axes)}};
+ATTR_MAP(ReduceSum) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits<bool>())}};
+OUTPUT_MAP(ReduceSum) = {{0, OUTPUT_DESC(y)}};
+
 // ReduceSumD
 INPUT_MAP(ReduceSumD) = {{1, INPUT_DESC(x)}};
 INPUT_ATTR_MAP(ReduceSumD) = {
-  {2, ATTR_DESC(axis, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
+  {2, ATTR_DESC(axes, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
 ATTR_MAP(ReduceSumD) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits<bool>())}};
 OUTPUT_MAP(ReduceSumD) = {{0, OUTPUT_DESC(y)}};
 
 // ReduceProdD
 INPUT_MAP(ReduceProdD) = {{1, INPUT_DESC(x)}};
 INPUT_ATTR_MAP(ReduceProdD) = {
-  {2, ATTR_DESC(axis, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
+  {2, ATTR_DESC(axes, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
 ATTR_MAP(ReduceProdD) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits<bool>())}};
 OUTPUT_MAP(ReduceProdD) = {{0, OUTPUT_DESC(y)}};
 
@@ -289,7 +292,7 @@ ATTR_MAP(CumprodD) = {{"exclusive", ATTR_DESC(exclusive, AnyTraits<bool>())},
                       {"reverse", ATTR_DESC(reverse, AnyTraits<bool>())}};
 OUTPUT_MAP(CumprodD) = {{0, OUTPUT_DESC(y)}};
 
-// SoftmaxCrossEntropyWithLogits/
+// SoftmaxCrossEntropyWithLogits
 INPUT_MAP(SoftmaxCrossEntropyWithLogits) = {{1, INPUT_DESC(features)}, {2, INPUT_DESC(labels)}};
 ATTR_MAP(SoftmaxCrossEntropyWithLogits) = EMPTY_ATTR_MAP;
 OUTPUT_MAP(SoftmaxCrossEntropyWithLogits) = {{0, OUTPUT_DESC(loss)}, {1, OUTPUT_DESC(backprop)}};
@@ -301,7 +304,7 @@ INPUT_ATTR_MAP(MeanGrad) = {{2, ATTR_DESC(mean_grad_output_shape_value, kOpForma
 ATTR_MAP(MeanGrad) = {{"mode", ATTR_DESC(mode, AnyTraits<int64_t>())}};
 
 INPUT_MAP(SliceD) = {{1, INPUT_DESC(x)}};
-INPUT_ATTR_MAP(SliceD) = {{2, ATTR_DESC(begin, AnyTraits<int>(), AnyTraits<std::vector<int64_t>>())},
+INPUT_ATTR_MAP(SliceD) = {{2, ATTR_DESC(offsets, AnyTraits<int>(), AnyTraits<std::vector<int64_t>>())},
                           {3, ATTR_DESC(size, AnyTraits<int>(), AnyTraits<std::vector<int64_t>>())}};
 ATTR_MAP(SliceD) = EMPTY_ATTR_MAP;
 OUTPUT_MAP(SliceD) = {{0, OUTPUT_DESC(y)}};
@@ -406,42 +409,10 @@ ATTR_MAP(BoundingBoxDecode) = {
 };
 OUTPUT_MAP(BoundingBoxDecode) = {{0, OUTPUT_DESC(bboxes)}};
 
-#ifdef VALID_CODE
-
-// Less
-INPUT_MAP(Less) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(y)}};
-ATTR_MAP(Less) = EMPTY_ATTR_MAP;
-OUTPUT_MAP(Less) = {{0, OUTPUT_DESC(z)}};
-
-// Cast
-INPUT_MAP(Cast) = {{1, INPUT_DESC(x)}};
-INPUT_ATTR_MAP(Cast) = {{2, ATTR_DESC(dst_type, AnyTraits<GEType>())}};
-ATTR_MAP(Cast) = {{"Truncate", ATTR_DESC(truncate, AnyTraits<bool>())}};
-OUTPUT_MAP(Cast) = {{0, OUTPUT_DESC(y)}};
-
-// Minimum
-INPUT_MAP(Minimum) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(y)}};
-ATTR_MAP(Minimum) = {{"alpha", ATTR_DESC(alpha, AnyTraits<float>())}, {"beta", ATTR_DESC(beta, AnyTraits<float>())}};
-OUTPUT_MAP(Minimum) = {{0, OUTPUT_DESC(z)}};
-
-// Sub
-INPUT_MAP(Sub) = {{1, INPUT_DESC(x1)}, {2, INPUT_DESC(x2)}};
-ATTR_MAP(Sub) = {{"alpha", ATTR_DESC(alpha, AnyTraits<float>())}, {"beta", ATTR_DESC(beta, AnyTraits<float>())}};
-
-#endif
-
-// TopKV2
-INPUT_MAP(TopKV2) = {
-  {1, INPUT_DESC(input)},
-  {2, INPUT_DESC(k)},
-};
-
-ATTR_MAP(TopKV2) = {{"T", ATTR_DESC(T, AnyTraits<GEType>())}, {"sorted", ATTR_DESC(sorted, AnyTraits<bool>())}};
-
-OUTPUT_MAP(TopKV2) = {
-  {0, OUTPUT_DESC(values)},
-  {1, OUTPUT_DESC(indices)},
-};
+// TopK
+INPUT_MAP(TopK) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(k)}};
+ATTR_MAP(TopK) = {{"sorted", ATTR_DESC(sorted, AnyTraits<bool>())}};
+OUTPUT_MAP(TopK) = {{0, OUTPUT_DESC(values)}, {1, OUTPUT_DESC(indices)}};
 
 // Multiply
 INPUT_MAP(Multiply) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(y)}};
@@ -481,7 +452,7 @@ ATTR_MAP(Iou) = {{"mode", ATTR_DESC(mode, AnyTraits<std::string>())}};
 OUTPUT_MAP(Iou) = {{0, OUTPUT_DESC(overlap)}};
 
 // ResizeNearestNeighborD
-INPUT_MAP(ResizeNearestNeighborD) = {{1, INPUT_DESC(images)}};
+INPUT_MAP(ResizeNearestNeighborD) = {{1, INPUT_DESC(x)}};
 ATTR_MAP(ResizeNearestNeighborD) = {
   {"size", ATTR_DESC(size, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
   {"align_corners", ATTR_DESC(align_corners, AnyTraits<bool>())}};
@@ -506,9 +477,9 @@ OUTPUT_MAP(ApplyAdam) = {{0, OUTPUT_DESC(var)}};
 #endif
 
 // Relu6
-INPUT_MAP(Relu6) = {{1, INPUT_DESC(features)}};
+INPUT_MAP(Relu6) = {{1, INPUT_DESC(x)}};
 ATTR_MAP(Relu6) = EMPTY_ATTR_MAP;
-OUTPUT_MAP(Relu6) = {{0, OUTPUT_DESC(activations)}};
+OUTPUT_MAP(Relu6) = {{0, OUTPUT_DESC(y)}};
 
 // Relu6Grad
 INPUT_MAP(Relu6Grad) = {{1, INPUT_DESC(gradients)}, {2, INPUT_DESC(features)}};
@@ -520,8 +491,8 @@ INPUT_MAP(ResizeBilinearGrad) = {{1, INPUT_DESC(grads)}, {2, INPUT_DESC(original
 ATTR_MAP(ResizeBilinearGrad) = {{"align_corners", ATTR_DESC(align_corners, AnyTraits<bool>())}};
 OUTPUT_MAP(ResizeBilinearGrad) = {{0, OUTPUT_DESC(y)}};
 
-// ResizeBilinear
-INPUT_MAP(ResizeBilinearD) = {{1, INPUT_DESC(images)}};
+// ResizeBilinearD
+INPUT_MAP(ResizeBilinearD) = {{1, INPUT_DESC(x)}};
 ATTR_MAP(ResizeBilinearD) = {
   {"size", ATTR_DESC(size, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
   {"align_corners", ATTR_DESC(align_corners, AnyTraits<bool>())}};
@@ -544,9 +515,9 @@ OUTPUT_MAP(NMSWithMask) = {
   {0, OUTPUT_DESC(selected_boxes)}, {1, OUTPUT_DESC(selected_idx)}, {2, OUTPUT_DESC(selected_mask)}};
 
 // Unpack
-INPUT_MAP(Unpack) = {{1, INPUT_DESC(value)}};
+INPUT_MAP(Unpack) = {{1, INPUT_DESC(x)}};
 ATTR_MAP(Unpack) = {{"axis", ATTR_DESC(axis, AnyTraits<int>())}, {"num", ATTR_DESC(num, AnyTraits<int>())}};
-DYN_OUTPUT_MAP(Unpack) = {{0, DYN_OUTPUT_DESC(output)}};
+DYN_OUTPUT_MAP(Unpack) = {{0, DYN_OUTPUT_DESC(y)}};
 
 // ScatterNdUpdate
 INPUT_MAP(ScatterNdUpdate) = {{1, INPUT_DESC(var)}, {2, INPUT_DESC(indices)}, {3, INPUT_DESC(updates)}};
@@ -579,8 +550,8 @@ INPUT_MAP(SigmoidCrossEntropyWithLogitsGrad) = {
 ATTR_MAP(SigmoidCrossEntropyWithLogitsGrad) = EMPTY_ATTR_MAP;
 OUTPUT_MAP(SigmoidCrossEntropyWithLogitsGrad) = {{0, OUTPUT_DESC(gradient)}};
 
-// ScatterNd
-INPUT_MAP(ScatterNdD) = {{1, INPUT_DESC(indices)}, {2, INPUT_DESC(updates)}};
+// ScatterNdD
+INPUT_MAP(ScatterNdD) = {{1, INPUT_DESC(indices)}, {2, INPUT_DESC(x)}};
 INPUT_ATTR_MAP(ScatterNdD) = {
   {3, ATTR_DESC(shape, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
 ATTR_MAP(ScatterNdD) = EMPTY_ATTR_MAP;
@@ -591,24 +562,14 @@ INPUT_MAP(PadD) = {{1, INPUT_DESC(x)}};
 ATTR_MAP(PadD) = {{"paddings", ATTR_DESC(paddings, AnyTraits<std::vector<std::vector<int64_t>>>())}};
 OUTPUT_MAP(PadD) = {{0, OUTPUT_DESC(y)}};
 
-// MirrorPad
-INPUT_MAP(MirrorPad) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(paddings)}};
-ATTR_MAP(MirrorPad) = {{"mode", ATTR_DESC(mode, AnyTraits<std::string>())}};
-OUTPUT_MAP(MirrorPad) = {{0, OUTPUT_DESC(y)}};
-
-// MirrorPadGrad
-INPUT_MAP(MirrorPadGrad) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(paddings)}};
-ATTR_MAP(MirrorPadGrad) = {{"mode", ATTR_DESC(mode, AnyTraits<std::string>())}};
-OUTPUT_MAP(MirrorPadGrad) = {{0, OUTPUT_DESC(y)}};
-
 // GatherNd
-INPUT_MAP(GatherNd) = {{1, INPUT_DESC(x1)}, {2, INPUT_DESC(x2)}};
+INPUT_MAP(GatherNd) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(indices)}};
 ATTR_MAP(GatherNd) = EMPTY_ATTR_MAP;
 OUTPUT_MAP(GatherNd) = {{0, OUTPUT_DESC(y)}};
 
 // ROIAlign
 INPUT_MAP(ROIAlign) = {{1, INPUT_DESC(features)}, {2, INPUT_DESC(rois)}};
-OUTPUT_MAP(ROIAlign) = {{0, OUTPUT_DESC(output)}};
+OUTPUT_MAP(ROIAlign) = {{0, OUTPUT_DESC(y)}};
 ATTR_MAP(ROIAlign) = {{"pooled_height", ATTR_DESC(pooled_height, AnyTraits<int>())},
                       {"pooled_width", ATTR_DESC(pooled_width, AnyTraits<int>())},
                       {"spatial_scale", ATTR_DESC(spatial_scale, AnyTraits<float>())},
@@ -627,13 +588,13 @@ ATTR_MAP(ROIAlignGrad) = {
 // ArgMaxD
 INPUT_MAP(ArgMaxD) = {{1, INPUT_DESC(x)}};
 ATTR_MAP(ArgMaxD) = {{"axis", ATTR_DESC(dimension, AnyTraits<int>())},
-                     {"output_type", ATTR_DESC(output_type, AnyTraits<GEType>())}};
+                     {"output_type", ATTR_DESC(dtype, AnyTraits<GEType>())}};
 OUTPUT_MAP(ArgMaxD) = {{0, OUTPUT_DESC(y)}};
 
 // ArgMinD
 INPUT_MAP(ArgMinD) = {{1, INPUT_DESC(x)}};
 ATTR_MAP(ArgMinD) = {{"axis", ATTR_DESC(dimension, AnyTraits<int>())},
-                     {"output_type", ATTR_DESC(output_type, AnyTraits<GEType>())}};
+                     {"output_type", ATTR_DESC(dtype, AnyTraits<GEType>())}};
 OUTPUT_MAP(ArgMinD) = {{0, OUTPUT_DESC(y)}};
 
 // ArgMaxWithValue
@@ -648,17 +609,15 @@ ATTR_MAP(ArgMinWithValue) = {{"axis", ATTR_DESC(dimension, AnyTraits<int>())},
                              {"keep_dims", ATTR_DESC(keep_dims, AnyTraits<bool>())}};
 OUTPUT_MAP(ArgMinWithValue) = {{0, OUTPUT_DESC(indice)}, {1, OUTPUT_DESC(values)}};
 
-// ReduceAllD
-INPUT_MAP(ReduceAllD) = {{1, INPUT_DESC(x)}};
-INPUT_ATTR_MAP(ReduceAllD) = {
-  {2, ATTR_DESC(axis, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
-ATTR_MAP(ReduceAllD) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits<bool>())}};
-OUTPUT_MAP(ReduceAllD) = {{0, OUTPUT_DESC(y)}};
+// ReduceAll
+INPUT_MAP(ReduceAll) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(axes)}};
+ATTR_MAP(ReduceAll) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits<bool>())}};
+OUTPUT_MAP(ReduceAll) = {{0, OUTPUT_DESC(y)}}
 
 // ReduceMeanD
 INPUT_MAP(ReduceMeanD) = {{1, INPUT_DESC(x)}};
 INPUT_ATTR_MAP(ReduceMeanD) = {
-  {2, ATTR_DESC(axis, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
+  {2, ATTR_DESC(axes, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
 ATTR_MAP(ReduceMeanD) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits<bool>())}};
 OUTPUT_MAP(ReduceMeanD) = {{0, OUTPUT_DESC(y)}};
 
@@ -725,11 +684,12 @@ INPUT_MAP(BiasAddGrad) = {{1, INPUT_DESC(x)}};
 ATTR_MAP(BiasAddGrad) = {{"data_format", ATTR_DESC(data_format, AnyTraits<std::string>())}};
 OUTPUT_MAP(BiasAddGrad) = {{0, OUTPUT_DESC(y)}};
 
-// maxpoolgrad
+// MaxPoolGrad
 INPUT_MAP(MaxPoolGrad) = {{1, INPUT_DESC(x1)}, {2, INPUT_DESC(x2)}, {3, INPUT_DESC(grad)}};
 ATTR_MAP(MaxPoolGrad) = {{"ksize", ATTR_DESC(ksize, AnyTraits<int>(), AnyTraits<std::vector<int64_t>>())},
                          {"strides", ATTR_DESC(strides, AnyTraits<int>(), AnyTraits<std::vector<int64_t>>())},
-                         {"padding", ATTR_DESC(padding, AnyTraits<std::string>())}};
+                         {"padding", ATTR_DESC(padding, AnyTraits<std::string>())},
+                         {"data_format", ATTR_DESC(data_format, AnyTraits<std::string>())}};
 OUTPUT_MAP(MaxPoolGrad) = {{0, OUTPUT_DESC(y)}};
 
 // avgpoolgrad
@@ -748,20 +708,16 @@ ATTR_MAP(MaxPoolWithArgmax) = {{"ksize", ATTR_DESC(ksize, AnyTraits<int>(), AnyT
 OUTPUT_MAP(MaxPoolWithArgmax) = {{0, OUTPUT_DESC(y)}, {1, OUTPUT_DESC(argmax)}};
 
 // MaxPoolGradWithArgmax
-INPUT_MAP(MaxPoolGradWithArgmax) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(grad)}, {3, INPUT_DESC(argmax)}};
+INPUT_MAP(MaxPoolGradWithArgmax) = {
+  {1, INPUT_DESC(x)},
+  {2, INPUT_DESC(grad)},
+  {3, INPUT_DESC(argmax)},
+};
 ATTR_MAP(MaxPoolGradWithArgmax) = {{"ksize", ATTR_DESC(ksize, AnyTraits<int>(), AnyTraits<std::vector<int64_t>>())},
                                    {"strides", ATTR_DESC(strides, AnyTraits<int>(), AnyTraits<std::vector<int64_t>>())},
                                    {"padding", ATTR_DESC(padding, AnyTraits<std::string>())}};
 OUTPUT_MAP(MaxPoolGradWithArgmax) = {{0, OUTPUT_DESC(y)}};
 
-// ExtractImagePatches
-INPUT_MAP(ExtractImagePatches) = {{1, INPUT_DESC(images)}};
-ATTR_MAP(ExtractImagePatches) = {{"ksizes", ATTR_DESC(ksizes, AnyTraits<int>(), AnyTraits<std::vector<int64_t>>())},
-                                 {"strides", ATTR_DESC(strides, AnyTraits<int>(), AnyTraits<std::vector<int64_t>>())},
-                                 {"rates", ATTR_DESC(rates, AnyTraits<int>(), AnyTraits<std::vector<int64_t>>())},
-                                 {"padding", ATTR_DESC(padding, AnyTraits<std::string>())}};
-OUTPUT_MAP(ExtractImagePatches) = {{0, OUTPUT_DESC(y)}};
-
 // Conv2D
 INPUT_MAP(Conv2D) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(filter)}};
 ATTR_MAP(Conv2D) = {
@@ -772,9 +728,9 @@ ATTR_MAP(Conv2D) = {
 OUTPUT_MAP(Conv2D) = {{0, OUTPUT_DESC(y)}};
 
 // Conv2DBackpropInputD
-INPUT_MAP(Conv2DBackpropInputD) = {{1, INPUT_DESC(out_backprop)}, {2, INPUT_DESC(filters)}};
+INPUT_MAP(Conv2DBackpropInputD) = {{1, INPUT_DESC(out_backprop)}, {2, INPUT_DESC(filter)}};
 INPUT_ATTR_MAP(Conv2DBackpropInputD) = {
-  {3, ATTR_DESC(input_sizes, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
+  {3, ATTR_DESC(input_size, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
 ATTR_MAP(Conv2DBackpropInputD) = {
   {"pad_list", ATTR_DESC(pads, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
   {"stride", ATTR_DESC(strides, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
@@ -785,7 +741,7 @@ OUTPUT_MAP(Conv2DBackpropInputD) = {{0, OUTPUT_DESC(y)}};
 // Conv2DBackpropFilterD
 INPUT_MAP(Conv2DBackpropFilterD) = {{1, INPUT_DESC(out_backprop)}, {2, INPUT_DESC(x)}};
 INPUT_ATTR_MAP(Conv2DBackpropFilterD) = {
-  {3, ATTR_DESC(filter_sizes, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
+  {3, ATTR_DESC(filter_size, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
 ATTR_MAP(Conv2DBackpropFilterD) = {
   {"pad_list", ATTR_DESC(pads, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
   {"stride", ATTR_DESC(strides, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
@@ -796,9 +752,9 @@ OUTPUT_MAP(Conv2DBackpropFilterD) = {{0, OUTPUT_DESC(y)}};
 // DepthwiseConv2D
 INPUT_MAP(DepthwiseConv2D) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(filter)}};
 ATTR_MAP(DepthwiseConv2D) = {
-  {"stride", ATTR_DESC(strides, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
+  {"stride", ATTR_DESC(strides, "pad", AnyTraits<std::vector<int64_t>>())},
   {"pads", ATTR_DESC(pads, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
-  {"dilation", ATTR_DESC(dilations, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
+  {"dilation", ATTR_DESC(dilations, "pad", AnyTraits<std::vector<int64_t>>())},
   {"data_format", ATTR_DESC(data_format, AnyTraits<std::string>())},
 };
 OUTPUT_MAP(DepthwiseConv2D) = {{0, OUTPUT_DESC(y)}};
@@ -808,9 +764,9 @@ INPUT_MAP(DepthwiseConv2DBackpropInputD) = {{2, INPUT_DESC(filter)}, {3, INPUT_D
 INPUT_ATTR_MAP(DepthwiseConv2DBackpropInputD) = {
   {1, ATTR_DESC(input_size, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
 ATTR_MAP(DepthwiseConv2DBackpropInputD) = {
-  {"stride", ATTR_DESC(strides, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
+  {"stride", ATTR_DESC(strides, "pad", AnyTraits<std::vector<int64_t>>())},
   {"pads", ATTR_DESC(pads, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
-  {"dilation", ATTR_DESC(dilations, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
+  {"dilation", ATTR_DESC(dilations, "pad", AnyTraits<std::vector<int64_t>>())},
 };
 OUTPUT_MAP(DepthwiseConv2DBackpropInputD) = {{0, OUTPUT_DESC(input_grad)}};
 
@@ -819,16 +775,16 @@ INPUT_MAP(DepthwiseConv2DBackpropFilterD) = {{1, INPUT_DESC(input)}, {3, INPUT_D
 INPUT_ATTR_MAP(DepthwiseConv2DBackpropFilterD) = {
   {2, ATTR_DESC(filter_size, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
 ATTR_MAP(DepthwiseConv2DBackpropFilterD) = {
-  {"stride", ATTR_DESC(strides, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
+  {"stride", ATTR_DESC(strides, "pad", AnyTraits<std::vector<int64_t>>())},
   {"pads", ATTR_DESC(pads, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
-  {"dilation", ATTR_DESC(dilations, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
+  {"dilation", ATTR_DESC(dilations, "pad", AnyTraits<std::vector<int64_t>>())},
 };
 OUTPUT_MAP(DepthwiseConv2DBackpropFilterD) = {{0, OUTPUT_DESC(filter_grad)}};
 
 // MatMul
 INPUT_MAP(MatMul) = {{1, INPUT_DESC(x1)}, {2, INPUT_DESC(x2)}};
-ATTR_MAP(MatMul) = {{"transpose_a", ATTR_DESC(transpose_a, AnyTraits<bool>())},
-                    {"transpose_b", ATTR_DESC(transpose_b, AnyTraits<bool>())}};
+ATTR_MAP(MatMul) = {{"transpose_a", ATTR_DESC(transpose_x1, AnyTraits<bool>())},
+                    {"transpose_b", ATTR_DESC(transpose_x2, AnyTraits<bool>())}};
 OUTPUT_MAP(MatMul) = {{0, OUTPUT_DESC(y)}};
 
 // Merge
@@ -875,10 +831,10 @@ ATTR_MAP(Sub) = EMPTY_ATTR_MAP;
 OUTPUT_MAP(Sub) = {{0, OUTPUT_DESC(y)}};
 
 // SplitD
-INPUT_MAP(SplitD) = {{1, INPUT_DESC(value)}};
+INPUT_MAP(SplitD) = {{1, INPUT_DESC(x)}};
 ATTR_MAP(SplitD) = {{"axis", ATTR_DESC(split_dim, AnyTraits<int>())},
                     {"output_num", ATTR_DESC(num_split, AnyTraits<int>())}};
-DYN_OUTPUT_MAP(SplitD) = {{0, DYN_OUTPUT_DESC(output)}};
+DYN_OUTPUT_MAP(SplitD) = {{0, DYN_OUTPUT_DESC(y)}};
 
 // Neg
 INPUT_MAP(Neg) = {{1, INPUT_DESC(x)}};
@@ -905,12 +861,12 @@ OUTPUT_MAP(Pack) = {{0, OUTPUT_DESC(y)}};
 
 // ConcatD
 INPUT_MAP(ConcatD) = EMPTY_INPUT_MAP;
-DYN_INPUT_MAP(ConcatD) = {{1, DYN_INPUT_DESC(input_values)}};
+DYN_INPUT_MAP(ConcatD) = {{1, DYN_INPUT_DESC(x)}};
 ATTR_MAP(ConcatD) = {
   {"axis", ATTR_DESC(concat_dim, AnyTraits<int>())},
   {"inputNums", ATTR_DESC(N, AnyTraits<int>())},
 };
-OUTPUT_MAP(ConcatD) = {{0, OUTPUT_DESC(output_data)}};
+OUTPUT_MAP(ConcatD) = {{0, OUTPUT_DESC(y)}};
 
 // Less
 INPUT_MAP(Less) = {{1, INPUT_DESC(x1)}, {2, INPUT_DESC(x2)}};
@@ -945,14 +901,14 @@ OUTPUT_MAP(TanhGrad) = {{0, OUTPUT_DESC(z)}};
 // ReduceMinD
 INPUT_MAP(ReduceMinD) = {{1, INPUT_DESC(x)}};
 INPUT_ATTR_MAP(ReduceMinD) = {
-  {2, ATTR_DESC(axis, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
+  {2, ATTR_DESC(axes, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
 ATTR_MAP(ReduceMinD) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits<bool>())}};
 OUTPUT_MAP(ReduceMinD) = {{0, OUTPUT_DESC(y)}};
 
 // ReduceMaxD
 INPUT_MAP(ReduceMaxD) = {{1, INPUT_DESC(x)}};
 INPUT_ATTR_MAP(ReduceMaxD) = {
-  {2, ATTR_DESC(axis, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
+  {2, ATTR_DESC(axes, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
 ATTR_MAP(ReduceMaxD) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits<bool>())}};
 OUTPUT_MAP(ReduceMaxD) = {{0, OUTPUT_DESC(y)}};
 
@@ -1037,11 +993,11 @@ INPUT_MAP(LessEqual) = {{1, INPUT_DESC(x1)}, {2, INPUT_DESC(x2)}};
 ATTR_MAP(LessEqual) = EMPTY_ATTR_MAP;
 OUTPUT_MAP(LessEqual) = {{0, OUTPUT_DESC(y)}};
 
-// LogSoftmax
-INPUT_MAP(LogSoftmax) = {{1, INPUT_DESC(logits)}};
-ATTR_MAP(LogSoftmax) = {
-  {"axis", ATTR_DESC(axis, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
-OUTPUT_MAP(LogSoftmax) = {{0, OUTPUT_DESC(logsoftmax)}};
+// LogSoftmaxV2
+INPUT_MAP(LogSoftmaxV2) = {{1, INPUT_DESC(logits)}};
+ATTR_MAP(LogSoftmaxV2) = {
+  {"axis", ATTR_DESC(axes, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
+OUTPUT_MAP(LogSoftmaxV2) = {{0, OUTPUT_DESC(logsoftmax)}};
 
 // RandomChoiceWithMask
 INPUT_MAP(RandomChoiceWithMask) = {{1, INPUT_DESC(x)}};
@@ -1123,8 +1079,8 @@ OUTPUT_MAP(LayerNormGrad) = {{0, OUTPUT_DESC(pd_x)}, {1, OUTPUT_DESC(pd_gamma)},
 
 // BatchMatMul
 INPUT_MAP(BatchMatMul) = {{1, INPUT_DESC(x1)}, {2, INPUT_DESC(x2)}};
-ATTR_MAP(BatchMatMul) = {{"transpose_x1", ATTR_DESC(adj_x, AnyTraits<bool>())},
-                         {"transpose_x2", ATTR_DESC(adj_y, AnyTraits<bool>())}};
+ATTR_MAP(BatchMatMul) = {{"transpose_x1", ATTR_DESC(adj_x1, AnyTraits<bool>())},
+                         {"transpose_x2", ATTR_DESC(adj_x2, AnyTraits<bool>())}};
 OUTPUT_MAP(BatchMatMul) = {{0, OUTPUT_DESC(y)}};
 
 // DropoutDoMask
@@ -1175,6 +1131,19 @@ ATTR_MAP(SparseApplyAdagradD) = {{"lr", ATTR_DESC(lr, AnyTraits<float>())},
                                  {"use_locking", ATTR_DESC(use_locking, AnyTraits<bool>())}};
 OUTPUT_MAP(SparseApplyAdagradD) = {{0, OUTPUT_DESC(var)}};
 
+// SparseApplyFtrlD
+INPUT_MAP(SparseApplyFtrlD) = {{1, INPUT_DESC(var)},
+                               {2, INPUT_DESC(accum)},
+                               {3, INPUT_DESC(linear)},
+                               {4, INPUT_DESC(grad)},
+                               {5, INPUT_DESC(indices)}};
+ATTR_MAP(SparseApplyFtrlD) = {{"use_locking", ATTR_DESC(use_locking, AnyTraits<bool>())},
+                              {"lr", ATTR_DESC(lr, AnyTraits<float>())},
+                              {"l1", ATTR_DESC(l1, AnyTraits<float>())},
+                              {"l2", ATTR_DESC(l2, AnyTraits<float>())},
+                              {"lr_power", ATTR_DESC(lr_power, AnyTraits<float>())}};
+OUTPUT_MAP(SparseApplyFtrlD) = {{0, OUTPUT_DESC(var)}};
+
 // SpaceToDepth
 INPUT_MAP(SpaceToDepth) = {{1, INPUT_DESC(x)}};
 ATTR_MAP(SpaceToDepth) = {{"block_size", ATTR_DESC(block_size, AnyTraits<int64_t>())}};

mindspore/ccsrc/transform/op_declare.h

@@ -95,8 +95,6 @@ DECLARE_OP_USE_OUTPUT(MaxPoolGradWithArgmax)
 DECLARE_OP_ADAPTER(Conv2D)
 DECLARE_OP_USE_ENUM(Conv2D)
 DECLARE_OP_USE_OUTPUT(Conv2D)
-DECLARE_OP_ADAPTER(ExtractImagePatches)
-DECLARE_OP_USE_OUTPUT(ExtractImagePatches)
 DECLARE_OP_ADAPTER(Conv2DBackpropInputD)
 DECLARE_OP_USE_ENUM(Conv2DBackpropInputD)
 DECLARE_OP_USE_INPUT_ATTR(Conv2DBackpropInputD)
@@ -118,20 +116,20 @@ DECLARE_OP_ADAPTER(Reshape)
 DECLARE_OP_USE_OUTPUT(Reshape)
 DECLARE_OP_ADAPTER(Iou)
 DECLARE_OP_USE_OUTPUT(Iou)
-DECLARE_OP_ADAPTER(ResizeNearestNeighborD)
-DECLARE_OP_USE_OUTPUT(ResizeNearestNeighborD)
-DECLARE_OP_ADAPTER(ResizeNearestNeighborGrad)
-DECLARE_OP_USE_OUTPUT(ResizeNearestNeighborGrad)
+DECLARE_OP_ADAPTER(ResizeNearestNeighborV2D)
+DECLARE_OP_USE_OUTPUT(ResizeNearestNeighborV2D)
+DECLARE_OP_ADAPTER(ResizeNearestNeighborV2Grad)
+DECLARE_OP_USE_OUTPUT(ResizeNearestNeighborV2Grad)
 DECLARE_OP_ADAPTER(ApplyAdam)
 DECLARE_OP_USE_OUTPUT(ApplyAdam)
 DECLARE_OP_ADAPTER(Relu6)
 DECLARE_OP_USE_OUTPUT(Relu6)
 DECLARE_OP_ADAPTER(Relu6Grad)
 DECLARE_OP_USE_OUTPUT(Relu6Grad)
-DECLARE_OP_ADAPTER(ResizeBilinearD)
-DECLARE_OP_USE_OUTPUT(ResizeBilinearD)
-DECLARE_OP_ADAPTER(ResizeBilinearGrad)
-DECLARE_OP_USE_OUTPUT(ResizeBilinearGrad)
+DECLARE_OP_ADAPTER(ResizeBilinearV2D)
+DECLARE_OP_USE_OUTPUT(ResizeBilinearV2D)
+DECLARE_OP_ADAPTER(ResizeBilinearV2Grad)
+DECLARE_OP_USE_OUTPUT(ResizeBilinearV2Grad)
 DECLARE_OP_ADAPTER(ZerosLike)
 DECLARE_OP_USE_OUTPUT(ZerosLike)
 DECLARE_OP_ADAPTER(OnesLike)
@@ -157,10 +155,6 @@ DECLARE_OP_USE_INPUT_ATTR(ScatterNdD)
 DECLARE_OP_USE_OUTPUT(ScatterNdD)
 DECLARE_OP_ADAPTER(PadD)
 DECLARE_OP_USE_OUTPUT(PadD)
-DECLARE_OP_ADAPTER(MirrorPad)
-DECLARE_OP_USE_OUTPUT(MirrorPad)
-DECLARE_OP_ADAPTER(MirrorPadGrad)
-DECLARE_OP_USE_OUTPUT(MirrorPadGrad)
 DECLARE_OP_ADAPTER(BoundingBoxEncode)
 DECLARE_OP_USE_OUTPUT(BoundingBoxEncode)
 DECLARE_OP_ADAPTER(BoundingBoxDecode)
@@ -217,8 +211,8 @@ DECLARE_OP_USE_OUTPUT(Merge)
 DECLARE_OP_ADAPTER(Switch)
 DECLARE_OP_USE_OUTPUT(Switch)
 
-DECLARE_OP_ADAPTER(TopKV2)
-DECLARE_OP_USE_OUTPUT(TopKV2)
+DECLARE_OP_ADAPTER(TopK)
+DECLARE_OP_USE_OUTPUT(TopK)
 
 DECLARE_OP_ADAPTER(RealDiv)
 DECLARE_OP_USE_OUTPUT(RealDiv)
@@ -268,8 +262,8 @@ DECLARE_OP_ADAPTER(Select)
 DECLARE_OP_USE_OUTPUT(Select)
 DECLARE_OP_ADAPTER(LessEqual)
 DECLARE_OP_USE_OUTPUT(LessEqual)
-DECLARE_OP_ADAPTER(LogSoftmax)
-DECLARE_OP_USE_OUTPUT(LogSoftmax)
+DECLARE_OP_ADAPTER(LogSoftmaxV2)
+DECLARE_OP_USE_OUTPUT(LogSoftmaxV2)
 DECLARE_OP_ADAPTER(TruncatedNormal)
 DECLARE_OP_USE_OUTPUT(TruncatedNormal)
 DECLARE_OP_ADAPTER(StridedSliceGrad)
@@ -402,8 +396,8 @@ DECLARE_OP_ADAPTER(Sigmoid)
 DECLARE_OP_USE_OUTPUT(Sigmoid)
 DECLARE_OP_ADAPTER(SigmoidGrad)
 DECLARE_OP_USE_OUTPUT(SigmoidGrad)
-DECLARE_OP_ADAPTER(Softmax)
-DECLARE_OP_USE_OUTPUT(Softmax)
+DECLARE_OP_ADAPTER(SoftmaxV2)
+DECLARE_OP_USE_OUTPUT(SoftmaxV2)
 DECLARE_OP_ADAPTER(SoftmaxGrad)
 DECLARE_OP_USE_OUTPUT(SoftmaxGrad)
 DECLARE_OP_ADAPTER(Greater)
@@ -446,6 +440,8 @@ DECLARE_OP_ADAPTER(Round)
 DECLARE_OP_USE_OUTPUT(Round)
 DECLARE_OP_ADAPTER(ApplyFtrl)
 DECLARE_OP_USE_OUTPUT(ApplyFtrl)
+DECLARE_OP_ADAPTER(SparseApplyFtrlD)
+DECLARE_OP_USE_OUTPUT(SparseApplyFtrlD)
 DECLARE_OP_ADAPTER(Diag)
 DECLARE_OP_USE_OUTPUT(Diag)
 DECLARE_OP_ADAPTER(DiagPart)

mindspore/ops/operations/array_ops.py

@@ -1235,8 +1235,8 @@ class UnsortedSegmentSum(PrimitiveWithInfer):
         Tensor, the shape is :math:`(z, x_{N+1}, ..., x_R)`.
 
     Examples:
-        >>> input_x = Tensor([1, 2, 3, 4], mindspore.float32)
-        >>> segment_ids = Tensor([0, 0, 1, 2], mindspore.int32)
+        >>> input_x = [1, 2, 3, 4]
+        >>> segment_ids = [0, 0, 1, 2]
         >>> num_segments = 4
         >>> P.UnsortedSegmentSum()(input_x, segment_ids, num_segments)
         [3, 3, 4, 0]

mindspore/ops/operations/nn_ops.py

@@ -1630,7 +1630,7 @@ class LayerNorm(Primitive):
     `Layer Normalization <https://arxiv.org/abs/1607.06450>`_.
 
     .. math::
-        y = \frac{x - mean}{\sqrt{variance + \epsilon}} * \gamma + \beta
+        y = \frac{x - mean]}{\sqrt{variance + \epsilon}} * \gamma + \beta
 
     where :math:`\gamma` is scale, :math:`\beta` is bias, :math:`\epsilon` is epsilon.
 

tests/ut/cpp/optimizer/lib_test.cc

@@ -556,24 +556,5 @@ TEST_F(TestOptLib, test_constant_duplicate_mul) {
   ASSERT_TRUE(CheckOpt(beforerl, after, patterns));
   ASSERT_TRUE(CheckOpt(beforerr, after, patterns));
 }
-
-TEST_F(TestOptLib, test_adjust_allreduce_mul_add) {
-  FuncGraphPtr beforell = getPyFun.CallAndParseRet("test_adjust_allreduce_mul_add", "beforell");
-  FuncGraphPtr beforelr = getPyFun.CallAndParseRet("test_adjust_allreduce_mul_add", "beforelr");
-  FuncGraphPtr beforerl = getPyFun.CallAndParseRet("test_adjust_allreduce_mul_add", "beforerl");
-  FuncGraphPtr beforerr = getPyFun.CallAndParseRet("test_adjust_allreduce_mul_add", "beforerr");
-  FuncGraphPtr after1 = getPyFun.CallAndParseRet("test_adjust_allreduce_mul_add", "after1");
-  FuncGraphPtr before2r = getPyFun.CallAndParseRet("test_adjust_allreduce_mul_add", "before2r");
-  FuncGraphPtr before2l = getPyFun.CallAndParseRet("test_adjust_allreduce_mul_add", "before2l");
-  FuncGraphPtr after2 = getPyFun.CallAndParseRet("test_adjust_allreduce_mul_add", "after2");
-  auto patterns = std::vector<SubstitutionPtr>({irpass.arithmetic_simplify_});
-  ASSERT_TRUE(CheckOpt(beforell, after1, patterns));
-  ASSERT_TRUE(CheckOpt(beforelr, after1, patterns));
-  ASSERT_TRUE(CheckOpt(beforerl, after1, patterns));
-  ASSERT_TRUE(CheckOpt(beforerr, after1, patterns));
-  ASSERT_TRUE(CheckOpt(before2l, after2, patterns));
-  ASSERT_TRUE(CheckOpt(before2r, after2, patterns));
-}
-
 }  // namespace opt
 }  // namespace mindspore

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

@@ -908,8 +908,8 @@ def test_print_tuple_wrapper(tag):
 
 def test_constant_duplicate_mul(tag):
     fns = FnDict()
-    Mul = Primitive('Mul')
-    Sqrt = Primitive('Sqrt')
+    Mul = Primitive('Mul');
+    Sqrt = Primitive('Sqrt');
 
     x = Tensor(np.array([[2, 2], [2, 3]]).astype('float32'))
     tensor1 = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]]).astype('float32'))
@@ -936,44 +936,3 @@ def test_constant_duplicate_mul(tag):
         return Mul(Sqrt(x), Mul(tensor1, tensor2))
 
     return fns[tag]
-
-
-def test_adjust_allreduce_mul_add(tag):
-    fns = FnDict()
-    Mul = Primitive('Mul')
-    AddN = Primitive('AddN')
-    AllReduce = Primitive('AllReduce')
-
-    @fns
-    def beforell(x, y, z):
-        return AddN((z, Mul(y, AllReduce(x))))
-
-    @fns
-    def beforelr(x, y, z):
-        return AddN((z, Mul(AllReduce(x), y)))
-
-    @fns
-    def beforerl(x, y, z):
-        return AddN((Mul(y, AllReduce(x)), z))
-
-    @fns
-    def beforerr(x, y, z):
-        return AddN((Mul(AllReduce(x), y), z))
-
-    @fns
-    def after1(x, y, z):
-        return Mul(AllReduce(AddN((z, x))), y)
-
-    @fns
-    def before2r(x, y, z):
-        return AddN((Mul(AllReduce(x), y), Mul(z, z)))
-
-    @fns
-    def before2l(x, y, z):
-        return AddN((Mul(z, z), Mul(AllReduce(x), y)))
-
-    @fns
-    def after2(x, y, z):
-        return Mul(AllReduce(AddN((Mul(z, z), x))), y)
-
-    return fns[tag]