Merge branch 'feature/grad_reg_mechanism_cont2' of...

Merge branch 'feature/grad_reg_mechanism_cont2' of https://github.com/reyoung/Paddle into dev_backward_for_op_desc_dev

paddle/framework/CMakeLists.txt

@@ -26,10 +26,8 @@ cc_library(op_info SRCS op_info.cc DEPS attribute framework_proto proto_desc)
 cc_library(operator SRCS operator.cc DEPS op_info device_context tensor scope)
 cc_test(operator_test SRCS operator_test.cc DEPS operator op_registry)
 
-cc_library(grad_op_builder SRCS grad_op_builder.cc DEPS operator proto_desc)
-cc_library(op_registry SRCS op_registry.cc DEPS grad_op_builder op_proto_maker op_info)
+cc_library(op_registry SRCS op_registry.cc DEPS op_proto_maker op_info operator)
 cc_test(op_registry_test SRCS op_registry_test.cc DEPS op_registry)
-cc_test(grad_op_builder_test SRCS grad_op_builder_test.cc DEPS grad_op_builder op_registry sum_op)
 
 py_proto_compile(framework_py_proto SRCS framework.proto)
 # Generate an empty __init__.py to make framework_py_proto as a valid python module.

paddle/framework/backward.cc

@@ -13,6 +13,7 @@
    limitations under the License. */
 
 #include "paddle/framework/backward.h"
+#include "paddle/operators/net_op.h"
 
 #include <deque>
 #include <list>
@@ -20,13 +21,41 @@
 
 #include "paddle/framework/block_desc.h"
 #include "paddle/framework/op_registry.h"
-#include "paddle/framework/program_desc.h"
 #include "paddle/operators/net_op.h"
 #include "paddle/operators/recurrent_op.h"
 
 namespace paddle {
 namespace framework {
 
+static inline std::unique_ptr<OperatorBase> CreateGradOp(
+    const OperatorBase& op) {
+  OpDescBind op_desc;
+  op_desc.SetInputMap(op.Inputs());
+  op_desc.SetOutputMap(op.Outputs());
+  op_desc.SetType(op.Type());
+  op_desc.SetAttrMap(op.Attrs());
+  auto& info = OpInfoMap::Instance().Get(op.Type());
+  auto grad_descs = info.GradOpMaker()(op_desc);
+  std::vector<std::unique_ptr<OperatorBase>> grad_ops;
+  grad_ops.reserve(grad_descs.size());
+  std::transform(grad_descs.begin(), grad_descs.end(),
+                 std::back_inserter(grad_ops),
+                 [](const std::unique_ptr<OpDescBind>& grad_desc) {
+                   return OpRegistry::CreateOp(*grad_desc);
+                 });
+  PADDLE_ENFORCE(!grad_ops.empty());
+  if (grad_ops.size() == 1) {
+    return std::move(grad_ops[0]);
+  } else {
+    auto net_op = new operators::NetOp();
+    for (auto& grad_op : grad_ops) {
+      net_op->AppendOp(std::move(grad_op));
+    }
+    net_op->CompleteAddOp();
+    return std::unique_ptr<OperatorBase>(net_op);
+  }
+}
+
 template <typename Map, typename T>
 static void ForEachVarName(const Map& names, T callback) {
   for (auto& name : names) {
@@ -174,7 +203,7 @@ static std::unique_ptr<OperatorBase> BackwardRecursive(
       net->InsertOp(pos.first + 1, std::move(pos.second));
     }
   } else {
-    std::unique_ptr<OperatorBase> grad_op(OpRegistry::CreateGradOp(forwardOp));
+    std::unique_ptr<OperatorBase> grad_op(CreateGradOp(forwardOp));
 
     ForEachVarName(grad_op->Inputs(), [&no_grad_names, &net, &grad_op](
                                           const std::string& grad_input) {
@@ -260,13 +289,14 @@ std::vector<std::unique_ptr<OpDescBind>> MakeOpGrad(
     std::unordered_set<std::string>& no_grad_vars) {
   std::vector<std::unique_ptr<OpDescBind>> grad_op_descs;
   // All input gradients of forwarding operator do not need to calculat.
-  if (AllGradInSet(op_desc->InputArgumentNames(), no_grad_vars)) {
+  const std::vector<std::string>& inputs = op_desc->InArgumentNames();
+  if (AllGradInSet(inputs, no_grad_vars)) {
     return grad_op_descs;  // empty vector
   }
   // All output gradients of forwarding operator do not need to calculate.
   const std::vector<std::string>& outputs = op_desc->OutputArgumentNames();
   if (AllGradInSet(outputs, no_grad_vars)) {
-    for (const std::string& name : outputs) {
+    for (const std::string& name : inputs) {
       no_grad_vars.insert(GradVarName(name));
     }
     return grad_op_descs;  // empty vector

paddle/framework/backward.h

@@ -13,8 +13,11 @@
    limitations under the License. */
 
 #pragma once
+
 #include <unordered_set>
-#include "operator.h"
+#include "paddle/framework/operator.h"
+#include "paddle/framework/program_desc.h"
+
 namespace paddle {
 namespace framework {
 
@@ -24,8 +27,7 @@ extern std::unique_ptr<OperatorBase> Backward(
     const OperatorBase& forwardOp,
     const std::unordered_set<std::string>& no_grad_vars);
 
-extern void AppendBackwardOpDescs(
-    BlockDescBind& block_desc,
+void AppendBackward(ProgramDescBind& program_desc,
                     const std::unordered_set<std::string>& no_grad_vars);
 
 }  // namespace framework

paddle/framework/backward_test.cc

@@ -15,30 +15,42 @@
 #include "paddle/framework/backward.h"
 
 #include <gtest/gtest.h>
+#include "paddle/framework/block_desc.h"
+#include "paddle/framework/op_desc.h"
 #include "paddle/framework/op_registry.h"
 #include "paddle/operators/net_op.h"
 
 namespace paddle {
 namespace framework {
 
-using OperatorBase = framework::OperatorBase;
-using OpProtoAndCheckerMaker = framework::OpProtoAndCheckerMaker;
-using OpProto = framework::OpProto;
-using OpAttrChecker = framework::OpAttrChecker;
-using Scope = framework::Scope;
 using DeviceContext = platform::DeviceContext;
 
 class RowWiseAddOpMaker : public OpProtoAndCheckerMaker {
  public:
   RowWiseAddOpMaker(OpProto *proto, OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("X", "Input X of Add").NotInGradient();
-    AddInput("b", "Bias of Add").NotInGradient();
-    AddOutput("Out", "Out of Add").NotInGradient();
+    AddInput("X", "Input X of Add");
+    AddInput("b", "Bias of Add");
+    AddOutput("Out", "Out of Add");
     AddComment("Add Op");
   }
 };
 
+class RowWiseAddGradMaker : public SingleGradOpDescMaker {
+ public:
+  using SingleGradOpDescMaker::SingleGradOpDescMaker;
+
+ protected:
+  std::unique_ptr<OpDescBind> Apply() const override {
+    auto grad_op = new OpDescBind();
+    grad_op->SetInput(GradVarName("Out"), OutputGrad("Out"));
+    grad_op->SetOutput(GradVarName("X"), InputGrad("X"));
+    grad_op->SetOutput(GradVarName("b"), InputGrad("b"));
+    grad_op->SetType("rowwise_add_grad");
+    return std::unique_ptr<OpDescBind>(grad_op);
+  }
+};
+
 class MulOpMaker : public OpProtoAndCheckerMaker {
  public:
   MulOpMaker(OpProto *proto, OpAttrChecker *op_checker)
@@ -137,10 +149,8 @@ class SumOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   SumOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("X", "the input tensors of sum operator.")
-        .AsDuplicable()
-        .NotInGradient();
-    AddOutput("Out", "the output tensor of sum operator.").NotInGradient();
+    AddInput("X", "the input tensors of sum operator.").AsDuplicable();
+    AddOutput("Out", "the output tensor of sum operator.");
     AddComment("");
   }
 };
@@ -151,8 +161,9 @@ class SumOpMaker : public framework::OpProtoAndCheckerMaker {
 namespace f = paddle::framework;
 namespace ops = paddle::operators;
 using EnforceNotMet = paddle::platform::EnforceNotMet;
-REGISTER_OP(rowwise_add, f::NOP, f::RowWiseAddOpMaker, rowwise_add_grad,
-            f::NOP);
+REGISTER_OPERATOR(rowwise_add, f::NOP, f::RowWiseAddOpMaker,
+                  f::RowWiseAddGradMaker);
+REGISTER_OPERATOR(rowwise_add_grad, f::NOP);
 REGISTER_OP(mul, f::NOP, f::MulOpMaker, mul_grad, f::NOP);
 REGISTER_OP(sigmoid, f::NOP, f::SigmoidOpMaker, sigmoid_grad, f::NOP);
 REGISTER_OP_WITHOUT_GRADIENT(nograd, f::NOP, f::NoGradOpMaker);
@@ -162,17 +173,6 @@ REGISTER_OP_WITHOUT_GRADIENT(fc, f::FcOp, f::FcOpMaker);
 REGISTER_OP(many_output_op, f::NOP, f::ManyOutputOpMaker, many_output_op_grad,
             f::NOP);
 
-TEST(Backward, simple_op_grad) {
-  auto fwd = f::OpRegistry::CreateOp(
-      "rowwise_add", {{"X", {"x"}}, {"b", {"b"}}}, {{"Out", {"out"}}}, {});
-  ASSERT_NE(fwd, nullptr);
-  auto gop = f::OpRegistry::CreateGradOp(*fwd);
-  ASSERT_EQ(1UL, gop->Inputs().size());
-  ASSERT_EQ("rowwise_add_grad", gop->Type());
-  ASSERT_EQ(f::GradVarName("x"), gop->Output(f::GradVarName("X")));
-  ASSERT_EQ(f::GradVarName("b"), gop->Output(f::GradVarName("b")));
-}
-
 TEST(Backward, simple_op_not_need_grad) {
   auto fwd = f::OpRegistry::CreateOp(
       "rowwise_add", {{"X", {"x"}}, {"b", {"b"}}}, {{"Out", {"out"}}}, {});
@@ -289,17 +289,6 @@ TEST(Backward, net_shared_weight) {
   ASSERT_EQ("sum", bwd_net->ops_[2]->Type());
 }
 
-TEST(Backward, op_register_grad_not_for_network) {
-  auto fwd =
-      f::OpRegistry::CreateOp("fc", {{"X", {"x"}}, {"W", {"w"}}, {"b", {"b"}}},
-                              {{"mul_result", {"mul_out"}},
-                               {"add_result", {"add_out"}},
-                               {"Out", {"out1"}}},
-                              {{"temporary_index", std::vector<int>{0, 1}}});
-
-  ASSERT_THROW(f::OpRegistry::CreateGradOp(*fwd), EnforceNotMet);
-}
-
 TEST(Backward, op_all_input_are_not_need) {
   auto fwd = f::OpRegistry::CreateOp(
       "rowwise_add", {{"X", {"x"}}, {"b", {"b"}}}, {{"Out", {"out"}}}, {});
@@ -402,3 +391,100 @@ TEST(Backward, linear_net_intermediate_variable_has_no_grad) {
   EXPECT_EQ(bwd_net->ops_[2]->Inputs(all).size(), 0UL);
   EXPECT_EQ(bwd_net->ops_[2]->Outputs(all).size(), 0UL);
 }
+
+// =================================== //
+
+f::ProgramDesc *GetNewProgramDesc() {
+  auto *program_desc = new f::ProgramDesc();
+  auto *root_block = program_desc->add_blocks();
+  root_block->set_idx(0);
+  root_block->set_parent_idx(-1);
+  return program_desc;
+}
+
+TEST(Backward, simple_single_op) {
+  f::ProgramDesc *program_desc = GetNewProgramDesc();
+  f::ProgramDescBind &program = f::ProgramDescBind::Instance(program_desc);
+  f::BlockDescBind *block = program.Block(0);
+  f::OpDescBind *op = block->AppendOp();
+  op->SetType("rowwise_add");
+  op->SetInput("X", {"x"});
+  op->SetInput("b", {"b"});
+  op->SetOutput("Out", {"out"});
+
+  AppendBackward(program, {});
+
+  ASSERT_EQ(block->AllOps().size(), 2UL);
+  f::OpDescBind *grad_op = block->AllOps()[1];
+  EXPECT_EQ(grad_op->Type(), "rowwise_add_grad");
+  ASSERT_EQ(grad_op->InputNames().size(), 1UL);
+  ASSERT_EQ(grad_op->OutputNames().size(), 2UL);
+  EXPECT_EQ(grad_op->Input(f::GradVarName("Out")),
+            std::vector<std::string>({f::GradVarName("out")}));
+  EXPECT_EQ(grad_op->Output(f::GradVarName("X")),
+            std::vector<std::string>({f::GradVarName("x")}));
+  EXPECT_EQ(grad_op->Output(f::GradVarName("b")),
+            std::vector<std::string>({f::GradVarName("b")}));
+}
+
+TEST(Backward, simple_mult_op) {
+  f::ProgramDesc *program_desc = GetNewProgramDesc();
+  f::ProgramDescBind &program = f::ProgramDescBind::Instance(program_desc);
+  f::BlockDescBind *block = program.Block(0);
+  f::OpDescBind *op1 = block->AppendOp();
+  op1->SetType("rowwise_add");
+  op1->SetInput("X", {"x1"});
+  op1->SetInput("b", {"b1"});
+  op1->SetOutput("Out", {"out1"});
+
+  f::OpDescBind *op2 = block->AppendOp();
+  op2->SetType("mul");
+  op2->SetInput("X", {"out1"});
+  op2->SetInput("Y", {"y2"});
+  op2->SetOutput("Out", {"out2"});
+
+  f::OpDescBind *op3 = block->AppendOp();
+  op3->SetType("rowwise_add");
+  op3->SetInput("X", {"out2"});
+  op3->SetInput("b", {"b3"});
+  op3->SetOutput("Out", {"out3"});
+
+  AppendBackward(program, {});
+
+  ASSERT_EQ(block->AllOps().size(), 6UL);
+  f::OpDescBind *grad_op1 = block->AllOps()[5];
+  EXPECT_EQ(grad_op1->Type(), "rowwise_add_grad");
+  ASSERT_EQ(grad_op1->InputNames().size(), 1UL);
+  ASSERT_EQ(grad_op1->OutputNames().size(), 2UL);
+  EXPECT_EQ(grad_op1->Input(f::GradVarName("Out")),
+            std::vector<std::string>({f::GradVarName("out1")}));
+  EXPECT_EQ(grad_op1->Output(f::GradVarName("X")),
+            std::vector<std::string>({f::GradVarName("x1")}));
+  EXPECT_EQ(grad_op1->Output(f::GradVarName("b")),
+            std::vector<std::string>({f::GradVarName("b1")}));
+
+  f::OpDescBind *grad_op2 = block->AllOps()[4];
+  EXPECT_EQ(grad_op2->Type(), "mul_grad");
+  ASSERT_EQ(grad_op2->InputNames().size(), 4UL);
+  ASSERT_EQ(grad_op2->OutputNames().size(), 2UL);
+  EXPECT_EQ(grad_op2->Input("X"), std::vector<std::string>({"out1"}));
+  EXPECT_EQ(grad_op2->Input("Y"), std::vector<std::string>({"y2"}));
+  EXPECT_EQ(grad_op2->Input("Out"), std::vector<std::string>({"out2"}));
+  EXPECT_EQ(grad_op2->Input(f::GradVarName("Out")),
+            std::vector<std::string>({f::GradVarName("out2")}));
+  EXPECT_EQ(grad_op2->Output(f::GradVarName("X")),
+            std::vector<std::string>({f::GradVarName("out1")}));
+  EXPECT_EQ(grad_op2->Output(f::GradVarName("Y")),
+            std::vector<std::string>({f::GradVarName("y2")}));
+
+  f::OpDescBind *grad_op3 = block->AllOps()[3];
+  EXPECT_EQ(grad_op3->Type(), "rowwise_add_grad");
+  ASSERT_EQ(grad_op3->InputNames().size(), 1UL);
+  ASSERT_EQ(grad_op3->OutputNames().size(), 2UL);
+  EXPECT_EQ(grad_op3->Input(f::GradVarName("Out")),
+            std::vector<std::string>({f::GradVarName("out3")}));
+  EXPECT_EQ(grad_op3->Output(f::GradVarName("X")),
+            std::vector<std::string>({f::GradVarName("out2")}));
+  EXPECT_EQ(grad_op3->Output(f::GradVarName("b")),
+            std::vector<std::string>({f::GradVarName("b3")}));
+}
\ No newline at end of file

paddle/framework/framework.proto

@@ -66,7 +66,6 @@ message OpProto {
 
     optional bool duplicable = 3 [ default = false ];
     optional bool intermediate = 4 [ default = false ];
-    optional bool not_in_gradient = 5 [ default = false ];
   }
 
   // AttrProto describes the C++ type Attribute.

paddle/framework/grad_op_builder.cc

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOpArgType::OUT WARRANTIES OR CONDITIONS OF ANY KOpArgType::IND, either
-express or implied. See the License for the specific language governing
-permissions and limitations under the License. */
-
-#include "paddle/framework/grad_op_builder.h"
-#include "paddle/framework/op_registry.h"
-
-namespace paddle {
-namespace framework {
-enum class OpArgType { IN, OUT };
-
-static void TransOpArg(const OperatorBase* src_op, const OpArgType& src_type,
-                       bool is_grad, VariableNameMap* vars) {
-  const auto& src_inout =
-      src_type == OpArgType::IN ? src_op->Inputs() : src_op->Outputs();
-  auto& dst_inout = *vars;
-  auto& proto = OpInfoMap::Instance().Get(src_op->Type()).Proto();
-  const auto& src_arg_list =
-      src_type == OpArgType::IN ? proto.inputs() : proto.outputs();
-  for (const auto& arg : src_arg_list) {
-    if (arg.not_in_gradient() && !is_grad) continue;
-    const std::string src_name = arg.name();
-    std::string dst_name = is_grad ? GradVarName(src_name) : src_name;
-    dst_inout[dst_name].reserve(src_inout.at(src_name).size());
-    for (auto& var_name : src_inout.at(src_name)) {
-      std::string s = is_grad ? GradVarName(var_name) : var_name;
-      dst_inout[dst_name].emplace_back(s);
-    }
-  }
-}
-
-OperatorBase* BuildGradOp(const OperatorBase* op) {
-  auto& info = OpInfoMap::Instance().Get(op->Type());
-  PADDLE_ENFORCE(info.HasGradientOp());
-
-  VariableNameMap inputs;
-  VariableNameMap outputs;
-  TransOpArg(op, OpArgType::IN, false, &inputs);   // I
-  TransOpArg(op, OpArgType::OUT, false, &inputs);  // O
-  TransOpArg(op, OpArgType::OUT, true, &inputs);   // OG
-  TransOpArg(op, OpArgType::IN, true, &outputs);   // IG
-
-  auto& grad_info = OpInfoMap::Instance().Get(info.grad_op_type_);
-  return grad_info.Creator()(info.grad_op_type_, inputs, outputs, op->Attrs());
-}
-
-static void TransOpDescArg(const OpDescBind* src_op, const OpArgType& src_type,
-                           bool is_grad, OpDescBind* dst_op,
-                           const OpArgType& dst_type) {
-  PADDLE_ENFORCE(dst_op != nullptr,
-                 "Protobuf desc of gradient op must be initialized first.");
-  const auto& proto = OpInfoMap::Instance().Get(src_op->Type()).Proto();
-  const auto& src_arg_list =
-      src_type == OpArgType::IN ? proto.inputs() : proto.outputs();
-  for (const auto& arg : src_arg_list) {
-    if (arg.not_in_gradient() && !is_grad) continue;
-    const std::string src_name = arg.name();
-    std::vector<std::string> vars = src_type == OpArgType::IN
-                                        ? src_op->Input(src_name)
-                                        : src_op->Output(src_name);
-    if (is_grad) {
-      for (std::string& var : vars) {
-        var = GradVarName(var);
-      }
-    }
-    std::string dst_name = is_grad ? GradVarName(src_name) : src_name;
-    dst_type == OpArgType::IN ? dst_op->SetInput(dst_name, vars)
-                              : dst_op->SetOutput(dst_name, vars);
-  }
-}
-
-void CompleteGradOpDesc(const OpDescBind* forw_op, OpDescBind* grad_op) {
-  auto& info = OpInfoMap::Instance().Get(forw_op->Type());
-  PADDLE_ENFORCE(info.HasGradientOp());
-
-  grad_op->SetType(info.grad_op_type_);
-
-  TransOpDescArg(forw_op, OpArgType::IN, false, grad_op, OpArgType::IN);
-  TransOpDescArg(forw_op, OpArgType::OUT, false, grad_op, OpArgType::IN);
-  TransOpDescArg(forw_op, OpArgType::OUT, true, grad_op, OpArgType::IN);
-  TransOpDescArg(forw_op, OpArgType::IN, true, grad_op, OpArgType::OUT);
-
-  grad_op->SetAttrMap(forw_op->GetAttrMap());
-}
-
-}  // namespace framework
-}  // namespace paddle

paddle/framework/grad_op_builder.h

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License. */
-
-#pragma once
-
-#include "paddle/framework/op_desc.h"
-#include "paddle/framework/operator.h"
-
-namespace paddle {
-namespace framework {
-
-OperatorBase* BuildGradOp(const OperatorBase* op);
-
-void CompleteGradOpDesc(const OpDescBind* forw_op, OpDescBind* grad_op);
-
-}  // namespace framework
-}  // namespace paddle

paddle/framework/grad_op_builder_test.cc

-#include "paddle/framework/grad_op_builder.h"
-#include <gtest/gtest.h>
-#include "paddle/framework/op_registry.h"
-#include "paddle/framework/operator.h"
-
-USE_OP(sum);
-
-namespace paddle {
-namespace framework {
-
-class MutiInOutOpMaker : public OpProtoAndCheckerMaker {
- public:
-  MutiInOutOpMaker(OpProto *proto, OpAttrChecker *op_checker)
-      : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("In1", "a single input");
-    AddInput("In2_mult", "a multiple input").AsDuplicable();
-    AddInput("In3", "another single input");
-    AddOutput("Out1", "a single output");
-    AddOutput("Out2_mult", "a multiple output").AsDuplicable();
-    AddComment("test op with multiple inputs and outputs");
-  }
-};
-
-class IOIgnoredOpMaker : public OpProtoAndCheckerMaker {
- public:
-  IOIgnoredOpMaker(OpProto *proto, OpAttrChecker *op_checker)
-      : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("In1", "a single input");
-    AddInput("In2_mult", "a multiple input").AsDuplicable().NotInGradient();
-    AddInput("In3_mult", "another multiple input").AsDuplicable();
-    AddOutput("Out1_mult", "a multiple output").AsDuplicable();
-    AddOutput("Out2", "a single output").NotInGradient();
-    AddComment("op with inputs and outputs ignored in gradient calculating");
-  }
-};
-
-}  // namespace framework
-}  // namespace paddle
-
-namespace f = paddle::framework;
-
-REGISTER_OP(mult_io, f::NOP, f::MutiInOutOpMaker, mult_io_grad, f::NOP);
-REGISTER_OP(io_ignored, f::NOP, f::IOIgnoredOpMaker, io_ignored_grad, f::NOP);
-
-TEST(GradOpBuilder, MutiInOut) {
-  std::shared_ptr<f::OperatorBase> test_op(f::OpRegistry::CreateOp(
-      "mult_io", {{"In1", {"in1"}},
-                  {"In2_mult", {"in2_1", "in2_2", "in2_3"}},
-                  {"In3", {"in3"}}},
-      {{"Out1", {"out1"}}, {"Out2_mult", {"out2_1", "out2_2"}}}, {}));
-  std::shared_ptr<f::OperatorBase> grad_test_op =
-      f::OpRegistry::CreateGradOp(*test_op);
-
-  ASSERT_EQ(grad_test_op->Inputs().size(), 3UL + 2UL + 2UL);
-  EXPECT_EQ(grad_test_op->Input("In1"), "in1");
-  EXPECT_EQ(grad_test_op->Inputs("In2_mult"),
-            std::vector<std::string>({"in2_1", "in2_2", "in2_3"}));
-  EXPECT_EQ(grad_test_op->Input("In3"), "in3");
-  EXPECT_EQ(grad_test_op->Input("Out1"), "out1");
-  EXPECT_EQ(grad_test_op->Inputs("Out2_mult"),
-            std::vector<std::string>({"out2_1", "out2_2"}));
-  EXPECT_EQ(grad_test_op->Input(f::GradVarName("Out1")),
-            f::GradVarName("out1"));
-  EXPECT_EQ(grad_test_op->Inputs(f::GradVarName("Out2_mult")),
-            std::vector<std::string>(
-                {f::GradVarName("out2_1"), f::GradVarName("out2_2")}));
-
-  ASSERT_EQ(grad_test_op->Outputs().size(), 3UL);
-  EXPECT_EQ(grad_test_op->Output(f::GradVarName("In1")), f::GradVarName("in1"));
-  EXPECT_EQ(grad_test_op->Outputs(f::GradVarName("In2_mult")),
-            std::vector<std::string>({f::GradVarName("in2_1"),
-                                      f::GradVarName("in2_2"),
-                                      f::GradVarName("in2_3")}));
-  EXPECT_EQ(grad_test_op->Output(f::GradVarName("In3")), f::GradVarName("in3"));
-}
-
-TEST(GradOpBuilder, IOIgnoredInGradient) {
-  std::shared_ptr<f::OperatorBase> test_op(f::OpRegistry::CreateOp(
-      "io_ignored", {{"In1", {"in1"}},
-                     {"In2_mult", {"in2_1", "in2_2"}},
-                     {"In3_mult", {"in3_1", "in3_2"}}},
-      {{"Out1_mult", {"out1_1", "out1_2"}}, {"Out2", {"out2"}}}, {}));
-  std::shared_ptr<f::OperatorBase> grad_test_op =
-      f::OpRegistry::CreateGradOp(*test_op);
-
-  // 'In2' and 'Out2' are ignored in gradient calculating
-  ASSERT_EQ(grad_test_op->Inputs().size(), 2UL + 1UL + 2UL);
-  EXPECT_EQ(grad_test_op->Input("In1"), "in1");
-  EXPECT_EQ(grad_test_op->Inputs("In3_mult"),
-            std::vector<std::string>({"in3_1", "in3_2"}));
-  EXPECT_EQ(grad_test_op->Inputs("Out1_mult"),
-            std::vector<std::string>({"out1_1", "out1_2"}));
-  EXPECT_EQ(grad_test_op->Inputs(f::GradVarName("Out1_mult")),
-            std::vector<std::string>(
-                {f::GradVarName("out1_1"), f::GradVarName("out1_2")}));
-  EXPECT_EQ(grad_test_op->Input(f::GradVarName("Out2")),
-            f::GradVarName("out2"));
-
-  ASSERT_EQ(grad_test_op->Outputs().size(), 3UL);
-  EXPECT_EQ(grad_test_op->Output(f::GradVarName("In1")), f::GradVarName("in1"));
-  EXPECT_EQ(grad_test_op->Outputs(f::GradVarName("In2_mult")),
-            std::vector<std::string>(
-                {f::GradVarName("in2_1"), f::GradVarName("in2_2")}));
-  EXPECT_EQ(grad_test_op->Outputs(f::GradVarName("In3_mult")),
-            std::vector<std::string>(
-                {f::GradVarName("in3_1"), f::GradVarName("in3_2")}));
-}
-
-TEST(GradOpDescBuilder, MutiInOut) {
-  f::OpDescBind *forw_op = new f::OpDescBind();
-  forw_op->SetType("mult_io");
-  forw_op->SetInput("In1", {"in1"});
-  forw_op->SetInput("In2_mult", {"in2_1", "in2_2", "in2_3"});
-  forw_op->SetInput("In3", {"in3"});
-  forw_op->SetOutput("Out1", {"out1"});
-  forw_op->SetOutput("Out2_mult", {"out2_1", "out2_2"});
-
-  f::OpDescBind *grad_op = new f::OpDescBind();
-  f::CompleteGradOpDesc(forw_op, grad_op);
-
-  EXPECT_EQ(grad_op->Type(), "mult_io_grad");
-  ASSERT_EQ(grad_op->InputNames().size(), 3UL + 2UL + 2UL);
-  EXPECT_EQ(grad_op->Input("In1"), std::vector<std::string>({"in1"}));
-  EXPECT_EQ(grad_op->Input("In2_mult"),
-            std::vector<std::string>({"in2_1", "in2_2", "in2_3"}));
-  EXPECT_EQ(grad_op->Input("In3"), std::vector<std::string>({"in3"}));
-  EXPECT_EQ(grad_op->Input("Out1"), std::vector<std::string>({"out1"}));
-  EXPECT_EQ(grad_op->Input("Out2_mult"),
-            std::vector<std::string>({"out2_1", "out2_2"}));
-  EXPECT_EQ(grad_op->Input(f::GradVarName("Out1")),
-            std::vector<std::string>({f::GradVarName("out1")}));
-  EXPECT_EQ(grad_op->Input(f::GradVarName("Out2_mult")),
-            std::vector<std::string>(
-                {f::GradVarName("out2_1"), f::GradVarName("out2_2")}));
-
-  ASSERT_EQ(grad_op->OutputNames().size(), 3UL);
-  EXPECT_EQ(grad_op->Output(f::GradVarName("In1")),
-            std::vector<std::string>({f::GradVarName("in1")}));
-  EXPECT_EQ(grad_op->Output(f::GradVarName("In2_mult")),
-            std::vector<std::string>({f::GradVarName("in2_1"),
-                                      f::GradVarName("in2_2"),
-                                      f::GradVarName("in2_3")}));
-  EXPECT_EQ(grad_op->Output(f::GradVarName("In3")),
-            std::vector<std::string>({f::GradVarName("in3")}));
-  delete forw_op;
-  delete grad_op;
-}
-
-TEST(GradOpDescBuilder, IOIgnoredInGradient) {
-  f::OpDescBind *forw_op = new f::OpDescBind();
-  forw_op->SetType("io_ignored");
-  forw_op->SetInput("In1", {"in1"});
-  forw_op->SetInput("In2_mult", {"in2_1", "in2_2"});
-  forw_op->SetInput("In3_mult", {"in3_1", "in3_2"});
-  forw_op->SetOutput("Out1_mult", {"out1_1", "out1_2"});
-  forw_op->SetOutput("Out2", {"out2"});
-
-  f::OpDescBind *grad_op = new f::OpDescBind();
-  f::CompleteGradOpDesc(forw_op, grad_op);
-
-  EXPECT_EQ(grad_op->Type(), "io_ignored_grad");
-  // 'In2' and 'Out2' are ignored in gradient calculating
-  ASSERT_EQ(grad_op->InputNames().size(), 2UL + 1UL + 2UL);
-  EXPECT_EQ(grad_op->Input("In1"), std::vector<std::string>({"in1"}));
-  EXPECT_EQ(grad_op->Input("In3_mult"),
-            std::vector<std::string>({"in3_1", "in3_2"}));
-  EXPECT_EQ(grad_op->Input("Out1_mult"),
-            std::vector<std::string>({"out1_1", "out1_2"}));
-  EXPECT_EQ(grad_op->Input(f::GradVarName("Out1_mult")),
-            std::vector<std::string>(
-                {f::GradVarName("out1_1"), f::GradVarName("out1_2")}));
-  EXPECT_EQ(grad_op->Input(f::GradVarName("Out2")),
-            std::vector<std::string>({f::GradVarName("out2")}));
-
-  ASSERT_EQ(grad_op->OutputNames().size(), 3UL);
-  EXPECT_EQ(grad_op->Output(f::GradVarName("In1")),
-            std::vector<std::string>({f::GradVarName("in1")}));
-  EXPECT_EQ(grad_op->Output(f::GradVarName("In2_mult")),
-            std::vector<std::string>(
-                {f::GradVarName("in2_1"), f::GradVarName("in2_2")}));
-  EXPECT_EQ(grad_op->Output(f::GradVarName("In3_mult")),
-            std::vector<std::string>(
-                {f::GradVarName("in3_1"), f::GradVarName("in3_2")}));
-  delete forw_op;
-  delete grad_op;
-}
\ No newline at end of file

paddle/framework/op_desc.h

@@ -81,6 +81,22 @@ class OpDescBind {
   std::vector<std::string> InputNames() const { return MapKeys(inputs_); }
   std::vector<std::string> OutputNames() const { return MapKeys(outputs_); }
 
+  void SetInputMap(const VariableNameMap &input) {
+    this->inputs_ = input;
+    this->need_update_ = true;
+  }
+
+  void SetOutputMap(const VariableNameMap &output) {
+    this->outputs_ = output;
+    this->need_update_ = true;
+  }
+
+  void Sync();
+
+  const VariableNameMap &Inputs() const { return inputs_; }
+
+  const VariableNameMap &Outputs() const { return outputs_; }
+
  private:
   template <typename MapType>
   static std::vector<typename MapType::key_type> MapKeys(const MapType &map) {
@@ -92,8 +108,6 @@ class OpDescBind {
     return ret_val;
   }
 
-  void Sync();
-
   OpDesc op_desc_;
   VariableNameMap inputs_;
   VariableNameMap outputs_;

paddle/framework/op_info.h

@@ -17,6 +17,7 @@
 #include <map>
 #include <string>
 #include <unordered_map>
+
 #include "paddle/framework/attribute.h"
 #include "paddle/framework/op_desc.h"
 #include "paddle/framework/type_defs.h"
@@ -27,7 +28,6 @@ namespace framework {
 
 struct OpInfo {
   OpCreator creator_;
-  std::string grad_op_type_;
   GradOpMakerFN grad_op_maker_;
   OpProto* proto_{nullptr};
   OpAttrChecker* checker_{nullptr};
@@ -43,19 +43,17 @@ struct OpInfo {
     return *proto_;
   }
 
-  const OpAttrChecker& Checker() const {
-    PADDLE_ENFORCE_NOT_NULL(checker_,
-                            "Operator Checker has not been registered");
-    return *checker_;
-  }
-
   const OpCreator& Creator() const {
     PADDLE_ENFORCE_NOT_NULL(creator_,
                             "Operator Creator has not been registered");
     return creator_;
   }
 
-  bool HasGradientOp() const { return !grad_op_type_.empty(); }
+  const GradOpMakerFN& GradOpMaker() const {
+    PADDLE_ENFORCE_NOT_NULL(grad_op_maker_,
+                            "Operator GradOpMaker has not been registered.");
+    return grad_op_maker_;
+  }
 };
 
 class OpInfoMap {

paddle/framework/op_proto_maker.h

@@ -44,11 +44,6 @@ class OpProtoAndCheckerMaker {
       var_->set_intermediate(true);
       return *this;
     }
-
-    VariableBuilder& NotInGradient() {
-      var_->set_not_in_gradient(true);
-      return *this;
-    }
   };
 
   VariableBuilder AddInput(const std::string& name, const std::string& comment);

paddle/framework/op_registry.cc

@@ -23,7 +23,9 @@ std::unique_ptr<OperatorBase> OpRegistry::CreateOp(
     const std::string& type, const VariableNameMap& inputs,
     const VariableNameMap& outputs, AttributeMap attrs) {
   auto& info = OpInfoMap::Instance().Get(type);
-  info.Checker().Check(attrs);
+  if (info.checker_ != nullptr) {
+    info.checker_->Check(attrs);
+  }
   auto op = info.Creator()(type, inputs, outputs, attrs);
   return std::unique_ptr<OperatorBase>(op);
 }
@@ -52,9 +54,9 @@ std::unique_ptr<OperatorBase> OpRegistry::CreateOp(const OpDesc& op_desc) {
   return CreateOp(op_desc.type(), inputs, outputs, attrs);
 }
 
-std::unique_ptr<OperatorBase> OpRegistry::CreateGradOp(const OperatorBase& op) {
-  PADDLE_ENFORCE(!op.IsNetOp(), "Use framework::Backward to get backward ops");
-  return std::unique_ptr<OperatorBase>(BuildGradOp(&op));
+std::unique_ptr<OperatorBase> OpRegistry::CreateOp(const OpDescBind& op_desc) {
+  return CreateOp(op_desc.Type(), op_desc.Inputs(), op_desc.Outputs(),
+                  op_desc.GetAttrMap());
 }
 
 std::vector<std::unique_ptr<OpDescBind>> OpRegistry::CreateGradOpDescs(

paddle/framework/op_registry.h

@@ -23,25 +23,37 @@ limitations under the License. */
 #include "paddle/framework/attribute.h"
 #include "paddle/framework/details/op_registry.h"
 #include "paddle/framework/framework.pb.h"
-#include "paddle/framework/grad_op_builder.h"
+#include "paddle/framework/grad_op_desc_maker.h"
+#include "paddle/framework/op_desc.h"
 #include "paddle/framework/operator.h"
 #include "paddle/framework/scope.h"
 
 namespace paddle {
 namespace framework {
+class Registrar {
+ public:
+  // In our design, various kinds of classes, e.g., operators and kernels,
+  // have their corresponding registry and registrar. The action of
+  // registration is in the constructor of a global registrar variable, which,
+  // however, are not used in the code that calls package framework, and would
+  // be removed from the generated binary file by the linker. To avoid such
+  // removal, we add Touch to all registrar classes and make USE_OP macros to
+  // call this method. So, as long as the callee code calls USE_OP, the global
+  // registrar variable won't be removed by the linker.
+  void Touch() {}
+};
 
 template <typename... ARGS>
-struct OperatorRegistrar {
+struct OperatorRegistrar : public Registrar {
   explicit OperatorRegistrar(const char* op_type) : op_type(op_type) {
     PADDLE_ENFORCE(!OpInfoMap::Instance().Has(op_type),
                    "'%s' is registered more than once.", op_type);
     static_assert(sizeof...(ARGS) != 0,
                   "OperatorRegistrar should be invoked at least by OpClass");
     details::OperatorRegistrarRecursive<0, false, ARGS...>(op_type, &info);
+    OpInfoMap::Instance().Insert(op_type, info);
   }
 
-  ~OperatorRegistrar() { OpInfoMap::Instance().Insert(op_type, info); }
-
   const char* op_type;
 
   OpInfo info;
@@ -67,23 +79,10 @@ class OpRegistry {
 
   static std::unique_ptr<OperatorBase> CreateOp(const OpDesc& op_desc);
 
-  static std::unique_ptr<OperatorBase> CreateGradOp(const OperatorBase& op);
-
   static std::vector<std::unique_ptr<OpDescBind>> CreateGradOpDescs(
       const OpDescBind& op_desc);
-};
 
-class Registrar {
- public:
-  // In our design, various kinds of classes, e.g., operators and kernels,
-  // have their corresponding registry and registrar. The action of
-  // registration is in the constructor of a global registrar variable, which,
-  // however, are not used in the code that calls package framework, and would
-  // be removed from the generated binary file by the linker. To avoid such
-  // removal, we add Touch to all registrar classes and make USE_OP macros to
-  // call this method. So, as long as the callee code calls USE_OP, the global
-  // registrar variable won't be removed by the linker.
-  void Touch() {}
+  static std::unique_ptr<OperatorBase> CreateOp(const OpDescBind& op_desc);
 };
 
 template <typename OpType, typename ProtoMakerType, typename GradOpType>
@@ -141,33 +140,41 @@ class OpKernelRegistrar : public Registrar {
                              __test_global_namespace_##uniq_name##__>::value, \
                 msg)
 
-/**
- * Macro to register Operator.
- */
-#define REGISTER_OP(op_type, op_class, op_maker_class, grad_op_type,          \
-                    grad_op_class)                                            \
+#define REGISTER_OPERATOR(op_type, op_class, ...)                      \
   STATIC_ASSERT_GLOBAL_NAMESPACE(                                      \
-      __reg_op__##op_type, "REGISTER_OP must be called in global namespace"); \
+      __reg_op__##op_type,                                             \
+      "REGISTER_OPERATOR must be called in global namespace");         \
   class _OpClass_##op_type##_ : public op_class {                      \
    public:                                                             \
     DEFINE_OP_CLONE_METHOD(_OpClass_##op_type##_);                     \
     DEFINE_OP_CONSTRUCTOR(_OpClass_##op_type##_, op_class);            \
   };                                                                   \
-  class _OpGradClass_##op_type##_ : public grad_op_class {                    \
-   public:                                                                    \
-    DEFINE_OP_CLONE_METHOD(_OpGradClass_##op_type##_);                        \
-    DEFINE_OP_CONSTRUCTOR(_OpGradClass_##op_type##_, grad_op_class);          \
-  };                                                                          \
-  static ::paddle::framework::OpRegistrar<                                    \
-      _OpClass_##op_type##_, op_maker_class, _OpGradClass_##op_type##_>       \
-      __op_registrar_##op_type##__(#op_type, #grad_op_type);                  \
+  static ::paddle::framework::OperatorRegistrar<_OpClass_##op_type##_, \
+                                                ##__VA_ARGS__>         \
+      __op_registrar_##op_type##__(#op_type);                          \
   int TouchOpRegistrar_##op_type() {                                   \
     __op_registrar_##op_type##__.Touch();                              \
     return 0;                                                          \
   }
 
+/**
+ * Macro to register Operator.
+ */
+#define REGISTER_OP(op_type, op_class, op_maker_class, grad_op_type,           \
+                    grad_op_class)                                             \
+  REGISTER_OPERATOR(grad_op_type, grad_op_class);                              \
+  class _GradOpDescMaker_##grad_op_type##_                                     \
+      : public ::paddle::framework::DefaultGradOpDescMaker {                   \
+    using ::paddle::framework::DefaultGradOpDescMaker::DefaultGradOpDescMaker; \
+                                                                               \
+   protected:                                                                  \
+    virtual std::string GradOpType() const { return #grad_op_type; }           \
+  };                                                                           \
+  REGISTER_OPERATOR(op_type, op_class, _GradOpDescMaker_##grad_op_type##_,     \
+                    op_maker_class);
+
 #define REGISTER_OP_WITHOUT_GRADIENT(op_type, op_class, op_maker_class) \
-  REGISTER_OP(op_type, op_class, op_maker_class, , ::paddle::framework::NOP)
+  REGISTER_OPERATOR(op_type, op_class, op_maker_class)
 
 /**
  * Macro to register OperatorKernel.

paddle/operators/mean_op.cc

@@ -36,7 +36,7 @@ class MeanOpMaker : public framework::OpProtoAndCheckerMaker {
   MeanOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X", "The input of mean op");
-    AddOutput("Out", "The output of mean op").NotInGradient();
+    AddOutput("Out", "The output of mean op");
     AddComment(R"DOC( Mean Operator
 )DOC");
   }
@@ -52,11 +52,27 @@ class MeanGradOp : public framework::OperatorWithKernel {
   }
 };
 
+class MeanGradMaker : public framework::SingleGradOpDescMaker {
+ public:
+  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
+
+ protected:
+  std::unique_ptr<framework::OpDescBind> Apply() const override {
+    auto* grad_op = new framework::OpDescBind();
+    grad_op->SetType("mean_grad");
+    grad_op->SetInput("X", Input("X"));
+    grad_op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
+    grad_op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
+    return std::unique_ptr<framework::OpDescBind>(grad_op);
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP(mean, ops::MeanOp, ops::MeanOpMaker, mean_grad, ops::MeanGradOp);
+REGISTER_OPERATOR(mean, ops::MeanOp, ops::MeanOpMaker, ops::MeanGradMaker);
+REGISTER_OPERATOR(mean_grad, ops::MeanGradOp);
 REGISTER_OP_CPU_KERNEL(mean,
                        ops::MeanKernel<paddle::platform::CPUPlace, float>);
 REGISTER_OP_CPU_KERNEL(mean_grad,

paddle/operators/minus_op.cc

@@ -49,9 +49,9 @@ class MinusOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   MinusOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("X", "The left tensor of minus operator.").NotInGradient();
-    AddInput("Y", "The right tensor of minus operator.").NotInGradient();
-    AddOutput("Out", "The output tensor of minus operator.").NotInGradient();
+    AddInput("X", "The left tensor of minus operator.");
+    AddInput("Y", "The right tensor of minus operator.");
+    AddOutput("Out", "The output tensor of minus operator.");
 
     AddComment(R"DOC(Minus Operator
 
@@ -64,26 +64,35 @@ or not. But the output only shares the LoD with input `X`.
 )DOC");
   }
 };
-template <typename AttrType>
-class MinusGradOp : public NetOp {
+
+class MinusGradMaker : public framework::GradOpDescMakerBase {
  public:
-  MinusGradOp(const std::string &type, const framework::VariableNameMap &inputs,
-              const framework::VariableNameMap &outputs,
-              const framework::AttributeMap &attrs)
-      : NetOp(type, inputs, outputs, attrs) {
-    auto out_grad = Input(framework::GradVarName("Out"));
-    auto x_grad = Output(framework::GradVarName("X"));
-    auto y_grad = Output(framework::GradVarName("Y"));
-
-    // x_grad = out_grad
-    AppendOp(framework::OpRegistry::CreateOp("identity", {{"X", {out_grad}}},
-                                             {{"Y", {x_grad}}}, {}));
-
-    framework::AttributeMap scale_attr;
-    scale_attr["scale"] = static_cast<AttrType>(-1);
-    AppendOp(framework::OpRegistry::CreateOp("scale", {{"X", {out_grad}}},
-                                             {{"Out", {y_grad}}}, scale_attr));
-    CompleteAddOp(false);
+  using framework::GradOpDescMakerBase::GradOpDescMakerBase;
+
+  std::vector<std::unique_ptr<framework::OpDescBind>> operator()()
+      const override {
+    std::vector<std::unique_ptr<framework::OpDescBind>> ops;
+    auto x_g = InputGrad("X");
+    if (!x_g.empty()) {
+      auto *x_g_op = new framework::OpDescBind();
+      x_g_op->SetType("scale");
+      x_g_op->SetInput("X", OutputGrad("Out"));
+      x_g_op->SetOutput("Out", x_g);
+      x_g_op->SetAttr("scale", 1.0f);
+      ops.emplace_back(x_g_op);
+    }
+
+    auto y_g = InputGrad("Y");
+    if (!y_g.empty()) {
+      auto *y_g_op = new framework::OpDescBind();
+      y_g_op->SetType("scale");
+      y_g_op->SetInput("X", OutputGrad("Out"));
+      y_g_op->SetOutput("Out", y_g);
+      y_g_op->SetAttr("scale", -1.0f);
+      ops.emplace_back(y_g_op);
+    }
+
+    return ops;
   }
 };
 
@@ -91,7 +100,6 @@ class MinusGradOp : public NetOp {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP(minus, ops::MinusOp, ops::MinusOpMaker, minus_grad,
-            ops::MinusGradOp<float>);
+REGISTER_OPERATOR(minus, ops::MinusOp, ops::MinusOpMaker, ops::MinusGradMaker);
 REGISTER_OP_CPU_KERNEL(minus,
                        ops::MinusKernel<paddle::platform::CPUPlace, float>);

paddle/operators/pad_op.cc

@@ -56,8 +56,7 @@ class PadOpMaker : public framework::OpProtoAndCheckerMaker {
              "The input should be a k-D tensor(k > 0 and k < 7)");
     AddOutput("Out",
               "The output of pad op."
-              "A tensor with the same shape as X.")
-        .NotInGradient();
+              "A tensor with the same shape as X.");
     AddComment(R"DOC(
 Pad input into output, as specified by paddings and pad_value. The input should be a k-D tensor(k > 0 and k < 7). As an example:
 
@@ -111,11 +110,29 @@ class PadOpGrad : public framework::OperatorWithKernel {
   }
 };
 
+class PadOpGradMaker : public framework::SingleGradOpDescMaker {
+ public:
+  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
+
+ protected:
+  std::unique_ptr<framework::OpDescBind> Apply() const override {
+    auto* bind = new framework::OpDescBind();
+    bind->SetInput("X", Input("X"));
+    bind->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
+    bind->SetOutput(framework::GradVarName("X"), InputGrad("X"));
+    bind->SetAttrMap(Attrs());
+    bind->SetType("pad_grad");
+    return std::unique_ptr<framework::OpDescBind>(bind);
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP(pad, ops::PadOp, ops::PadOpMaker, pad_grad, ops::PadOpGrad);
+
+REGISTER_OPERATOR(pad, ops::PadOp, ops::PadOpMaker, ops::PadOpGradMaker);
+REGISTER_OPERATOR(pad_grad, ops::PadOpGrad);
 REGISTER_OP_CPU_KERNEL(pad, ops::PadKernel<paddle::platform::CPUPlace, float>);
 REGISTER_OP_CPU_KERNEL(pad_grad,
                        ops::PadGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/scale_op.cc

@@ -41,8 +41,8 @@ class ScaleOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   ScaleOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("X", "The input tensor of scale operator.").NotInGradient();
-    AddOutput("Out", "The output tensor of scale operator.").NotInGradient();
+    AddInput("X", "The input tensor of scale operator.");
+    AddOutput("Out", "The output tensor of scale operator.");
     AddComment(R"DOC(Scale operator
 
 The equation is: Out = scale*X
@@ -52,21 +52,18 @@ The equation is: Out = scale*X
   }
 };
 
-// The operator to calculate gradients of a scale operator is just the scale
-// operator itself.
-// Grad(Out=scale(X)) => Grad(X) = scale(Grad(Out))
-template <typename AttrType>
-class ScaleGradOp : public NetOp {
+class ScaleGradMaker : public framework::SingleGradOpDescMaker {
  public:
-  ScaleGradOp(const std::string &type, const framework::VariableNameMap &inputs,
-              const framework::VariableNameMap &outputs,
-              const framework::AttributeMap &attrs)
-      : NetOp(type, inputs, outputs, attrs) {
-    AppendOp(framework::OpRegistry::CreateOp(
-        "scale", {{"X", {Input(framework::GradVarName("Out"))}}},
-        {{"Out", {Output(framework::GradVarName("X"))}}},
-        {{"scale", Attr<AttrType>("scale")}}));
-    CompleteAddOp(false);
+  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
+
+ protected:
+  std::unique_ptr<framework::OpDescBind> Apply() const override {
+    auto *grad_op = new framework::OpDescBind();
+    grad_op->SetType("scale");
+    grad_op->SetInput("X", OutputGrad("Out"));
+    grad_op->SetOutput("Out", InputGrad("X"));
+    grad_op->SetAttr("scale", GetAttr("scale"));
+    return std::unique_ptr<framework::OpDescBind>(grad_op);
   }
 };
 
@@ -75,7 +72,7 @@ class ScaleGradOp : public NetOp {
 
 namespace ops = paddle::operators;
 
-REGISTER_OP(scale, ops::ScaleOp, ops::ScaleOpMaker<float>, scale_grad,
-            ops::ScaleGradOp<float>);
+REGISTER_OPERATOR(scale, ops::ScaleOp, ops::ScaleOpMaker<float>,
+                  ops::ScaleGradMaker);
 REGISTER_OP_CPU_KERNEL(scale,
                        ops::ScaleKernel<paddle::platform::CPUPlace, float>);

paddle/operators/softmax_with_cross_entropy_op.cc

@@ -14,6 +14,7 @@
 
 #include "paddle/operators/softmax_with_cross_entropy_op.h"
 #include <paddle/function/TensorType.h>
+#include <iostream>
 
 namespace paddle {
 namespace operators {
@@ -27,13 +28,12 @@ class SoftmaxWithCrossEntropyOpMaker
     AddInput("Logits",
              "(Tensor, default: Tensor<float>), The unscaled log probabilities "
              "which is a 2-D tensor with shape [N x K]. N is the batch_size, "
-             "and K is the class number.")
-        .NotInGradient();
-    AddInput(
-        "Label",
+             "and K is the class number.");
+    AddInput("Label",
              "(Tensor, default: Tensor<int>), The ground truth which is a 2-D "
              "tensor. "
-        "If softLable is set to 0, Label is a Tensor<int> with shape [N x 1]. "
+             "If softLable is set to 0, Label is a Tensor<int> with shape [N x "
+             "1]. "
              "If softLable is set to 1, Label is a Tensor<float/double> "
              "with shape [N x K].");
     AddOutput(
@@ -163,14 +163,33 @@ class SoftmaxWithCrossEntropyOpGrad : public framework::OperatorWithKernel {
   }
 };
 
+class SoftmaxGradMaker : public framework::SingleGradOpDescMaker {
+ public:
+  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
+
+ protected:
+  std::unique_ptr<framework::OpDescBind> Apply() const override {
+    auto* grad_op = new framework::OpDescBind();
+    grad_op->SetType("softmax_with_cross_entropy_grad");
+    grad_op->SetInput("Label", Input("Label"));
+    grad_op->SetInput("Softmax", Output("Softmax"));
+    grad_op->SetInput("Loss", Output("Loss"));
+    grad_op->SetInput(framework::GradVarName("Softmax"), OutputGrad("Softmax"));
+    grad_op->SetInput(framework::GradVarName("Loss"), OutputGrad("Loss"));
+    grad_op->SetOutput(framework::GradVarName("Logits"), InputGrad("Logits"));
+    grad_op->SetAttrMap(Attrs());
+    return std::unique_ptr<framework::OpDescBind>(grad_op);
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
 
-REGISTER_OP(softmax_with_cross_entropy, ops::SoftmaxWithCrossEntropyOp,
-            ops::SoftmaxWithCrossEntropyOpMaker,
-            softmax_with_cross_entropy_grad,
+REGISTER_OPERATOR(softmax_with_cross_entropy, ops::SoftmaxWithCrossEntropyOp,
+                  ops::SoftmaxWithCrossEntropyOpMaker, ops::SoftmaxGradMaker);
+REGISTER_OPERATOR(softmax_with_cross_entropy_grad,
                   ops::SoftmaxWithCrossEntropyOpGrad);
 REGISTER_OP_CPU_KERNEL(softmax_with_cross_entropy,
                        ops::SoftmaxWithCrossEntropyKernel<float>);

paddle/operators/sum_op.cc

@@ -45,10 +45,8 @@ class SumOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   SumOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("X", "the input tensors of sum operator.")
-        .AsDuplicable()
-        .NotInGradient();
-    AddOutput("Out", "the output tensor of sum operator.").NotInGradient();
+    AddInput("X", "the input tensors of sum operator.").AsDuplicable();
+    AddOutput("Out", "the output tensor of sum operator.");
     AddComment(R"DOC(
 Sum the input tensors.
 
@@ -58,23 +56,26 @@ or not. But the output only shares the LoD with the first input.
   }
 };
 
-class SumGradOp : public NetOp {
+class SumGradMaker : public framework::GradOpDescMakerBase {
  public:
-  SumGradOp(const std::string& type, const framework::VariableNameMap& inputs,
-            const framework::VariableNameMap& outputs,
-            const framework::AttributeMap& attrs)
-      : NetOp(type, inputs, outputs, attrs) {
-    auto& x_grad_names = Outputs(framework::GradVarName("X"));
-    auto out_grad_name = this->Input(framework::GradVarName("Out"));
+  using framework::GradOpDescMakerBase::GradOpDescMakerBase;
 
-    framework::AttributeMap grad_attrs;
-    grad_attrs["scale"] = 1.0f;
-    for (auto& x_grad_name : x_grad_names) {
-      AppendOp(framework::OpRegistry::CreateOp(
-          "scale", {{"X", {out_grad_name}}}, {{"Out", {x_grad_name}}},
-          grad_attrs));
-    }
-    CompleteAddOp(false);
+  std::vector<std::unique_ptr<framework::OpDescBind>> operator()()
+      const override {
+    auto x_grads = InputGrad("X");
+    std::vector<std::unique_ptr<framework::OpDescBind>> grad_ops;
+    grad_ops.reserve(x_grads.size());
+    auto og = OutputGrad("Out");
+    std::transform(x_grads.begin(), x_grads.end(), std::back_inserter(grad_ops),
+                   [&og](const std::string& x_grad) {
+                     auto* grad_op = new framework::OpDescBind();
+                     grad_op->SetType("scale");
+                     grad_op->SetInput("X", og);
+                     grad_op->SetOutput("Out", {x_grad});
+                     grad_op->SetAttr("scale", 1.0f);
+                     return std::unique_ptr<framework::OpDescBind>(grad_op);
+                   });
+    return grad_ops;
   }
 };
 
@@ -82,5 +83,6 @@ class SumGradOp : public NetOp {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP(sum, ops::SumOp, ops::SumOpMaker, sum_grad, ops::SumGradOp);
+
+REGISTER_OPERATOR(sum, ops::SumOp, ops::SumOpMaker, ops::SumGradMaker);
 REGISTER_OP_CPU_KERNEL(sum, ops::SumKernel<paddle::platform::CPUPlace, float>);