Merge pull request #3067 from reyoung/make_network_op

Make network op

paddle/framework/CMakeLists.txt

@@ -29,7 +29,5 @@ py_proto_compile(framework_py_proto SRCS attr_type.proto op_proto.proto op_desc.
 add_custom_target(framework_py_proto_init ALL COMMAND ${CMAKE_COMMAND} -E touch __init__.py)
 add_dependencies(framework_py_proto framework_py_proto_init)
 
-proto_library(net_proto SRCS net_proto.proto DEPS op_proto)
-# cc_library(net SRCS net.cc DEPS operator net_proto op_registry fc_op)
-cc_library(net SRCS net.cc DEPS operator net_proto op_registry)
+cc_library(net SRCS net.cc DEPS op_registry)
 cc_test(net_op_test SRCS net_op_test.cc DEPS net add_op mul_op sigmoid_op softmax_op fc_op)

paddle/framework/net.cc

@@ -20,17 +20,7 @@
 namespace paddle {
 namespace framework {
 
-std::shared_ptr<PlainNet> AddBackwardOp(std::shared_ptr<PlainNet> ForwardOps) {
-  auto grad_ops = std::make_shared<PlainNet>();
-  for (auto& op : ForwardOps->ops_) {
-    auto op_grad = OpRegistry::CreateGradOp(op);
-    grad_ops->AddOp(op_grad);
-  }
-  grad_ops->CompleteAddOp();
-  return grad_ops;
-}
-
-void PlainNet::CompleteAddOp(bool calc) {
+void NetOp::CompleteAddOp(bool calc) {
   add_op_done_ = true;
   if (!calc) return;
   std::unordered_set<std::string> input_set;
@@ -70,7 +60,7 @@ void PlainNet::CompleteAddOp(bool calc) {
   attrs_["temporary_index"] = tmp_index;
 }
 
-std::string PlainNet::DebugString() const {
+std::string NetOp::DebugString() const {
   std::ostringstream os;
   os << OperatorBase::DebugString() << std::endl;
   for (auto& op : ops_) {
@@ -82,5 +72,7 @@ std::string PlainNet::DebugString() const {
   return os.str();
 }
 
+bool NetOp::IsNetOp() const { return true; }
+
 }  // namespace framework
 }  // namespace paddle

paddle/framework/net.h

@@ -37,21 +37,7 @@ namespace framework {
  * This is the base class of network, all the networks should implement the APIs
  * it defines.
  */
-class Net : public OperatorBase {
- public:
-  virtual void AddOp(const std::shared_ptr<OperatorBase>& op) = 0;
-  virtual void CompleteAddOp(bool calc) = 0;
-};
-
-using NetPtr = std::shared_ptr<Net>;
-
-/**
- * @brief a basic implementation of Net.
- *
- * PlainNet is a very simple Net, it create a list of operators, and run them
- * sequentially following the order they added.
- */
-class PlainNet : public Net {
+class NetOp : public OperatorBase {
  public:
   /**
    * Infer all the operators' input and output variables' shapes, will be called
@@ -80,15 +66,17 @@ class PlainNet : public Net {
   /**
    * @brief Add an operator by ptr
    */
-  void AddOp(const std::shared_ptr<OperatorBase>& op) override {
+  void AddOp(const std::shared_ptr<OperatorBase>& op) {
     PADDLE_ENFORCE(!add_op_done_, "Cannot AddOp when this network is sealed");
     ops_.push_back(op);
   }
 
-  void CompleteAddOp(bool calculate = true) override;
+  void CompleteAddOp(bool calculate = true);
 
   std::string DebugString() const override;
 
+  bool IsNetOp() const override;
+
   std::vector<std::shared_ptr<OperatorBase>> ops_;
 
  private:
@@ -100,7 +88,5 @@ class PlainNet : public Net {
   }
 };
 
-std::shared_ptr<PlainNet> AddBackwardOp(std::shared_ptr<PlainNet> ForwardOps);
-
 }  // namespace framework
 }  // namespace paddle

paddle/framework/net_op_test.cc

@@ -40,7 +40,7 @@ void AssertSameVectorWithoutOrder(const std::vector<T>& expected,
 }
 
 TEST(OpKernel, all) {
-  auto net = std::make_shared<PlainNet>();
+  auto net = std::make_shared<NetOp>();
   ASSERT_NE(net, nullptr);
 
   auto op1 = std::make_shared<TestOp>();
@@ -71,28 +71,21 @@ TEST(OpKernel, all) {
   ASSERT_EQ(2, run_cnt);
   ASSERT_THROW(net->AddOp(op2), paddle::platform::EnforceNotMet);
 }
-TEST(AddBackwardOp, TestGradOp) {
-  auto net = std::make_shared<PlainNet>();
-  ASSERT_NE(net, nullptr);
-  net->AddOp(framework::OpRegistry::CreateOp("mul", {"X", "Y"}, {"Out"}, {}));
-  net->AddOp(
-      framework::OpRegistry::CreateOp("add_two", {"X", "Y"}, {"Out"}, {}));
-  net->AddOp(framework::OpRegistry::CreateOp("add_two", {"X", "Y"}, {""}, {}));
-  auto grad_ops = AddBackwardOp(net);
-  for (auto& op : grad_ops->ops_) {
-    op->DebugString();
-  }
-}
 
-// TODO(zhihong): add fc grad without registering.
-// TEST(AddBackwardOp, TestNoGradOp) {
-//   auto net = std::make_shared<PlainNet>();
-//   ASSERT_NE(net, nullptr);
-//   net->AddOp(framework::OpRegistry::CreateOp("fc", {"X", "W", "b"}, {"Y"},
-//   {})); auto grad_ops = AddBackwardOp(net); for (auto& op : grad_ops->ops_) {
-//     op->DebugString();
-//   }
-// }
+//! TODO(yuyang18): Refine Backward Op.
+// TEST(AddBackwardOp, TestGradOp) {
+//  auto net = std::make_shared<NetOp>();
+//  ASSERT_NE(net, nullptr);
+//  net->AddOp(framework::OpRegistry::CreateOp("mul", {"X", "Y"}, {"Out"}, {}));
+//  net->AddOp(
+//      framework::OpRegistry::CreateOp("add_two", {"X", "Y"}, {"Out"}, {}));
+//  net->AddOp(framework::OpRegistry::CreateOp("add_two", {"X", "Y"}, {""},
+//  {}));
+//  auto grad_ops = AddBackwardOp(net);
+//  for (auto& op : grad_ops->ops_) {
+//    op->DebugString();
+//  }
+//}
 
 }  // namespace framework
 }  // namespace paddle

paddle/framework/net_proto.proto

-syntax="proto2";
-package paddle.framework;
-
-import "op_proto.proto";
-
-message NetDesc {
-  // network identification
-  optional string name = 1;
-  // operator contains in network
-  repeated OpProto operators = 2;
-  // network type to run with. e.g "plainNet", "DAG"
-  optional string net_type = 3;
-  // num worker always
-  optional int32 num_workers = 4;
-}

paddle/framework/operator.h

@@ -90,15 +90,17 @@ class OperatorBase {
   virtual void Run(const std::shared_ptr<Scope>& scope,
                    const platform::DeviceContext& dev_ctx) const = 0;
 
-  // Get a input with argument's name described in `op_proto`
+  virtual bool IsNetOp() const { return false; }
+
+  //! Get a input with argument's name described in `op_proto`
   const std::string& Input(const std::string& name) const;
-  // Get a input which has multiple variables.
-  // TODO add a vector_view to prevent memory copy.
+  //! Get a input which has multiple variables.
+  //! TODO add a vector_view to prevent memory copy.
   std::vector<std::string> Inputs(const std::string& name) const;
-  // Get a output with argument's name described in `op_proto`
+  //! Get a output with argument's name described in `op_proto`
   const std::string& Output(const std::string& name) const;
-  // Get an output which has multiple variables.
-  // TODO add a vector_view to prevent memory copy.
+  //! Get an output which has multiple variables.
+  //! TODO add a vector_view to prevent memory copy.
   std::vector<std::string> Outputs(const std::string& name) const;
 
  public:

paddle/operators/add_op.cc

@@ -13,17 +13,14 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/operators/add_op.h"
-#include "paddle/framework/op_registry.h"
-#include "paddle/framework/tensor.h"
 
 namespace paddle {
 namespace operators {
 
-class AddOp : public framework::OperatorWithKernel {
+class AddOp : public OperatorWithKernel {
 protected:
-  void InferShape(
-      const std::vector<const framework::Tensor *> &inputs,
-      const std::vector<framework::Tensor *> &outputs) const override {
+  void InferShape(const std::vector<const Tensor *> &inputs,
+                  const std::vector<Tensor *> &outputs) const override {
     PADDLE_ENFORCE(inputs.size() == 2, "Input size of AddOp must be two");
     PADDLE_ENFORCE(outputs.size() == 1, "Output size of AddOp must be one");
     PADDLE_ENFORCE(
@@ -35,10 +32,10 @@ protected:
   }
 };
 
-class AddOpMaker : public framework::OpProtoAndCheckerMaker {
+class AddOpMaker : public OpProtoAndCheckerMaker {
 public:
-  AddOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
-      : framework::OpProtoAndCheckerMaker(proto, op_checker) {
+  AddOpMaker(OpProto *proto, OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X", "The first input of add op");
     AddInput("Y", "The second input of add op");
     AddOutput("Out", "The output of add op");
@@ -50,11 +47,10 @@ The equation is: Out = X + Y
   }
 };
 
-class AddOpGrad : public framework::OperatorWithKernel {
+class AddOpGrad : public OperatorWithKernel {
 protected:
-  void InferShape(
-      const std::vector<const framework::Tensor *> &inputs,
-      const std::vector<framework::Tensor *> &outputs) const override {}
+  void InferShape(const std::vector<const Tensor *> &inputs,
+                  const std::vector<Tensor *> &outputs) const override {}
   std::string DebugString() const override {
     LOG(INFO) << "AddOpGrad";
     return "";
@@ -64,7 +60,6 @@ protected:
 }  // namespace operators
 }  // namespace paddle
 
-REGISTER_OP(add_two, paddle::operators::AddOp, paddle::operators::AddOpMaker);
-REGISTER_GRADIENT_OP(add_two, add_two_grad, paddle::operators::AddOpGrad);
-REGISTER_OP_CPU_KERNEL(
-    add_two, paddle::operators::AddKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP(add_two, ops::AddOp, ops::AddOpMaker);
+REGISTER_GRADIENT_OP(add_two, add_two_grad, ops::AddOpGrad);
+REGISTER_OP_CPU_KERNEL(add_two, ops::AddKernel<ops::CPUPlace, float>);

paddle/operators/add_op.cu

-#include "paddle/operators/add_op.h"
 #include "paddle/framework/op_registry.h"
+#include "paddle/operators/add_op.h"
 
-REGISTER_OP_GPU_KERNEL(add_two,
-                       paddle::operators::AddKernel<paddle::platform::GPUPlace, float>);
\ No newline at end of file
+REGISTER_OP_GPU_KERNEL(add_two, ops::AddKernel<ops::GPUPlace, float>);

paddle/operators/add_op.h

@@ -13,27 +13,24 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
-#include "glog/logging.h"
-#include "paddle/framework/eigen.h"
-#include "paddle/framework/operator.h"
+#include "paddle/operators/type_alias.h"
 
 namespace paddle {
 namespace operators {
 
 template <typename Place, typename T>
-class AddKernel : public framework::OpKernel {
+class AddKernel : public OpKernel {
 public:
-  void Compute(const framework::KernelContext& context) const override {
-    auto input0 = context.Input(0)->Get<framework::Tensor>();
-    auto input1 = context.Input(1)->Get<framework::Tensor>();
-    auto* output = context.Output(0)->GetMutable<framework::Tensor>();
+  void Compute(const KernelContext& context) const override {
+    auto input0 = context.Input(0)->Get<Tensor>();
+    auto input1 = context.Input(1)->Get<Tensor>();
+    auto output = context.Output(0)->GetMutable<Tensor>();
 
     output->mutable_data<T>(context.GetPlace());
 
-    framework::EigenVector<T>::Flatten(*output).device(
+    EigenVector<T>::Flatten(*output).device(
         *(context.GetEigenDevice<Place>())) =
-        framework::EigenVector<T>::Flatten(input0) +
-        framework::EigenVector<T>::Flatten(input1);
+        EigenVector<T>::Flatten(input0) + EigenVector<T>::Flatten(input1);
   }
 };
 

paddle/operators/cross_entropy_op.cc

@@ -13,17 +13,14 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/operators/cross_entropy_op.h"
-#include "paddle/framework/op_registry.h"
-#include "paddle/framework/tensor.h"
 
 namespace paddle {
 namespace operators {
 
-class OnehotCrossEntropyOp : public framework::OperatorWithKernel {
+class OnehotCrossEntropyOp : public OperatorWithKernel {
 protected:
-  void InferShape(
-      const std::vector<const framework::Tensor *> &inputs,
-      const std::vector<framework::Tensor *> &outputs) const override {
+  void InferShape(const std::vector<const Tensor *> &inputs,
+                  const std::vector<Tensor *> &outputs) const override {
     PADDLE_ENFORCE(inputs.size() == 2,
                    "Input size of OnehotCrossEntropyOp must be two");
     PADDLE_ENFORCE(outputs.size() == 1,
@@ -35,15 +32,14 @@ protected:
     PADDLE_ENFORCE(inputs[0]->dims().size() == 2, "X's dimension must be 2.");
     PADDLE_ENFORCE(outputs[0]->dims().size() == 1,
                    "label's dimension must be 1.");
-    outputs[0]->Resize(framework::make_ddim({inputs[0]->dims()[0]}));
+    outputs[0]->Resize({inputs[0]->dims()[0]});
   }
 };
 
-class OnehotCrossEntropyOpMaker : public framework::OpProtoAndCheckerMaker {
+class OnehotCrossEntropyOpMaker : public OpProtoAndCheckerMaker {
 public:
-  OnehotCrossEntropyOpMaker(framework::OpProto *proto,
-                            framework::OpAttrChecker *op_checker)
-      : framework::OpProtoAndCheckerMaker(proto, op_checker) {
+  OnehotCrossEntropyOpMaker(OpProto *proto, OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X", "The first input of OnehotCrossEntropyOp");
     AddInput("label", "The second input of OnehotCrossEntropyOp");
     AddOutput("Y", "The output of OnehotCrossEntropyOp");
@@ -59,9 +55,7 @@ OnehotCrossEntropy Operator.
 }  // namespace paddle
 
 REGISTER_OP(onehot_cross_entropy,
-            paddle::operators::OnehotCrossEntropyOp,
-            paddle::operators::OnehotCrossEntropyOpMaker);
-REGISTER_OP_CPU_KERNEL(
-    onehot_cross_entropy,
-    paddle::operators::OnehotCrossEntropyOpKernel<::paddle::platform::CPUPlace,
-                                                  float>);
+            ops::OnehotCrossEntropyOp,
+            ops::OnehotCrossEntropyOpMaker);
+REGISTER_OP_CPU_KERNEL(onehot_cross_entropy,
+                       ops::OnehotCrossEntropyOpKernel<ops::CPUPlace, float>);

paddle/operators/cross_entropy_op.cu

 #include "paddle/operators/cross_entropy_op.h"
-#include "paddle/framework/op_registry.h"
 
 REGISTER_OP_GPU_KERNEL(onehot_cross_entropy,
-                       paddle::operators::OnehotCrossEntropyOpKernel<
-                            ::paddle::platform::GPUPlace, float>);
\ No newline at end of file
+                       ops::OnehotCrossEntropyOpKernel<ops::GPUPlace, float>);
\ No newline at end of file

paddle/operators/cross_entropy_op.h

@@ -13,23 +13,21 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
-#include "glog/logging.h"
-#include "paddle/framework/operator.h"
+#include "paddle/operators/type_alias.h"
 
 namespace paddle {
 namespace operators {
 
 template <typename Place, typename T>
-class OnehotCrossEntropyOpKernel : public framework::OpKernel {
+class OnehotCrossEntropyOpKernel : public OpKernel {
 public:
   constexpr T LOG_THRESHOLD() const { return static_cast<T>(1e-20); }
 
-  void Compute(const framework::KernelContext& context) const override {
-    auto X = context.Input(0)->Get<framework::Tensor>();
+  void Compute(const KernelContext& context) const override {
+    auto X = context.Input(0)->Get<Tensor>();
     const T* X_data = X.data<T>();
-    const int* label_data =
-        context.Input(1)->Get<framework::Tensor>().data<int>();
-    auto* Y = context.Output(0)->GetMutable<framework::Tensor>();
+    const int* label_data = context.Input(1)->Get<Tensor>().data<int>();
+    auto* Y = context.Output(0)->GetMutable<Tensor>();
 
     Y->mutable_data<T>(context.GetPlace());
 

paddle/operators/fc_op.cc

@@ -12,41 +12,38 @@
    See the License for the specific language governing permissions and
    limitations under the License. */
 
-#include "paddle/framework/net.h"
-#include "paddle/framework/op_registry.h"
-#include "paddle/framework/operator.h"
+#include "type_alias.h"
 
 namespace paddle {
 namespace operators {
 
-class FullyConnectedOp : public framework::PlainNet {
+class FullyConnectedOp : public NetOp {
 public:
   void Init() override {
-    AddOp(framework::OpRegistry::CreateOp("mul",
-                                          {
-                                              Input("X"), Input("W"),
-                                          },
-                                          {Output("before_act")},
-                                          {}));
+    AddOp(OpRegistry::CreateOp("mul",
+                               {
+                                   Input("X"), Input("W"),
+                               },
+                               {Output("before_act")},
+                               {}));
     auto b = Input("b");
-    if (b != framework::OperatorBase::EMPTY_VAR_NAME()) {
-      AddOp(framework::OpRegistry::CreateOp("rowwise_add",
-                                            {Output("before_act"), Input("b")},
-                                            {Output("before_act")},
-                                            {}));
+    if (b != EMPTY_VAR_NAME()) {
+      AddOp(OpRegistry::CreateOp("rowwise_add",
+                                 {Output("before_act"), Input("b")},
+                                 {Output("before_act")},
+                                 {}));
     }
 
     auto activation = GetAttr<std::string>("activation");
-    AddOp(framework::OpRegistry::CreateOp(
+    AddOp(OpRegistry::CreateOp(
         activation, {Output("before_act")}, {Output("Y")}, {}));
     CompleteAddOp(false);
   }
 };
 
-class FullyConnectedOpMaker : public framework::OpProtoAndCheckerMaker {
+class FullyConnectedOpMaker : public OpProtoAndCheckerMaker {
 public:
-  FullyConnectedOpMaker(framework::OpProto *proto,
-                        framework::OpAttrChecker *op_checker)
+  FullyConnectedOpMaker(OpProto *proto, OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X", "the input of fc operator");
     AddInput("W", "the weight of fc operator");
@@ -71,6 +68,4 @@ USE_OP(rowwise_add);
 USE_OP(sigmoid);
 USE_OP(softmax);
 
-REGISTER_OP(fc,
-            paddle::operators::FullyConnectedOp,
-            paddle::operators::FullyConnectedOpMaker);
+REGISTER_OP(fc, ops::FullyConnectedOp, ops::FullyConnectedOpMaker);

paddle/operators/mul_op.cc

@@ -13,17 +13,14 @@
    limitations under the License. */
 
 #include "paddle/operators/mul_op.h"
-#include "paddle/framework/op_registry.h"
-#include "paddle/framework/tensor.h"
 
 namespace paddle {
 namespace operators {
 
-class MulOp : public framework::OperatorWithKernel {
+class MulOp : public OperatorWithKernel {
 protected:
-  void InferShape(
-      const std::vector<const framework::Tensor *> &inputs,
-      const std::vector<framework::Tensor *> &outputs) const override {
+  void InferShape(const std::vector<const Tensor *> &inputs,
+                  const std::vector<Tensor *> &outputs) const override {
     PADDLE_ENFORCE(inputs.size() == 2, "The mul op must take two inputs");
     auto dim0 = inputs[0]->dims();
     auto dim1 = inputs[1]->dims();
@@ -37,10 +34,10 @@ protected:
   }
 };
 
-class MulOpMaker : public framework::OpProtoAndCheckerMaker {
+class MulOpMaker : public OpProtoAndCheckerMaker {
 public:
-  MulOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
-      : framework::OpProtoAndCheckerMaker(proto, op_checker) {
+  MulOpMaker(OpProto *proto, OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X", "The first input of mul op");
     AddInput("Y", "The second input of mul op");
     AddOutput("Out", "The output of mul op");
@@ -52,11 +49,10 @@ The equation is: Out = X * Y
   }
 };
 
-class MulOpGrad : public framework::OperatorWithKernel {
+class MulOpGrad : public OperatorWithKernel {
 protected:
-  void InferShape(
-      const std::vector<const framework::Tensor *> &inputs,
-      const std::vector<framework::Tensor *> &outputs) const override {}
+  void InferShape(const std::vector<const Tensor *> &inputs,
+                  const std::vector<Tensor *> &outputs) const override {}
   std::string DebugString() const override {
     LOG(INFO) << "MulGrad";
     return "";
@@ -66,8 +62,7 @@ protected:
 }  // namespace operators
 }  // namespace paddle
 
-REGISTER_OP(mul, paddle::operators::MulOp, paddle::operators::MulOpMaker);
-REGISTER_GRADIENT_OP(mul, mul_grad, paddle::operators::MulOpGrad);
+REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker);
+REGISTER_GRADIENT_OP(mul, mul_grad, ops::MulOpGrad);
 
-REGISTER_OP_CPU_KERNEL(
-    mul, paddle::operators::MulKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(mul, ops::MulKernel<ops::CPUPlace, float>);

paddle/operators/mul_op.cu

@@ -13,8 +13,5 @@
    limitations under the License. */
 
 #include "paddle/operators/mul_op.h"
-#include "paddle/framework/op_registry.h"
 
-REGISTER_OP_GPU_KERNEL(mul,
-                       paddle::operators::MulKernel<paddle::platform
-                       ::GPUPlace, float>);
\ No newline at end of file
+REGISTER_OP_GPU_KERNEL(mul, ops::MulKernel<ops::GPUPlace, float>);
\ No newline at end of file

paddle/operators/mul_op.h

@@ -14,30 +14,27 @@
 
 #pragma once
 
-#include "glog/logging.h"
-#include "paddle/framework/eigen.h"
-#include "paddle/framework/operator.h"
+#include "paddle/operators/type_alias.h"
 
 namespace paddle {
 namespace operators {
 
 template <typename Place, typename T>
-class MulKernel : public framework::OpKernel {
+class MulKernel : public OpKernel {
 public:
-  void Compute(const framework::KernelContext& context) const override {
+  void Compute(const KernelContext& context) const override {
     Eigen::array<Eigen::IndexPair<Eigen::DenseIndex>, 1> dim_pair = {
         {Eigen::IndexPair<Eigen::DenseIndex>(1, 0)}};
 
-    auto input0 = context.Input(0)->Get<framework::Tensor>();
-    auto input1 = context.Input(1)->Get<framework::Tensor>();
-    auto* output = context.Output(0)->GetMutable<framework::Tensor>();
+    auto input0 = context.Input(0)->Get<Tensor>();
+    auto input1 = context.Input(1)->Get<Tensor>();
+    auto* output = context.Output(0)->GetMutable<Tensor>();
 
     output->mutable_data<T>(context.GetPlace());
 
-    framework::EigenMatrix<T>::From(*output).device(
-        *(context.GetEigenDevice<Place>())) =
-        framework::EigenMatrix<T>::From(input0).contract(
-            framework::EigenMatrix<T>::From(input1), dim_pair);
+    EigenMatrix<T>::From(*output).device(*(context.GetEigenDevice<Place>())) =
+        EigenMatrix<T>::From(input0).contract(EigenMatrix<T>::From(input1),
+                                              dim_pair);
   }
 };
 }  // namespace operators

paddle/operators/rowwise_add_op.cc

@@ -13,15 +13,13 @@
    limitations under the License. */
 
 #include "paddle/operators/rowwise_add_op.h"
-#include "paddle/framework/op_registry.h"
 namespace paddle {
 namespace operators {
 
-class RowWiseAddOp : public framework::OperatorWithKernel {
+class RowWiseAddOp : public OperatorWithKernel {
 protected:
-  void InferShape(
-      const std::vector<const framework::Tensor *> &inputs,
-      const std::vector<framework::Tensor *> &outputs) const override {
+  void InferShape(const std::vector<const Tensor *> &inputs,
+                  const std::vector<Tensor *> &outputs) const override {
     PADDLE_ENFORCE(inputs.size() == 2UL, "Two inputs is needed by rowwise add");
     auto dim0 = inputs[0]->dims();
     auto dim1 = inputs[1]->dims();
@@ -34,11 +32,10 @@ protected:
   }
 };
 
-class RowWiseAddOpMaker : public framework::OpProtoAndCheckerMaker {
+class RowWiseAddOpMaker : public OpProtoAndCheckerMaker {
 public:
-  RowWiseAddOpMaker(framework::OpProto *proto,
-                    framework::OpAttrChecker *op_checker)
-      : framework::OpProtoAndCheckerMaker(proto, op_checker) {
+  RowWiseAddOpMaker(OpProto *proto, OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X", "The left input of row-wise add op, must be matrix");
     AddInput("b", "The right input of row-wise add op, must be vector");
     AddOutput("Out", "The output of row-wise add op");
@@ -53,9 +50,6 @@ for i in xrange(X.shape[0]):
 }  // namespace operators
 }  // namespace paddle
 
-REGISTER_OP(rowwise_add,
-            paddle::operators::RowWiseAddOp,
-            paddle::operators::RowWiseAddOpMaker);
-REGISTER_OP_CPU_KERNEL(
-    rowwise_add,
-    paddle::operators::RowWiseAddKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP(rowwise_add, ops::RowWiseAddOp, ops::RowWiseAddOpMaker);
+REGISTER_OP_CPU_KERNEL(rowwise_add,
+                       ops::RowWiseAddKernel<ops::CPUPlace, float>);

paddle/operators/rowwise_add_op.cu

-#include "paddle/framework/op_registry.h"
 #include "paddle/operators/rowwise_add_op.h"
 
-REGISTER_OP_GPU_KERNEL(
-    rowwise_add,
-    paddle::operators::RowWiseAddKernel<paddle::platform ::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(rowwise_add,
+                       ops::RowWiseAddKernel<ops::GPUPlace, float>);

paddle/operators/rowwise_add_op.h

@@ -13,25 +13,23 @@
    limitations under the License. */
 
 #pragma once
-#include "glog/logging.h"
-#include "paddle/framework/eigen.h"
-#include "paddle/framework/operator.h"
+#include "paddle/operators/type_alias.h"
 
 namespace paddle {
 namespace operators {
 
 template <typename Place, typename T>
-class RowWiseAddKernel : public framework::OpKernel {
+class RowWiseAddKernel : public OpKernel {
 public:
-  void Compute(const framework::KernelContext& context) const override {
-    auto in0 = context.Input(0)->Get<framework::Tensor>();
-    auto in1 = context.Input(1)->Get<framework::Tensor>();
-    auto* out = context.Output(0)->GetMutable<framework::Tensor>();
+  void Compute(const KernelContext& context) const override {
+    auto in0 = context.Input(0)->Get<Tensor>();
+    auto in1 = context.Input(1)->Get<Tensor>();
+    auto* out = context.Output(0)->GetMutable<Tensor>();
     out->mutable_data<T>(context.GetPlace());
 
-    auto input = framework::EigenMatrix<T>::From(in0);
-    auto bias = framework::EigenVector<T>::From(in1);
-    auto output = framework::EigenMatrix<T>::From(*out);
+    auto input = EigenMatrix<T>::From(in0);
+    auto bias = EigenVector<T>::From(in1);
+    auto output = EigenMatrix<T>::From(*out);
 
     const int bias_size = bias.dimension(0);
     const int rest_size = input.size() / bias_size;

paddle/operators/sgd_op.cc

@@ -13,17 +13,14 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/operators/sgd_op.h"
-#include "paddle/framework/op_registry.h"
-#include "paddle/framework/tensor.h"
 
 namespace paddle {
 namespace operators {
 
-class SGDOp : public framework::OperatorWithKernel {
+class SGDOp : public OperatorWithKernel {
 protected:
-  void InferShape(
-      const std::vector<const framework::Tensor *> &inputs,
-      const std::vector<framework::Tensor *> &outputs) const override {
+  void InferShape(const std::vector<const Tensor *> &inputs,
+                  const std::vector<Tensor *> &outputs) const override {
     PADDLE_ENFORCE(inputs.size() == 2, "Input size of SGDOp must be two");
     PADDLE_ENFORCE(outputs.size() == 1, "Output size of SGDOp must be one");
     PADDLE_ENFORCE(inputs[0] != nullptr, "inputs[0] mast be set");
@@ -35,10 +32,10 @@ protected:
   }
 };
 
-class SGDOpMaker : public framework::OpProtoAndCheckerMaker {
+class SGDOpMaker : public OpProtoAndCheckerMaker {
 public:
-  SGDOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
-      : framework::OpProtoAndCheckerMaker(proto, op_checker) {
+  SGDOpMaker(OpProto *proto, OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("param", "input parameter");
     AddInput("grad", "input gradient");
     AddOutput("param_out", "output parameter");
@@ -55,7 +52,5 @@ param_out = param - learning_rate * grad;
 }  // namespace operators
 }  // namespace paddle
 
-REGISTER_OP(sgd, paddle::operators::SGDOp, paddle::operators::SGDOpMaker);
-typedef paddle::operators::SGDOpKernel<::paddle::platform::CPUPlace, float>
-    SGDOpKernel_CPU_float;
-REGISTER_OP_CPU_KERNEL(sgd, SGDOpKernel_CPU_float);
+REGISTER_OP(sgd, ops::SGDOp, ops::SGDOpMaker);
+REGISTER_OP_CPU_KERNEL(sgd, ops::SGDOpKernel<ops::CPUPlace, float>);

paddle/operators/sgd_op.cu

 #include "paddle/operators/sgd_op.h"
-#include "paddle/framework/op_registry.h"
 
-typedef paddle::operators::SGDOpKernel<::paddle::platform::GPUPlace, float> SGDOpKernel_GPU_float;
-REGISTER_OP_GPU_KERNEL(sgd, SGDOpKernel_GPU_float);
\ No newline at end of file
+REGISTER_OP_GPU_KERNEL(sgd, ops::SGDOpKernel<ops::GPUPlace, float>);
\ No newline at end of file

paddle/operators/sgd_op.h

@@ -13,28 +13,24 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
-#include "glog/logging.h"
-#include "paddle/framework/eigen.h"
-#include "paddle/framework/operator.h"
+#include "paddle/operators/type_alias.h"
 
 namespace paddle {
 namespace operators {
 
 template <typename Place, typename T>
-class SGDOpKernel : public framework::OpKernel {
+class SGDOpKernel : public OpKernel {
 public:
-  void Compute(const framework::KernelContext& ctx) const override {
-    auto param = ctx.Input("param")->Get<framework::Tensor>();
-    auto grad = ctx.Input("grad")->Get<framework::Tensor>();
-    auto* param_out = ctx.Output(0)->GetMutable<framework::Tensor>();
+  void Compute(const KernelContext& ctx) const override {
+    auto param = ctx.Input("param")->Get<Tensor>();
+    auto grad = ctx.Input("grad")->Get<Tensor>();
+    auto* param_out = ctx.Output(0)->GetMutable<Tensor>();
     float lr = ctx.op_.GetAttr<float>("learning_rate");
 
     param_out->mutable_data<T>(ctx.GetPlace());
 
-    framework::EigenVector<T>::Flatten(*param_out)
-        .device(*(ctx.GetEigenDevice<Place>())) =
-        framework::EigenVector<T>::Flatten(param) -
-        lr * framework::EigenVector<T>::Flatten(grad);
+    EigenVector<T>::Flatten(*param_out).device(*(ctx.GetEigenDevice<Place>())) =
+        EigenVector<T>::Flatten(param) - lr * EigenVector<T>::Flatten(grad);
   }
 };
 

paddle/operators/sigmoid_op.cc

@@ -13,37 +13,33 @@
    limitations under the License. */
 
 #include "paddle/operators/sigmoid_op.h"
-#include "paddle/framework/op_registry.h"
 namespace paddle {
 namespace operators {
 
-class SigmoidOp : public framework::OperatorWithKernel {
+class SigmoidOp : public OperatorWithKernel {
 protected:
-  void InferShape(
-      const std::vector<const framework::Tensor *> &inputs,
-      const std::vector<framework::Tensor *> &outputs) const override {
+  void InferShape(const std::vector<const Tensor *> &inputs,
+                  const std::vector<Tensor *> &outputs) const override {
     PADDLE_ENFORCE(inputs.size() == 1, "Sigmoid Op only have one input");
     PADDLE_ENFORCE(outputs.size() == 1, "Sigmoid Op only have one output");
     outputs[0]->Resize(inputs[0]->dims());
   }
 };
 
-class SigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
+class SigmoidOpMaker : public OpProtoAndCheckerMaker {
 public:
-  SigmoidOpMaker(framework::OpProto *proto,
-                 framework::OpAttrChecker *op_checker)
-      : framework::OpProtoAndCheckerMaker(proto, op_checker) {
+  SigmoidOpMaker(OpProto *proto, OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X", "sigmoid input");
     AddOutput("Y", "sigmoid output");
     AddComment("Sigmoid function");
   }
 };
 
-class SigmoidOpGrad : public framework::OperatorWithKernel {
+class SigmoidOpGrad : public OperatorWithKernel {
 protected:
-  void InferShape(
-      const std::vector<const framework::Tensor *> &inputs,
-      const std::vector<framework::Tensor *> &outputs) const override {}
+  void InferShape(const std::vector<const Tensor *> &inputs,
+                  const std::vector<Tensor *> &outputs) const override {}
   std::string DebugString() const override {
     LOG(INFO) << "SigmoidGrad";
     return "";
@@ -53,11 +49,7 @@ protected:
 }  // namespace operators
 }  // namespace paddle
 
-REGISTER_OP(sigmoid,
-            paddle::operators::SigmoidOp,
-            paddle::operators::SigmoidOpMaker);
-REGISTER_GRADIENT_OP(sigmoid, sigmoid_grad, paddle::operators::SigmoidOpGrad);
+REGISTER_OP(sigmoid, ops::SigmoidOp, ops::SigmoidOpMaker);
+REGISTER_GRADIENT_OP(sigmoid, sigmoid_grad, ops::SigmoidOpGrad);
 
-REGISTER_OP_CPU_KERNEL(
-    sigmoid,
-    paddle::operators::SigmoidKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(sigmoid, ops::SigmoidKernel<ops::CPUPlace, float>);

paddle/operators/sigmoid_op.cu

 #include "paddle/operators/sigmoid_op.h"
-#include "paddle/framework/op_registry.h"
 
-REGISTER_OP_GPU_KERNEL(
-    sigmoid, paddle::operators::SigmoidKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(sigmoid, ops::SigmoidKernel<ops::GPUPlace, float>);

paddle/operators/sigmoid_op.h

@@ -14,25 +14,23 @@
 
 #pragma once
 
-#include "glog/logging.h"
-#include "paddle/framework/eigen.h"
-#include "paddle/framework/operator.h"
+#include "paddle/operators/type_alias.h"
 
 namespace paddle {
 namespace operators {
 
 template <typename Place, typename T>
-class SigmoidKernel : public framework::OpKernel {
+class SigmoidKernel : public OpKernel {
 public:
-  void Compute(const framework::KernelContext& context) const override {
-    auto input = context.Input(0)->Get<framework::Tensor>();
-    auto* output = context.Output(0)->GetMutable<framework::Tensor>();
+  void Compute(const KernelContext& context) const override {
+    auto input = context.Input(0)->Get<Tensor>();
+    auto* output = context.Output(0)->GetMutable<Tensor>();
 
     output->mutable_data<T>(context.GetPlace());
 
-    framework::EigenVector<T>::Flatten(*output).device(
+    EigenVector<T>::Flatten(*output).device(
         *(context.GetEigenDevice<Place>())) =
-        1.0 / (1.0 + (-1.0 * framework::EigenVector<T>::Flatten(input)).exp());
+        1.0 / (1.0 + (-1.0 * EigenVector<T>::Flatten(input)).exp());
   }
 };
 }  // namespace operators

paddle/operators/softmax_op.cc

@@ -12,16 +12,14 @@
    See the License for the specific language governing permissions and
    limitations under the License. */
 #include "paddle/operators/softmax_op.h"
-#include "paddle/framework/op_registry.h"
 
 namespace paddle {
 namespace operators {
 
-class SoftmaxOp : public framework::OperatorWithKernel {
+class SoftmaxOp : public OperatorWithKernel {
 protected:
-  void InferShape(
-      const std::vector<const framework::Tensor *> &inputs,
-      const std::vector<framework::Tensor *> &outputs) const override {
+  void InferShape(const std::vector<const Tensor *> &inputs,
+                  const std::vector<Tensor *> &outputs) const override {
     PADDLE_ENFORCE(inputs.size() == 1, "Only one input is need for softmax");
     PADDLE_ENFORCE(inputs[0]->dims().size() == 2,
                    "The input of softmax op must be matrix");
@@ -31,10 +29,9 @@ protected:
   }
 };
 
-class SoftmaxOpMaker : public framework::OpProtoAndCheckerMaker {
+class SoftmaxOpMaker : public OpProtoAndCheckerMaker {
 public:
-  SoftmaxOpMaker(framework::OpProto *proto,
-                 framework::OpAttrChecker *op_checker)
+  SoftmaxOpMaker(OpProto *proto, OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X", "input of softmax");
     AddOutput("Y", "output of softmax");
@@ -42,11 +39,10 @@ public:
   }
 };
 
-class SoftmaxOpGrad : public framework::OperatorWithKernel {
+class SoftmaxOpGrad : public OperatorWithKernel {
 protected:
-  void InferShape(
-      const std::vector<const framework::Tensor *> &inputs,
-      const std::vector<framework::Tensor *> &outputs) const override {}
+  void InferShape(const std::vector<const Tensor *> &inputs,
+                  const std::vector<Tensor *> &outputs) const override {}
   std::string DebugString() const override {
     LOG(INFO) << "SoftmaxOpGrad";
     return "";
@@ -56,9 +52,6 @@ protected:
 }  // namespace operators
 }  // namespace paddle
 
-namespace ops = paddle::operators;
-
 REGISTER_OP(softmax, ops::SoftmaxOp, ops::SoftmaxOpMaker);
-REGISTER_GRADIENT_OP(softmax, softmax_grad, paddle::operators::SoftmaxOpGrad);
-REGISTER_OP_CPU_KERNEL(softmax,
-                       ops::SoftmaxKernel<paddle::platform::CPUPlace, float>);
+REGISTER_GRADIENT_OP(softmax, softmax_grad, ops::SoftmaxOpGrad);
+REGISTER_OP_CPU_KERNEL(softmax, ops::SoftmaxKernel<ops::CPUPlace, float>);

paddle/operators/softmax_op.cu

 #include "paddle/framework/op_registry.h"
 #include "paddle/operators/softmax_op.h"
 
-REGISTER_OP_GPU_KERNEL(
-    softmax, paddle::operators::SoftmaxKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(softmax, ops::SoftmaxKernel<ops::GPUPlace, float>);

paddle/operators/softmax_op.h

@@ -14,23 +14,21 @@
 
 #pragma once
 
-#include "glog/logging.h"
-#include "paddle/framework/eigen.h"
-#include "paddle/framework/operator.h"
+#include "paddle/operators/type_alias.h"
 
 namespace paddle {
 namespace operators {
 
 template <typename Place, typename T>
-class SoftmaxKernel : public framework::OpKernel {
+class SoftmaxKernel : public OpKernel {
 public:
-  void Compute(const framework::KernelContext& context) const override {
-    auto input = context.Input(0)->Get<framework::Tensor>();
-    auto* output = context.Output(0)->GetMutable<framework::Tensor>();
+  void Compute(const KernelContext& context) const override {
+    auto input = context.Input(0)->Get<Tensor>();
+    auto* output = context.Output(0)->GetMutable<Tensor>();
     output->mutable_data<T>(context.GetPlace());
 
-    auto logits = framework::EigenMatrix<T>::From(input);
-    auto softmax = framework::EigenMatrix<T>::From(*output);
+    auto logits = EigenMatrix<T>::From(input);
+    auto softmax = EigenMatrix<T>::From(*output);
 
     const int kBatchDim = 0;
     const int kClassDim = 1;

paddle/operators/type_alias.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/eigen.h"
+#include "paddle/framework/net.h"
+#include "paddle/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+using OpKernel = framework::OpKernel;
+using KernelContext = framework::KernelContext;
+template <typename T,
+          int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
+template <typename T,
+          int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
+template <typename T,
+          size_t D,
+          int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
+using Tensor = framework::Tensor;
+using OperatorWithKernel = framework::OperatorWithKernel;
+using OpProtoAndCheckerMaker = framework::OpProtoAndCheckerMaker;
+using OpProto = framework::OpProto;
+using OpAttrChecker = framework::OpAttrChecker;
+using CPUPlace = platform::CPUPlace;
+using GPUPlace = platform::GPUPlace;
+using NetOp = framework::NetOp;
+using OpRegistry = framework::OpRegistry;
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;

paddle/pybind/pybind.cc

@@ -146,22 +146,22 @@ All parameter, weight, gradient are variables in Paddle.
   });
   ExposeOperator(operator_base);
 
-  using PlainNetPtr = std::shared_ptr<pd::PlainNet>;
-  py::class_<pd::PlainNet, PlainNetPtr> net(m, "Net");
+  py::class_<pd::NetOp, std::shared_ptr<pd::NetOp>> net(m, "Net");
 
   net.def_static("create",
-                 []() -> std::shared_ptr<pd::PlainNet> {
-                   auto retv = std::make_shared<pd::PlainNet>();
+                 []() -> std::shared_ptr<pd::NetOp> {
+                   auto retv = std::make_shared<pd::NetOp>();
                    retv->type_ = "plain_net";
                    return retv;
                  })
-      .def("add_op", &pd::PlainNet::AddOp)
+      .def("add_op", &pd::NetOp::AddOp)
       .def("add_op",
-           [](PlainNetPtr& self, const PlainNetPtr& net) -> void {
-             self->AddOp(std::static_pointer_cast<pd::OperatorBase>(net));
+           [](pd::NetOp& self, const std::shared_ptr<pd::NetOp>& net) -> void {
+             self.AddOp(std::static_pointer_cast<pd::OperatorBase>(net));
            })
-      .def("complete_add_op", &pd::PlainNet::CompleteAddOp)
-      .def("complete_add_op", [](PlainNetPtr& self) { self->CompleteAddOp(); });
+      .def("complete_add_op", &pd::NetOp::CompleteAddOp)
+      .def("complete_add_op",
+           [](std::shared_ptr<pd::NetOp>& self) { self->CompleteAddOp(); });
   ExposeOperator(net);
 
   m.def("unique_integer", UniqueIntegerGenerator);