Merge remote-tracking branch 'origin/develop' into random_op

paddle/framework/backward.cc

@@ -13,6 +13,7 @@
    limitations under the License. */
 
 #include "paddle/framework/backward.h"
+
 #include <list>
 #include "paddle/framework/op_registry.h"
 #include "paddle/operators/net_op.h"

paddle/framework/backward_test.cc

@@ -17,16 +17,21 @@
 #include <gtest/gtest.h>
 #include "paddle/framework/op_registry.h"
 #include "paddle/operators/net_op.h"
-#include "paddle/operators/type_alias.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 EmptyOp : public OperatorBase {
  public:
   void InferShape(const Scope &scope) const override {}
-  void Run(const Scope &scope,
-           const platform::DeviceContext &dev_ctx) const override {}
+  void Run(const Scope &scope, const DeviceContext &dev_ctx) const override {}
 };
 
 class RowWiseAddOpMaker : public OpProtoAndCheckerMaker {
@@ -71,7 +76,7 @@ class NoGradOpMaker : public OpProtoAndCheckerMaker {
   }
 };
 
-class FcOp : public ops::NetOp {
+class FcOp : public operators::NetOp {
  public:
   void Init() override {
     AddOp(OpRegistry::CreateOp("mul", {Input("X"), Input("W")},
@@ -143,6 +148,7 @@ class AddOpMaker : public OpProtoAndCheckerMaker {
 }  // namespace paddle
 
 namespace f = paddle::framework;
+namespace ops = paddle::operators;
 using EnforceNotMet = paddle::platform::EnforceNotMet;
 REGISTER_OP(rowwise_add, f::EmptyOp, f::RowWiseAddOpMaker);
 REGISTER_GRADIENT_OP(rowwise_add, rowwise_add_grad, f::EmptyOp);

paddle/framework/pybind.cc

@@ -18,11 +18,8 @@ limitations under the License. */
 
 #include "paddle/framework/backward.h"
 #include "paddle/framework/op_registry.h"
-#include "paddle/framework/operator.h"
-#include "paddle/framework/scope.h"
 #include "paddle/framework/tensor_py.h"
 #include "paddle/operators/net_op.h"
-#include "paddle/operators/type_alias.h"
 #include "paddle/platform/enforce.h"
 #include "paddle/platform/place.h"
 #include "pybind11/numpy.h"
@@ -47,6 +44,9 @@ USE_OP(uniform_random);
 
 namespace paddle {
 namespace framework {
+
+using Tensor = framework::Tensor;
+
 template <typename ClassType>
 void ExposeOperator(ClassType &m) {
   m.def("infer_shape", &ClassType::type::InferShape)
@@ -152,8 +152,8 @@ All parameter, weight, gradient are variables in Paddle.
            [](Variable &self) -> Tensor * { return self.GetMutable<Tensor>(); },
            py::return_value_policy::reference)
       .def("get_net",
-           [](Variable &self) -> ops::NetOp * {
-             return self.GetMutable<ops::NetOp>();
+           [](Variable &self) -> operators::NetOp * {
+             return self.GetMutable<operators::NetOp>();
            },
            py::return_value_policy::reference);
 
@@ -232,23 +232,24 @@ All parameter, weight, gradient are variables in Paddle.
 
   ExposeOperator(operator_base);
 
-  py::class_<ops::NetOp, std::shared_ptr<ops::NetOp>> net(m, "Net");
+  py::class_<operators::NetOp, std::shared_ptr<operators::NetOp>> net(m, "Net");
 
   net.def_static("create",
-                 []() -> std::shared_ptr<ops::NetOp> {
-                   auto retv = std::make_shared<ops::NetOp>();
+                 []() -> std::shared_ptr<operators::NetOp> {
+                   auto retv = std::make_shared<operators::NetOp>();
                    retv->type_ = "plain_net";
                    return retv;
                  })
-      .def("add_op", &ops::NetOp::AddOp)
-      .def(
-          "add_op",
-          [](ops::NetOp &self, const std::shared_ptr<ops::NetOp> &net) -> void {
-            self.AddOp(std::static_pointer_cast<OperatorBase>(net));
-          })
-      .def("complete_add_op", &ops::NetOp::CompleteAddOp)
-      .def("complete_add_op",
-           [](std::shared_ptr<ops::NetOp> &self) { self->CompleteAddOp(); });
+      .def("add_op", &operators::NetOp::AddOp)
+      .def("add_op",
+           [](operators::NetOp &self,
+              const std::shared_ptr<operators::NetOp> &net) -> void {
+             self.AddOp(std::static_pointer_cast<OperatorBase>(net));
+           })
+      .def("complete_add_op", &operators::NetOp::CompleteAddOp)
+      .def("complete_add_op", [](std::shared_ptr<operators::NetOp> &self) {
+        self->CompleteAddOp();
+      });
 
   ExposeOperator(net);
 

paddle/operators/CMakeLists.txt

@@ -60,6 +60,7 @@ op_library(cross_entropy_op SRCS cross_entropy_op.cc cross_entropy_op.cu)
 op_library(fill_zeros_like_op SRCS fill_zeros_like_op.cc fill_zeros_like_op.cu)
 
 op_library(sgd_op SRCS sgd_op.cc sgd_op.cu)
+cc_test(sgd_op_test SRCS sgd_op_test.cc DEPS sgd_op)
 
 op_library(fc_op
     SRCS fc_op.cc

paddle/operators/add_op.cc

@@ -17,9 +17,9 @@ limitations under the License. */
 namespace paddle {
 namespace operators {
 
-class AddOp : public OperatorWithKernel {
+class AddOp : public framework::OperatorWithKernel {
  protected:
-  void InferShape(const InferShapeContext &ctx) const override {
+  void InferShape(const framework::InferShapeContext &ctx) const override {
     PADDLE_ENFORCE_EQ(ctx.InputSize(), 2);
     PADDLE_ENFORCE_EQ(ctx.OutputSize(), 1);
     PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(0), "Inputs of AddOp must all be set");
@@ -31,9 +31,9 @@ class AddOp : public OperatorWithKernel {
   }
 };
 
-class AddOpMaker : public OpProtoAndCheckerMaker {
+class AddOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
-  AddOpMaker(OpProto *proto, OpAttrChecker *op_checker)
+  AddOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X", "The first input of add op");
     AddInput("Y", "The second input of add op");
@@ -46,14 +46,17 @@ The equation is: Out = X + Y
   }
 };
 
-class AddOpGrad : public OperatorWithKernel {
+class AddOpGrad : public framework::OperatorWithKernel {
  protected:
-  void InferShape(const InferShapeContext &ctx) const override {}
+  void InferShape(const framework::InferShapeContext &ctx) const override {}
 };
 
 }  // namespace operators
 }  // namespace paddle
 
+namespace ops = paddle::operators;
 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>);
+
+REGISTER_OP_CPU_KERNEL(add_two,
+                       ops::AddKernel<paddle::platform::CPUPlace, float>);

paddle/operators/add_op.cu

@@ -16,4 +16,6 @@
 #include "paddle/framework/op_registry.h"
 #include "paddle/operators/add_op.h"
 
-REGISTER_OP_GPU_KERNEL(add_two, ops::AddKernel<ops::GPUPlace, float>);
+namespace ops = paddle::operators;
+REGISTER_OP_GPU_KERNEL(add_two,
+                       ops::AddKernel<paddle::platform::GPUPlace, float>);

paddle/operators/add_op.h

@@ -13,15 +13,21 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
-#include "paddle/operators/type_alias.h"
+#include "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
 
 namespace paddle {
 namespace operators {
 
+using Tensor = framework::Tensor;
+template <typename T, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
+
 template <typename Place, typename T>
-class AddKernel : public OpKernel {
+class AddKernel : public framework::OpKernel {
  public:
-  void Compute(const ExecutionContext& context) const override {
+  void Compute(const framework::ExecutionContext& context) const override {
     auto input0 = context.Input<Tensor>(0);
     auto input1 = context.Input<Tensor>(1);
     auto output = context.Output<Tensor>(0);

paddle/operators/add_op_test.cc

@@ -14,9 +14,9 @@ limitations under the License. */
 
 #include <gtest/gtest.h>
 #define private public
-#include <paddle/framework/op_registry.h>
+#include "paddle/framework/op_registry.h"
+
 USE_OP(add_two);
-// USE_OP(add_two_grad);
 
 TEST(AddOp, GetOpProto) {
   auto& protos = paddle::framework::OpRegistry::protos();

paddle/operators/cross_entropy_op.cc

@@ -17,9 +17,9 @@ limitations under the License. */
 namespace paddle {
 namespace operators {
 
-class OnehotCrossEntropyOp : public OperatorWithKernel {
+class OnehotCrossEntropyOp : public framework::OperatorWithKernel {
  protected:
-  void InferShape(const InferShapeContext &ctx) const override {
+  void InferShape(const framework::InferShapeContext &ctx) const override {
     PADDLE_ENFORCE_EQ(ctx.InputSize(), 2,
                       "Input size of OnehotCrossEntropyOp must be two");
     PADDLE_ENFORCE_EQ(ctx.OutputSize(), 1,
@@ -37,9 +37,9 @@ class OnehotCrossEntropyOp : public OperatorWithKernel {
   }
 };
 
-class OnehotCrossEntropyGradientOp : public OperatorWithKernel {
+class OnehotCrossEntropyGradientOp : public framework::OperatorWithKernel {
  protected:
-  void InferShape(const InferShapeContext &ctx) const override {
+  void InferShape(const framework::InferShapeContext &ctx) const override {
     auto X_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
     auto X = ctx.Input<Tensor>("X");
 
@@ -48,9 +48,10 @@ class OnehotCrossEntropyGradientOp : public OperatorWithKernel {
   }
 };
 
-class OnehotCrossEntropyOpMaker : public OpProtoAndCheckerMaker {
+class OnehotCrossEntropyOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
-  OnehotCrossEntropyOpMaker(OpProto *proto, OpAttrChecker *op_checker)
+  OnehotCrossEntropyOpMaker(framework::OpProto *proto,
+                            framework::OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X", "The first input of OnehotCrossEntropyOp");
     AddInput("label", "The second input of OnehotCrossEntropyOp");
@@ -66,12 +67,14 @@ OnehotCrossEntropy Operator.
 }  // namespace operators
 }  // namespace paddle
 
+namespace ops = paddle::operators;
 REGISTER_OP(onehot_cross_entropy, ops::OnehotCrossEntropyOp,
             ops::OnehotCrossEntropyOpMaker);
-REGISTER_OP_CPU_KERNEL(onehot_cross_entropy,
-                       ops::OnehotCrossEntropyOpKernel<ops::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(
+    onehot_cross_entropy,
+    ops::OnehotCrossEntropyOpKernel<paddle::platform::CPUPlace, float>);
 REGISTER_GRADIENT_OP(onehot_cross_entropy, onehot_cross_entropy_grad,
                      ops::OnehotCrossEntropyGradientOp);
 REGISTER_OP_CPU_KERNEL(
     onehot_cross_entropy_grad,
-    ops::OnehotCrossEntropyGradientOpKernel<ops::CPUPlace, float>);
+    ops::OnehotCrossEntropyGradientOpKernel<paddle::platform::CPUPlace, float>);

paddle/operators/cross_entropy_op.cu

@@ -14,3 +14,8 @@
 
 #define EIGEN_USE_GPU
 #include "paddle/operators/cross_entropy_op.h"
+
+namespace ops = paddle::operators;
+REGISTER_OP_GPU_KERNEL(
+    onehot_cross_entropy,
+    ops::OnehotCrossEntropyOpKernel<paddle::platform::GPUPlace, float>);

paddle/operators/cross_entropy_op.h

@@ -13,11 +13,13 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
-#include "paddle/operators/type_alias.h"
+#include "paddle/framework/op_registry.h"
 
 namespace paddle {
 namespace operators {
 
+using Tensor = framework::Tensor;
+
 template <typename T>
 T tolerable_value(T x) {
   static_assert(std::is_floating_point<T>::value,
@@ -38,9 +40,9 @@ T tolerable_value(T x) {
 }
 
 template <typename Place, typename T>
-class OnehotCrossEntropyOpKernel : public OpKernel {
+class OnehotCrossEntropyOpKernel : public framework::OpKernel {
  public:
-  void Compute(const ExecutionContext& ctx) const override {
+  void Compute(const framework::ExecutionContext& ctx) const override {
     auto X = ctx.Input<Tensor>("X");
     const T* Xdata = X->data<T>();
     const int* label_data = ctx.Input<Tensor>(1)->data<int>();
@@ -61,9 +63,9 @@ class OnehotCrossEntropyOpKernel : public OpKernel {
 };
 
 template <typename Place, typename T>
-class OnehotCrossEntropyGradientOpKernel : public OpKernel {
+class OnehotCrossEntropyGradientOpKernel : public framework::OpKernel {
  public:
-  void Compute(const ExecutionContext& ctx) const override {
+  void Compute(const framework::ExecutionContext& ctx) const override {
     auto X = ctx.Input<Tensor>("X");
     auto dX = ctx.Output<Tensor>(framework::GradVarName("X"));
     auto dY = ctx.Input<Tensor>(framework::GradVarName("Y"));

paddle/operators/fc_op.cc

@@ -12,11 +12,16 @@
    See the License for the specific language governing permissions and
    limitations under the License. */
 
-#include "type_alias.h"
+#include "paddle/operators/net_op.h"
+
+#include "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
 
 namespace paddle {
 namespace operators {
 
+using OpRegistry = framework::OpRegistry;
+
 class FullyConnectedOp : public NetOp {
  public:
   void Init() override {
@@ -39,9 +44,10 @@ class FullyConnectedOp : public NetOp {
   }
 };
 
-class FullyConnectedOpMaker : public OpProtoAndCheckerMaker {
+class FullyConnectedOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
-  FullyConnectedOpMaker(OpProto *proto, OpAttrChecker *op_checker)
+  FullyConnectedOpMaker(framework::OpProto *proto,
+                        framework::OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X", "the input of fc operator");
     AddInput("W", "the weight of fc operator");
@@ -66,4 +72,5 @@ USE_OP(rowwise_add);
 USE_OP(sigmoid);
 USE_OP(softmax);
 
+namespace ops = paddle::operators;
 REGISTER_OP(fc, ops::FullyConnectedOp, ops::FullyConnectedOpMaker);

paddle/operators/fill_zeros_like_op.cc

@@ -50,8 +50,8 @@ The output will have the same size with input.
 }  // namespace operators
 }  // namespace paddle
 
-REGISTER_OP(fill_zeros_like, paddle::operators::FillZerosLikeOp,
-            paddle::operators::FillZerosLikeOpMaker);
+namespace ops = paddle::operators;
+REGISTER_OP(fill_zeros_like, ops::FillZerosLikeOp, ops::FillZerosLikeOpMaker);
 REGISTER_OP_CPU_KERNEL(
     fill_zeros_like,
-    paddle::operators::FillZerosLikeKernel<paddle::platform::CPUPlace, float>);
+    ops::FillZerosLikeKernel<paddle::platform::CPUPlace, float>);

paddle/operators/fill_zeros_like_op.cu

@@ -16,6 +16,7 @@
 #include "paddle/framework/op_registry.h"
 #include "paddle/operators/fill_zeros_like_op.h"
 
+namespace ops = paddle::operators;
 REGISTER_OP_GPU_KERNEL(
     fill_zeros_like,
-    paddle::operators::FillZerosLikeKernel<paddle::platform::GPUPlace, float>);
+    ops::FillZerosLikeKernel<paddle::platform::GPUPlace, float>);

paddle/operators/fill_zeros_like_op.h

@@ -13,7 +13,8 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
-#include "paddle/operators/type_alias.h"
+#include "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
 
 namespace paddle {
 namespace operators {

paddle/operators/mean_op.cc

@@ -17,9 +17,9 @@ limitations under the License. */
 namespace paddle {
 namespace operators {
 
-class MeanOp : public OperatorWithKernel {
+class MeanOp : public framework::OperatorWithKernel {
  protected:
-  void InferShape(const InferShapeContext &ctx) const override {
+  void InferShape(const framework::InferShapeContext &ctx) const override {
     PADDLE_ENFORCE_EQ(ctx.InputSize(), 1, "Input size of AddOp must be one");
     PADDLE_ENFORCE_EQ(ctx.OutputSize(), 1, "Output size of AddOp must be one");
     PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(0), "input should be set");
@@ -28,9 +28,9 @@ class MeanOp : public OperatorWithKernel {
   }
 };
 
-class MeanOpMaker : public OpProtoAndCheckerMaker {
+class MeanOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
-  MeanOpMaker(OpProto *proto, OpAttrChecker *op_checker)
+  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").IgnoreGradient();
@@ -38,9 +38,9 @@ class MeanOpMaker : public OpProtoAndCheckerMaker {
   }
 };
 
-class MeanGradOp : public OperatorWithKernel {
+class MeanGradOp : public framework::OperatorWithKernel {
  protected:
-  void InferShape(const InferShapeContext &ctx) const override {
+  void InferShape(const framework::InferShapeContext &ctx) const override {
     ctx.Output<Tensor>("X" + framework::kGradVarSuffix)
         ->Resize(ctx.Input<Tensor>("X")->dims());
   }
@@ -49,7 +49,10 @@ class MeanGradOp : public OperatorWithKernel {
 }  // namespace operators
 }  // namespace paddle
 
+namespace ops = paddle::operators;
 REGISTER_OP(mean, ops::MeanOp, ops::MeanOpMaker);
-REGISTER_OP_CPU_KERNEL(mean, ops::MeanKernel<ops::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(mean,
+                       ops::MeanKernel<paddle::platform::CPUPlace, float>);
 REGISTER_GRADIENT_OP(mean, mean_grad, ops::MeanGradOp);
-REGISTER_OP_CPU_KERNEL(mean_grad, ops::MeanGradKernel<ops::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(mean_grad,
+                       ops::MeanGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/mean_op.cu

@@ -16,5 +16,8 @@
 
 #include "paddle/operators/mean_op.h"
 
-REGISTER_OP_GPU_KERNEL(mean, ops::MeanKernel<ops::GPUPlace, float>);
-REGISTER_OP_GPU_KERNEL(mean_grad, ops::MeanGradKernel<ops::GPUPlace, float>);
+namespace ops = paddle::operators;
+REGISTER_OP_GPU_KERNEL(mean,
+                       ops::MeanKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(mean_grad,
+                       ops::MeanGradKernel<paddle::platform::GPUPlace, float>);

paddle/operators/mean_op.h

@@ -13,15 +13,24 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
-#include "paddle/operators/type_alias.h"
+#include "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
 
 namespace paddle {
 namespace operators {
 
+using Tensor = framework::Tensor;
+template <typename T, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenScalar = framework::EigenScalar<T, MajorType, IndexType>;
+template <typename T, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
+
 template <typename Place, typename T>
-class MeanKernel : public OpKernel {
+class MeanKernel : public framework::OpKernel {
  public:
-  void Compute(const ExecutionContext& context) const override {
+  void Compute(const framework::ExecutionContext& context) const override {
     auto input = context.Input<Tensor>(0);
     auto output = context.Output<Tensor>(0);
 
@@ -36,9 +45,9 @@ class MeanKernel : public OpKernel {
 };
 
 template <typename Place, typename T>
-class MeanGradKernel : public OpKernel {
+class MeanGradKernel : public framework::OpKernel {
  public:
-  void Compute(const ExecutionContext& context) const override {
+  void Compute(const framework::ExecutionContext& context) const override {
     auto OG = context.Input<Tensor>("Out" + framework::kGradVarSuffix);
     PADDLE_ENFORCE(framework::product(OG->dims()) == 1,
                    "Mean Gradient should be scalar");

paddle/operators/mul_op.cc

@@ -17,9 +17,9 @@
 namespace paddle {
 namespace operators {
 
-class MulOp : public OperatorWithKernel {
+class MulOp : public framework::OperatorWithKernel {
  protected:
-  void InferShape(const InferShapeContext &ctx) const override {
+  void InferShape(const framework::InferShapeContext &ctx) const override {
     PADDLE_ENFORCE(ctx.InputSize() == 2, "The mul op must take two inputs");
     auto dim0 = ctx.Input<Tensor>(0)->dims();
     auto dim1 = ctx.Input<Tensor>(1)->dims();
@@ -37,9 +37,9 @@ class MulOp : public OperatorWithKernel {
   }
 };
 
-class MulOpMaker : public OpProtoAndCheckerMaker {
+class MulOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
-  MulOpMaker(OpProto *proto, OpAttrChecker *op_checker)
+  MulOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X", "The first input of mul op");
     AddInput("Y", "The second input of mul op");
@@ -52,9 +52,9 @@ The equation is: Out = X * Y
   }
 };
 
-class MulOpGrad : public OperatorWithKernel {
+class MulOpGrad : public framework::OperatorWithKernel {
  protected:
-  void InferShape(const InferShapeContext &ctx) const override {}
+  void InferShape(const framework::InferShapeContext &ctx) const override {}
   std::string DebugString() const override {
     LOG(INFO) << "MulGrad";
     return "";
@@ -64,7 +64,8 @@ class MulOpGrad : public OperatorWithKernel {
 }  // namespace operators
 }  // namespace paddle
 
+namespace ops = paddle::operators;
 REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker);
 REGISTER_GRADIENT_OP(mul, mul_grad, ops::MulOpGrad);
 
-REGISTER_OP_CPU_KERNEL(mul, ops::MulKernel<ops::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(mul, ops::MulKernel<paddle::platform::CPUPlace, float>);

paddle/operators/mul_op.cu

@@ -15,4 +15,6 @@
 #define EIGEN_USE_GPU
 #include "paddle/operators/mul_op.h"
 
-REGISTER_OP_GPU_KERNEL(mul, ops::MulKernel<ops::GPUPlace, float>);
+namespace ops = paddle::operators;
+
+REGISTER_OP_GPU_KERNEL(mul, ops::MulKernel<paddle::platform::GPUPlace, float>);

paddle/operators/mul_op.h

@@ -13,16 +13,21 @@
    limitations under the License. */
 
 #pragma once
-
-#include "paddle/operators/type_alias.h"
+#include "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
 
 namespace paddle {
 namespace operators {
 
+using Tensor = framework::Tensor;
+template <typename T, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
+
 template <typename Place, typename T>
-class MulKernel : public OpKernel {
+class MulKernel : public framework::OpKernel {
  public:
-  void Compute(const ExecutionContext& context) const override {
+  void Compute(const framework::ExecutionContext& context) const override {
     Eigen::array<Eigen::IndexPair<Eigen::DenseIndex>, 1> dim_pair = {
         {Eigen::IndexPair<Eigen::DenseIndex>(1, 0)}};
 
@@ -40,5 +45,6 @@ class MulKernel : public OpKernel {
     Z.device(place) = X.contract(Y, dim_pair);
   }
 };
+
 }  // namespace operators
 }  // namespace paddle

paddle/operators/net_op.cc

@@ -15,7 +15,6 @@
 */
 
 #include "paddle/operators/net_op.h"
-#include "paddle/framework/op_registry.h"
 
 namespace paddle {
 namespace operators {

paddle/operators/net_op.h

@@ -14,13 +14,7 @@ limitations under the License. */
 
 #pragma once
 
-#include "paddle/framework/op_desc.pb.h"
-#include "paddle/framework/op_proto.pb.h"
 #include "paddle/framework/op_registry.h"
-#include "paddle/framework/operator.h"
-#include "paddle/framework/scope.h"
-#include "paddle/operators/type_alias.h"
-#include "paddle/platform/device_context.h"
 
 namespace paddle {
 namespace operators {

paddle/operators/net_op_test.cc

@@ -2,31 +2,27 @@
 
 #include <gtest/gtest.h>
 
-#include "paddle/framework/op_registry.h"
-#include "paddle/framework/operator.h"
-
 namespace paddle {
 namespace operators {
+using Scope = framework::Scope;
+using DeviceContext = platform::DeviceContext;
 
 static int infer_shape_cnt = 0;
 static int run_cnt = 0;
 
-class TestOp : public OperatorBase {
+class TestOp : public framework::OperatorBase {
  public:
-  void InferShape(const framework::Scope& scope) const override {
-    ++infer_shape_cnt;
-  }
-  void Run(const framework::Scope& scope,
-           const paddle::platform::DeviceContext& dev_ctx) const override {
+  void InferShape(const Scope& scope) const override { ++infer_shape_cnt; }
+  void Run(const Scope& scope,
+           const platform::DeviceContext& dev_ctx) const override {
     ++run_cnt;
   }
 };
 
-class EmptyOp : public OperatorBase {
+class EmptyOp : public framework::OperatorBase {
  public:
   void InferShape(const Scope& scope) const override {}
-  void Run(const Scope& scope,
-           const platform::DeviceContext& dev_ctx) const override {}
+  void Run(const Scope& scope, const DeviceContext& dev_ctx) const override {}
 };
 
 template <typename T>
@@ -72,7 +68,7 @@ TEST(OpKernel, all) {
   net->Run(scope, dev_ctx);
   ASSERT_EQ(2, infer_shape_cnt);
   ASSERT_EQ(2, run_cnt);
-  ASSERT_THROW(net->AddOp(op2), paddle::platform::EnforceNotMet);
+  ASSERT_THROW(net->AddOp(op2), platform::EnforceNotMet);
 }
 
 TEST(NetOp, insert_op) {

paddle/operators/recurrent_op.cc

@@ -14,17 +14,19 @@
 
 #include "paddle/operators/recurrent_op.h"
 
-#include <glog/logging.h>
 #include <cstring>
 #include <sstream>
 
 #include "paddle/framework/op_registry.h"
 #include "paddle/operators/net_op.h"
-#include "paddle/platform/enforce.h"
 
 namespace paddle {
 namespace operators {
 
+using Scope = framework::Scope;
+using Variable = framework::Variable;
+using Tensor = framework::Tensor;
+
 void RecurrentAlgorithm::InferShape(const Scope& scope) const {
   seq_len_ = scope.FindVar((arg_->inlinks[0]).external)
                  ->GetMutable<Tensor>()
@@ -135,10 +137,11 @@ void RecurrentOp::Init() {
   alg_.Init(std::move(arg));
 }
 
-class RecurrentAlgorithmProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
+class RecurrentAlgorithmProtoAndCheckerMaker
+    : public framework::OpProtoAndCheckerMaker {
  public:
-  RecurrentAlgorithmProtoAndCheckerMaker(OpProto* proto,
-                                         OpAttrChecker* op_checker)
+  RecurrentAlgorithmProtoAndCheckerMaker(framework::OpProto* proto,
+                                         framework::OpAttrChecker* op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     const auto& name = RecurrentOp::kArgName;
     // inputs and outputs stored in proto

paddle/operators/recurrent_op_test.cc

@@ -27,6 +27,10 @@ namespace operators {
 
 using framework::make_ddim;
 using framework::DDim;
+using framework::Tensor;
+using framework::Variable;
+using framework::Scope;
+using framework::OpRegistry;
 
 class RecurrentOpTest : public ::testing::Test {
  protected:
@@ -164,7 +168,7 @@ class RecurrentOpTest : public ::testing::Test {
 
   // father scope
   Scope scope_;
-  std::shared_ptr<OperatorBase> rnn_op_;
+  std::shared_ptr<framework::OperatorBase> rnn_op_;
 };
 
 TEST_F(RecurrentOpTest, Run) {

paddle/operators/rnn/recurrent_op_utils.cc

@@ -18,7 +18,9 @@ namespace paddle {
 namespace operators {
 namespace rnn {
 
-namespace fmw = paddle::framework;
+namespace f = paddle::framework;
+
+using Tensor = framework::Tensor;
 
 void SegmentInputs(const std::vector<Scope*>& step_scopes,
                    const std::vector<Link>& inlinks, const size_t seq_len,
@@ -30,10 +32,10 @@ void SegmentInputs(const std::vector<Scope*>& step_scopes,
                    inlinks[i].external);
 
     Tensor* input = input_var->GetMutable<Tensor>();
-    fmw::DDim dims = input->dims();
+    f::DDim dims = input->dims();
     PADDLE_ENFORCE(static_cast<size_t>(dims[0]) == seq_len,
                    "all the inlinks must have same length");
-    fmw::DDim step_dims = slice_ddim(dims, 1, dims.size());
+    f::DDim step_dims = slice_ddim(dims, 1, dims.size());
     for (size_t j = 0; j < seq_len; j++) {
       Tensor* step_input =
           step_scopes[j]->NewVar(inlinks[i].internal)->GetMutable<Tensor>();
@@ -58,11 +60,10 @@ void ConcatOutputs(const std::vector<Scope*>& step_scopes,
       auto step_scope_var = step_scopes[0]->FindVar(outlinks[i].internal);
       PADDLE_ENFORCE(step_scope_var != nullptr, "%s not in scope",
                      outlinks[i].internal);
-      fmw::DDim step_dims =
-          step_scope_var->template GetMutable<Tensor>()->dims();
+      f::DDim step_dims = step_scope_var->template GetMutable<Tensor>()->dims();
       std::vector<int> dims_vec = vectorize(step_dims);
       dims_vec.insert(dims_vec.begin(), seq_len);
-      output->Resize(fmw::make_ddim(dims_vec));
+      output->Resize(f::make_ddim(dims_vec));
     } else {
       output->mutable_data<float>(platform::CPUPlace());
       for (size_t j = 0; j < seq_len; j++) {
@@ -104,7 +105,7 @@ void LinkMemories(const std::vector<Scope*>& scopes,
 }
 
 void InitArgument(const ArgumentName& name, Argument* arg,
-                  const OperatorBase& op) {
+                  const framework::OperatorBase& op) {
   arg->step_net = op.Input(name.step_net);
   arg->step_scopes = op.Output(name.step_scopes);
 

paddle/operators/rnn/recurrent_op_utils.h

@@ -17,12 +17,13 @@
 #include <string>
 
 #include "paddle/framework/operator.h"
-#include "paddle/operators/type_alias.h"
 
 namespace paddle {
 namespace operators {
 namespace rnn {
 
+using Scope = framework::Scope;
+
 /**
  * Memory of a RNN (same as the role of `Momory` in PaddlePaddle).
  *
@@ -86,7 +87,7 @@ void LinkMemories(const std::vector<Scope*>& step_scopes,
                   const int offset, bool infer_shape_mode);
 
 void InitArgument(const ArgumentName& name, Argument* arg,
-                  const OperatorBase& op);
+                  const framework::OperatorBase& op);
 
 }  // namespace rnn
 }  // namespace operators

paddle/operators/rowwise_add_op.cc

@@ -13,12 +13,13 @@
    limitations under the License. */
 
 #include "paddle/operators/rowwise_add_op.h"
+
 namespace paddle {
 namespace operators {
 
-class RowWiseAddOp : public OperatorWithKernel {
+class RowWiseAddOp : public framework::OperatorWithKernel {
  protected:
-  void InferShape(const InferShapeContext &ctx) const override {
+  void InferShape(const framework::InferShapeContext &ctx) const override {
     PADDLE_ENFORCE(ctx.InputSize() == 2UL,
                    "Two inputs is needed by rowwise add");
     auto dim0 = ctx.Input<Tensor>(0)->dims();
@@ -32,9 +33,10 @@ class RowWiseAddOp : public OperatorWithKernel {
   }
 };
 
-class RowWiseAddOpMaker : public OpProtoAndCheckerMaker {
+class RowWiseAddOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
-  RowWiseAddOpMaker(OpProto *proto, OpAttrChecker *op_checker)
+  RowWiseAddOpMaker(framework::OpProto *proto,
+                    framework::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");
@@ -50,6 +52,7 @@ for i in xrange(X.shape[0]):
 }  // namespace operators
 }  // namespace paddle
 
+namespace ops = paddle::operators;
 REGISTER_OP(rowwise_add, ops::RowWiseAddOp, ops::RowWiseAddOpMaker);
-REGISTER_OP_CPU_KERNEL(rowwise_add,
-                       ops::RowWiseAddKernel<ops::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(
+    rowwise_add, ops::RowWiseAddKernel<paddle::platform::CPUPlace, float>);

paddle/operators/rowwise_add_op.cu

@@ -15,5 +15,6 @@
 #define EIGEN_USE_GPU
 #include "paddle/operators/rowwise_add_op.h"
 
-REGISTER_OP_GPU_KERNEL(rowwise_add,
-                       ops::RowWiseAddKernel<ops::GPUPlace, float>);
+namespace ops = paddle::operators;
+REGISTER_OP_GPU_KERNEL(
+    rowwise_add, ops::RowWiseAddKernel<paddle::platform::GPUPlace, float>);

paddle/operators/rowwise_add_op.h

@@ -13,15 +13,24 @@
    limitations under the License. */
 
 #pragma once
-#include "paddle/operators/type_alias.h"
+#include "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
 
 namespace paddle {
 namespace operators {
 
+using Tensor = framework::Tensor;
+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 Place, typename T>
-class RowWiseAddKernel : public OpKernel {
+class RowWiseAddKernel : public framework::OpKernel {
  public:
-  void Compute(const ExecutionContext& context) const override {
+  void Compute(const framework::ExecutionContext& context) const override {
     auto out = context.Output<Tensor>(0);
     out->mutable_data<T>(context.GetPlace());
 

paddle/operators/sgd_op.cc

@@ -17,9 +17,9 @@ limitations under the License. */
 namespace paddle {
 namespace operators {
 
-class SGDOp : public OperatorWithKernel {
+class SGDOp : public framework::OperatorWithKernel {
  protected:
-  void InferShape(const InferShapeContext &ctx) const override {
+  void InferShape(const framework::InferShapeContext &ctx) const override {
     PADDLE_ENFORCE_EQ(ctx.InputSize(), 2, "Input size of SGDOp must be two");
     PADDLE_ENFORCE_EQ(ctx.OutputSize(), 1, "Output size of SGDOp must be one");
     PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(0), "inputs[0] mast be set");
@@ -31,9 +31,9 @@ class SGDOp : public OperatorWithKernel {
   }
 };
 
-class SGDOpMaker : public OpProtoAndCheckerMaker {
+class SGDOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
-  SGDOpMaker(OpProto *proto, OpAttrChecker *op_checker)
+  SGDOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("param", "input parameter");
     AddInput("grad", "input gradient");
@@ -51,5 +51,7 @@ param_out = param - learning_rate * grad;
 }  // namespace operators
 }  // namespace paddle
 
+namespace ops = paddle::operators;
 REGISTER_OP(sgd, ops::SGDOp, ops::SGDOpMaker);
-REGISTER_OP_CPU_KERNEL(sgd, ops::SGDOpKernel<ops::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(sgd,
+                       ops::SGDOpKernel<paddle::platform::CPUPlace, float>);

paddle/operators/sgd_op.cu

@@ -15,4 +15,6 @@
 #define EIGEN_USE_GPU
 #include "paddle/operators/sgd_op.h"
 
-REGISTER_OP_GPU_KERNEL(sgd, ops::SGDOpKernel<ops::GPUPlace, float>);
+namespace ops = paddle::operators;
+REGISTER_OP_GPU_KERNEL(sgd,
+                       ops::SGDOpKernel<paddle::platform::GPUPlace, float>);

paddle/operators/sgd_op.h

@@ -13,15 +13,21 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
-#include "paddle/operators/type_alias.h"
+#include "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
 
 namespace paddle {
 namespace operators {
 
+using Tensor = framework::Tensor;
+template <typename T, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
+
 template <typename Place, typename T>
-class SGDOpKernel : public OpKernel {
+class SGDOpKernel : public framework::OpKernel {
  public:
-  void Compute(const ExecutionContext& ctx) const override {
+  void Compute(const framework::ExecutionContext& ctx) const override {
     auto param = ctx.Input<Tensor>("param");
     auto grad = ctx.Input<Tensor>("grad");
     auto param_out = ctx.Output<Tensor>(0);

paddle/operators/sigmoid_op.cc

@@ -13,21 +13,23 @@
    limitations under the License. */
 
 #include "paddle/operators/sigmoid_op.h"
+
 namespace paddle {
 namespace operators {
 
-class SigmoidOp : public OperatorWithKernel {
+class SigmoidOp : public framework::OperatorWithKernel {
  protected:
-  void InferShape(const InferShapeContext &ctx) const override {
+  void InferShape(const framework::InferShapeContext &ctx) const override {
     PADDLE_ENFORCE(ctx.InputSize() == 1, "Sigmoid Op only have one input");
     PADDLE_ENFORCE(ctx.OutputSize() == 1, "Sigmoid Op only have one output");
     ctx.Output<Tensor>(0)->Resize(ctx.Input<Tensor>(0)->dims());
   }
 };
 
-class SigmoidOpMaker : public OpProtoAndCheckerMaker {
+class SigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
-  SigmoidOpMaker(OpProto *proto, OpAttrChecker *op_checker)
+  SigmoidOpMaker(framework::OpProto *proto,
+                 framework::OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X", "sigmoid input");
     AddOutput("Y", "sigmoid output");
@@ -35,9 +37,9 @@ class SigmoidOpMaker : public OpProtoAndCheckerMaker {
   }
 };
 
-class SigmoidOpGrad : public OperatorWithKernel {
+class SigmoidOpGrad : public framework::OperatorWithKernel {
  protected:
-  void InferShape(const InferShapeContext &ctx) const override {
+  void InferShape(const framework::InferShapeContext &ctx) const override {
     ctx.Output<Tensor>(0)->Resize(ctx.Input<Tensor>(0)->dims());
   }
 };
@@ -45,9 +47,11 @@ class SigmoidOpGrad : public OperatorWithKernel {
 }  // namespace operators
 }  // namespace paddle
 
+namespace ops = paddle::operators;
 REGISTER_OP(sigmoid, ops::SigmoidOp, ops::SigmoidOpMaker);
 REGISTER_GRADIENT_OP(sigmoid, sigmoid_grad, ops::SigmoidOpGrad);
 
-REGISTER_OP_CPU_KERNEL(sigmoid, ops::SigmoidKernel<ops::CPUPlace, float>);
-REGISTER_OP_CPU_KERNEL(sigmoid_grad,
-                       ops::SigmoidGradKernel<ops::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(sigmoid,
+                       ops::SigmoidKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(
+    sigmoid_grad, ops::SigmoidGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/sigmoid_op.cu

@@ -15,6 +15,9 @@
 #define EIGEN_USE_GPU
 #include "paddle/operators/sigmoid_op.h"
 
-REGISTER_OP_GPU_KERNEL(sigmoid, ops::SigmoidKernel<ops::GPUPlace, float>);
-REGISTER_OP_GPU_KERNEL(sigmoid_grad,
-                       ops::SigmoidGradKernel<ops::GPUPlace, float>);
+namespace ops = paddle::operators;
+
+REGISTER_OP_GPU_KERNEL(sigmoid,
+                       ops::SigmoidKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(
+    sigmoid_grad, ops::SigmoidGradKernel<paddle::platform::GPUPlace, float>);

paddle/operators/sigmoid_op.h

@@ -13,16 +13,21 @@
    limitations under the License. */
 
 #pragma once
-
-#include "paddle/operators/type_alias.h"
+#include "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
 
 namespace paddle {
 namespace operators {
 
+using Tensor = framework::Tensor;
+template <typename T, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
+
 template <typename Place, typename T>
-class SigmoidKernel : public OpKernel {
+class SigmoidKernel : public framework::OpKernel {
  public:
-  void Compute(const ExecutionContext& context) const override {
+  void Compute(const framework::ExecutionContext& context) const override {
     auto input = context.Input<Tensor>(0);
     auto output = context.Output<Tensor>(0);
     output->mutable_data<T>(context.GetPlace());
@@ -37,9 +42,9 @@ class SigmoidKernel : public OpKernel {
 };
 
 template <typename Place, typename T>
-class SigmoidGradKernel : public OpKernel {
+class SigmoidGradKernel : public framework::OpKernel {
  public:
-  void Compute(const ExecutionContext& context) const override {
+  void Compute(const framework::ExecutionContext& context) const override {
     auto Y_t = context.Input<Tensor>("Y");
     auto dY_t = context.Input<Tensor>(framework::GradVarName("Y"));
     auto dX_t = context.Output<Tensor>(framework::GradVarName("X"));

paddle/operators/softmax_op.cc

@@ -17,9 +17,9 @@ limitations under the License. */
 namespace paddle {
 namespace operators {
 
-class SoftmaxOp : public OperatorWithKernel {
+class SoftmaxOp : public framework::OperatorWithKernel {
  protected:
-  void InferShape(const InferShapeContext &ctx) const override {
+  void InferShape(const framework::InferShapeContext &ctx) const override {
     PADDLE_ENFORCE_EQ(ctx.InputSize(), 1UL,
                       "Only one input is need for softmax");
     PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("X")->dims().size(), 2UL,
@@ -30,9 +30,10 @@ class SoftmaxOp : public OperatorWithKernel {
   }
 };
 
-class SoftmaxOpMaker : public OpProtoAndCheckerMaker {
+class SoftmaxOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
-  SoftmaxOpMaker(OpProto *proto, OpAttrChecker *op_checker)
+  SoftmaxOpMaker(framework::OpProto *proto,
+                 framework::OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X", "input of softmax");
     AddOutput("Y", "output of softmax");
@@ -40,9 +41,9 @@ class SoftmaxOpMaker : public OpProtoAndCheckerMaker {
   }
 };
 
-class SoftmaxOpGrad : public OperatorWithKernel {
+class SoftmaxOpGrad : public framework::OperatorWithKernel {
  protected:
-  void InferShape(const InferShapeContext &ctx) const override {
+  void InferShape(const framework::InferShapeContext &ctx) const override {
     PADDLE_ENFORCE_EQ(ctx.InputSize(), 3UL,
                       "Input of SoftmaxOpGrad should be 3, X, Y, YG");
     PADDLE_ENFORCE_EQ(ctx.OutputSize(), 1UL,
@@ -61,8 +62,11 @@ class SoftmaxOpGrad : public OperatorWithKernel {
 }  // namespace operators
 }  // namespace paddle
 
+namespace ops = paddle::operators;
+
 REGISTER_OP(softmax, ops::SoftmaxOp, ops::SoftmaxOpMaker);
-REGISTER_OP_CPU_KERNEL(softmax, ops::SoftmaxKernel<ops::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(softmax,
+                       ops::SoftmaxKernel<paddle::platform::CPUPlace, float>);
 REGISTER_GRADIENT_OP(softmax, softmax_grad, ops::SoftmaxOpGrad);
-REGISTER_OP_CPU_KERNEL(softmax_grad,
-                       ops::SoftmaxGradKernel<ops::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(
+    softmax_grad, ops::SoftmaxGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/softmax_op.cu

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+/* 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.
@@ -13,9 +13,11 @@
    limitations under the License. */
 
 #define EIGEN_USE_GPU
-#include "paddle/framework/op_registry.h"
 #include "paddle/operators/softmax_op.h"
 
-REGISTER_OP_GPU_KERNEL(softmax, ops::SoftmaxKernel<ops::GPUPlace, float>);
-REGISTER_OP_GPU_KERNEL(softmax_grad,
-                       ops::SoftmaxGradKernel<ops::GPUPlace, float>);
+namespace ops = paddle::operators;
+
+REGISTER_OP_GPU_KERNEL(softmax,
+                       ops::SoftmaxKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(
+    softmax_grad, ops::SoftmaxGradKernel<paddle::platform::GPUPlace, float>);

paddle/operators/softmax_op.h

@@ -13,19 +13,21 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
-
-#include "paddle/framework/ddim.h"
-#include "paddle/framework/operator.h"
-#include "paddle/framework/tensor.h"
-#include "paddle/operators/type_alias.h"
+#include "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
 
 namespace paddle {
 namespace operators {
 
+using Tensor = framework::Tensor;
+template <typename T, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
+
 template <typename Place, typename T>
-class SoftmaxKernel : public OpKernel {
+class SoftmaxKernel : public framework::OpKernel {
  public:
-  void Compute(const ExecutionContext& context) const override {
+  void Compute(const framework::ExecutionContext& context) const override {
     auto input = context.Input<Tensor>("X");
     auto output = context.Output<Tensor>("Y");
     output->mutable_data<T>(context.GetPlace());
@@ -62,9 +64,9 @@ class SoftmaxKernel : public OpKernel {
 };
 
 template <typename Place, typename T>
-class SoftmaxGradKernel : public OpKernel {
+class SoftmaxGradKernel : public framework::OpKernel {
  public:
-  void Compute(const ExecutionContext& context) const override {
+  void Compute(const framework::ExecutionContext& context) const override {
     std::shared_ptr<Tensor> scale_ = std::make_shared<Tensor>();
 
     auto Y = context.Input<Tensor>("Y");

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/op_registry.h"
-#include "paddle/operators/net_op.h"
-
-namespace paddle {
-namespace operators {
-
-using OpKernel = framework::OpKernel;
-using OperatorBase = framework::OperatorBase;
-using InferShapeContext = framework::InferShapeContext;
-using ExecutionContext = framework::ExecutionContext;
-using Variable = framework::Variable;
-template <typename T, int MajorType = Eigen::RowMajor,
-          typename IndexType = Eigen::DenseIndex>
-using EigenScalar = framework::EigenScalar<T, MajorType, IndexType>;
-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 Scope = framework::Scope;
-using OperatorWithKernel = framework::OperatorWithKernel;
-using OperatorBase = framework::OperatorBase;
-using OpProtoAndCheckerMaker = framework::OpProtoAndCheckerMaker;
-using OpProto = framework::OpProto;
-using OpAttrChecker = framework::OpAttrChecker;
-using CPUPlace = platform::CPUPlace;
-using GPUPlace = platform::GPUPlace;
-using OpRegistry = framework::OpRegistry;
-
-}  // namespace operators
-}  // namespace paddle
-
-namespace ops = paddle::operators;

paddle/pybind/CMakeLists.txt

-cc_library(paddle_pybind SHARED
-    SRCS pybind.cc
-    DEPS pybind python backward
-	fc_op
-	sgd_op
-	add_op
-	mean_op
-  guassian_random_op
-	cross_entropy_op
-	recurrent_op
-	fill_zeros_like_op)

paddle/pybind/pybind.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,
-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. */
-
-#include <Python.h>
-#include <fstream>
-#include <vector>
-
-#include "paddle/framework/net.h"
-#include "paddle/framework/op_registry.h"
-#include "paddle/framework/operator.h"
-#include "paddle/framework/scope.h"
-#include "paddle/pybind/tensor_bind.h"
-#include "pybind11/numpy.h"
-#include "pybind11/pybind11.h"
-#include "pybind11/stl.h"
-
-namespace py = pybind11;
-namespace pd = paddle::framework;
-
-USE_OP(add_two);
-USE_OP(onehot_cross_entropy);
-USE_OP_WITHOUT_KERNEL(fc);
-USE_OP(sgd);
-USE_OP(mul);
-USE_OP(sigmoid);
-USE_OP(softmax);
-USE_OP(rowwise_add);
-USE_OP(gaussian_random);
-USE_OP_WITHOUT_KERNEL(recurrent_op);
-
-template <typename ClassType>
-void ExposeOperator(ClassType& m) {
-  m.def("infer_shape", &ClassType::type::InferShape)
-      .def("run", &ClassType::type::Run)
-      .def("outputs",
-           [](const typename ClassType::type& op) -> std::vector<std::string> {
-             return op.outputs_;
-           })
-      .def("__str__", &ClassType::type::DebugString);
-}
-
-static size_t UniqueIntegerGenerator() {
-  static std::atomic<size_t> generator;
-  return generator.fetch_add(1);
-}
-
-PYBIND11_PLUGIN(core) {
-  py::module m("core", "C++ core of PaddlePaddle");
-
-  py::class_<pd::Tensor>(m, "Tensor", py::buffer_protocol())
-      .def_buffer([](pd::Tensor& self) -> py::buffer_info {
-        return paddle::pybind::CastToPyBuffer(self);
-      })
-      .def("get_dims",
-           [](const pd::Tensor& self) { return pd::vectorize(self.dims()); })
-      .def("set_dims",
-           [](pd::Tensor& self, const std::vector<int>& dim) {
-             self.Resize(pd::make_ddim(dim));
-           })
-      .def("alloc_float",
-           [](pd::Tensor& self) {
-             self.mutable_data<float>(paddle::platform::CPUPlace());
-           })
-      .def("alloc_int",
-           [](pd::Tensor& self) {
-             self.mutable_data<int>(paddle::platform::CPUPlace());
-           })
-      .def("set", paddle::pybind::PyTensorSetFromArray<float>)
-      .def("set", paddle::pybind::PyTensorSetFromArray<int>)
-      .def("shape",
-           [](pd::Tensor& self) { return pd::vectorize(self.dims()); });
-
-  py::class_<pd::Variable>(m, "Variable", R"DOC(Variable Class.
-
-All parameter, weight, gradient are variables in Paddle.
-)DOC")
-      .def("is_int", [](const pd::Variable& var) { return var.IsType<int>(); })
-      .def("set_int",
-           [](pd::Variable& var, int val) -> void {
-             *var.GetMutable<int>() = val;
-           })
-      .def("get_int",
-           [](const pd::Variable& var) -> int { return var.Get<int>(); })
-      .def("get_tensor",
-           [](pd::Variable& self) -> pd::Tensor* {
-             return self.GetMutable<pd::Tensor>();
-           },
-           py::return_value_policy::reference)
-      .def("get_net",
-           [](pd::Variable& self) -> pd::NetOp* {
-             return self.GetMutable<pd::NetOp>();
-           },
-           py::return_value_policy::reference);
-
-  py::class_<pd::Scope, std::shared_ptr<pd::Scope>>(m, "Scope")
-      .def(py::init<const std::shared_ptr<pd::Scope>&>())
-      .def("get_var",
-           &pd::Scope::GetVariable,
-           py::return_value_policy::reference)
-      .def("create_var",
-           &pd::Scope::CreateVariable,
-           py::return_value_policy::reference)
-      .def("get_var_name", &pd::Scope::GetVariableName);
-
-  //! @note: Be careful! PyBind will return std::string as an unicode, not
-  //! Python str. If you want a str object, you should cast them in Python.
-  m.def("get_all_op_protos", []() -> std::vector<py::bytes> {
-    auto& protos = pd::OpRegistry::protos();
-    std::vector<py::bytes> ret_values;
-    for (auto it = protos.begin(); it != protos.end(); ++it) {
-      PADDLE_ENFORCE(it->second.IsInitialized(),
-                     "OpProto must all be initialized");
-      std::string str;
-      PADDLE_ENFORCE(it->second.SerializeToString(&str),
-                     "Serialize OpProto Error. This could be a bug of Paddle.");
-      ret_values.push_back(py::bytes(str));
-    }
-    return ret_values;
-  });
-  m.def_submodule(
-       "var_names",
-       "The module will return special predefined variable name in Paddle")
-      .def("empty", pd::OperatorBase::EMPTY_VAR_NAME)
-      .def("temp", pd::OperatorBase::TMP_VAR_NAME);
-
-  py::class_<paddle::platform::DeviceContext>(m, "DeviceContext")
-      .def_static("cpu_context", []() -> paddle::platform::DeviceContext* {
-        return new paddle::platform::CPUDeviceContext();
-      });
-
-  py::class_<pd::OperatorBase, std::shared_ptr<pd::OperatorBase>> operator_base(
-      m, "Operator");
-
-  operator_base.def_static("create", [](py::bytes protobin) {
-    pd::OpDesc desc;
-    PADDLE_ENFORCE(desc.ParsePartialFromString(protobin),
-                   "Cannot parse user input to OpDesc");
-    PADDLE_ENFORCE(desc.IsInitialized(),
-                   "User OpDesc is not initialized, reason %s",
-                   desc.InitializationErrorString());
-    return pd::OpRegistry::CreateOp(desc);
-  });
-  ExposeOperator(operator_base);
-
-  py::class_<pd::NetOp, std::shared_ptr<pd::NetOp>> net(m, "Net");
-
-  net.def_static("create",
-                 []() -> std::shared_ptr<pd::NetOp> {
-                   auto retv = std::make_shared<pd::NetOp>();
-                   retv->type_ = "plain_net";
-                   return retv;
-                 })
-      .def("add_op", &pd::NetOp::AddOp)
-      .def("add_op",
-           [](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::NetOp::CompleteAddOp)
-      .def("complete_add_op",
-           [](std::shared_ptr<pd::NetOp>& self) { self->CompleteAddOp(); });
-  ExposeOperator(net);
-
-  m.def("unique_integer", UniqueIntegerGenerator);
-
-  return m.ptr();
-}