Elementwise operator. (#4139)

Elementwise operator add/sub/mul/div

paddle/operators/elementwise_add_op.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 "paddle/operators/elementwise_add_op.h"
+
+namespace paddle {
+namespace operators {
+class ElementwiseAddOpMaker : public ElementwiseOpMaker {
+ public:
+  ElementwiseAddOpMaker(framework::OpProto* proto,
+                        framework::OpAttrChecker* op_checker)
+      : ElementwiseOpMaker(proto, op_checker) {
+    SetComment("add", "Out = X + Y");
+    AddComment(comment_);
+  }
+};
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP(elementwise_add, ops::ElementwiseOp, ops::ElementwiseAddOpMaker,
+            elementwise_add_grad, ops::ElementwiseOpGrad);
+REGISTER_OP_CPU_KERNEL(
+    elementwise_add,
+    ops::ElementwiseAddKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(
+    elementwise_add_grad,
+    ops::ElementwiseAddGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/elementwise_add_op.cu

+/* 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. */
+
+#define EIGEN_USE_GPU
+#include "paddle/operators/elementwise_add_op.h"
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_GPU_KERNEL(
+    elementwise_add,
+    ops::ElementwiseAddKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(
+    elementwise_add_grad,
+    ops::ElementwiseAddGradKernel<paddle::platform::GPUPlace, float>);

paddle/operators/elementwise_add_op.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. */
+
+#include "paddle/operators/elementwise_op.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename Place, typename T>
+class ElementwiseAddKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    ElementwiseCompute<EigenAddFunctor, Place, T>(ctx);
+  }
+};
+
+template <typename T>
+struct ElementwiseAddGradFunctor {
+  template <typename Device, typename X, typename Y, typename Z, typename dX,
+            typename dY, typename dZ>
+  void operator()(Device d, X x, Y y, Z z, dX dx, dY dy, dZ dz) {
+    auto dz_e = framework::EigenVector<T>::Flatten(*dz);
+    if (dx) {
+      auto dx_e = framework::EigenVector<T>::Flatten(*dx);
+      dx_e.device(d) = dz_e;
+    }
+    if (dy) {
+      auto dy_e = framework::EigenVector<T>::Flatten(*dy);
+      dy_e.device(d) = dz_e;
+    }
+  }
+};
+
+template <typename T>
+struct ElementwiseAddOneGradFunctor {
+  template <typename Device, typename X, typename Y, typename Z, typename dX,
+            typename dY, typename dZ>
+  void operator()(Device d, X x, Y y, Z z, dX dx, dY dy, dZ dz) {
+    auto dz_e = framework::EigenVector<T>::Flatten(*dz);
+    if (dx) {
+      auto dx_e = framework::EigenVector<T>::Flatten(*dx);
+      dx_e.device(d) = dz_e;
+    }
+    if (dy) {
+      auto dy_e = framework::EigenVector<T>::Flatten(*dy);
+      dy_e.device(d) = dz_e.sum();
+    }
+  }
+};
+
+template <typename T>
+struct ElementwiseAddBroadCastGradFunctor {
+  template <typename Device, typename X, typename Y, typename Z, typename dX,
+            typename dY, typename dZ, typename Pre, typename N>
+  void operator()(Device d, X x, Y y, Z z, dX dx, dY dy, dZ dz, Pre pre, N n) {
+    auto dz_e = framework::EigenVector<T>::Flatten(*dz);
+    if (dx) {
+      auto dx_e = framework::EigenVector<T>::Flatten(*dx);
+      dx_e.device(d) = dz_e;
+    }
+
+    if (dy) {
+      auto dy_e = framework::EigenVector<T>::Flatten(*dy);
+      dy_e.device(d) = dz_e.reshape(Eigen::DSizes<int, 2>(pre, n))
+                           .sum(Eigen::array<int, 1>{{0}});
+    }
+  }
+};
+
+template <typename T>
+struct ElementwiseAddBroadCast2GradFunctor {
+  template <typename Device, typename X, typename Y, typename Z, typename dX,
+            typename dY, typename dZ, typename Pre, typename N, typename Post>
+  void operator()(Device d, X x, Y y, Z z, dX dx, dY dy, dZ dz, Pre pre, N n,
+                  Post post) {
+    auto dz_e = framework::EigenVector<T>::Flatten(*dz);
+    if (dx) {
+      auto dx_e = framework::EigenVector<T>::Flatten(*dx);
+      dx_e.device(d) = dz_e;
+    }
+
+    if (dy) {
+      auto dy_e = framework::EigenVector<T>::Flatten(*dy);
+      dy_e.device(d) = dz_e.reshape(Eigen::DSizes<int, 3>(pre, n, post))
+                           .sum(Eigen::array<int, 2>{{0, 2}});
+    }
+  }
+};
+
+template <typename Place, typename T>
+class ElementwiseAddGradKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    ElementwiseGradCompute<Place, T, ElementwiseAddGradFunctor<T>,
+                           ElementwiseAddOneGradFunctor<T>,
+                           ElementwiseAddBroadCastGradFunctor<T>,
+                           ElementwiseAddBroadCast2GradFunctor<T>>(ctx);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/operators/elementwise_div_op.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 "paddle/operators/elementwise_div_op.h"
+
+namespace paddle {
+namespace operators {
+class ElementwiseDivOpMaker : public ElementwiseOpMaker {
+ public:
+  ElementwiseDivOpMaker(framework::OpProto* proto,
+                        framework::OpAttrChecker* op_checker)
+      : ElementwiseOpMaker(proto, op_checker) {
+    SetComment("Div", "Out = X / Y");
+    AddComment(comment_);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP(elementwise_div, ops::ElementwiseOp, ops::ElementwiseDivOpMaker,
+            elementwise_div_grad, ops::ElementwiseOpGrad);
+REGISTER_OP_CPU_KERNEL(
+    elementwise_div,
+    ops::ElementwiseDivKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(
+    elementwise_div_grad,
+    ops::ElementwiseDivGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/elementwise_div_op.cu

+/* 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. */
+
+#define EIGEN_USE_GPU
+#include "paddle/operators/elementwise_div_op.h"
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_GPU_KERNEL(
+    elementwise_div,
+    ops::ElementwiseDivKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(
+    elementwise_div_grad,
+    ops::ElementwiseDivGradKernel<paddle::platform::GPUPlace, float>);

paddle/operators/elementwise_div_op.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. */
+
+#include "paddle/operators/elementwise_op.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename Place, typename T>
+class ElementwiseDivKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    ElementwiseCompute<EigenDivFunctor, Place, T>(ctx);
+  }
+};
+
+template <typename T>
+struct ElementwiseDivGradFunctor {
+  template <typename Device, typename X, typename Y, typename Z, typename dX,
+            typename dY, typename dZ>
+  void operator()(Device d, X x, Y y, Z z, dX dx, dY dy, dZ dz) {
+    auto y_e = framework::EigenVector<T>::Flatten(*y);
+    auto z_e = framework::EigenVector<T>::Flatten(*z);
+    auto dz_e = framework::EigenVector<T>::Flatten(*dz);
+
+    if (dx) {
+      auto dx_e = framework::EigenVector<T>::Flatten(*dx);
+      dx_e.device(d) = dz_e / y_e;
+    }
+
+    if (dy) {
+      auto dy_e = framework::EigenVector<T>::Flatten(*dy);
+      dy_e.device(d) = -1.0 * dz_e * z_e / y_e;
+    }
+  }
+};
+
+template <typename T>
+struct ElementwiseDivBroadCastGradFunctor {
+  template <typename Device, typename X, typename Y, typename Z, typename dX,
+            typename dY, typename dZ, typename Pre, typename N>
+  void operator()(Device d, X x, Y y, Z z, dX dx, dY dy, dZ dz, Pre pre, N n) {
+    auto x_e = framework::EigenVector<T>::Flatten(*x);
+    auto y_e = framework::EigenVector<T>::Flatten(*y);
+    auto dz_e = framework::EigenVector<T>::Flatten(*dz);
+
+    auto y_e_bcast = y_e.reshape(Eigen::DSizes<int, 2>(1, n))
+                         .broadcast(Eigen::DSizes<int, 2>(pre, 1))
+                         .reshape(Eigen::DSizes<int, 1>(x_e.size()));
+
+    if (dx) {
+      auto dx_e = framework::EigenVector<T>::Flatten(*dx);
+      dx_e.device(d) = dz_e / y_e_bcast;
+    }
+
+    if (dy) {
+      auto dy_e = framework::EigenVector<T>::Flatten(*dy);
+      dy_e.device(d) = (-1.0 * (x_e * dz_e) / (y_e_bcast * y_e_bcast))
+                           .reshape(Eigen::DSizes<int, 2>(pre, n))
+                           .sum(Eigen::array<int, 1>{{0}});
+    }
+  }
+};
+
+template <typename T>
+struct ElementwiseDivBroadCast2GradFunctor {
+  template <typename Device, typename X, typename Y, typename Z, typename dX,
+            typename dY, typename dZ, typename Pre, typename N, typename Post>
+  void operator()(Device d, X x, Y y, Z z, dX dx, dY dy, dZ dz, Pre pre, N n,
+                  Post post) {
+    auto x_e = framework::EigenVector<T>::Flatten(*x);
+    auto y_e = framework::EigenVector<T>::Flatten(*y);
+    auto dz_e = framework::EigenVector<T>::Flatten(*dz);
+
+    auto y_e_bcast = y_e.reshape(Eigen::DSizes<int, 3>(1, n, 1))
+                         .broadcast(Eigen::DSizes<int, 3>(pre, 1, post))
+                         .reshape(Eigen::DSizes<int, 1>(x_e.size()));
+    if (dx) {
+      auto dx_e = framework::EigenVector<T>::Flatten(*dx);
+      dx_e.device(d) = dz_e / y_e_bcast;
+    }
+
+    if (dy) {
+      auto dy_e = framework::EigenVector<T>::Flatten(*dy);
+      dy_e.device(d) = (-1.0 * (x_e * dz_e) / (y_e_bcast * y_e_bcast))
+                           .reshape(Eigen::DSizes<int, 3>(pre, n, post))
+                           .sum(Eigen::array<int, 2>{{0, 2}});
+    }
+  }
+};
+
+template <typename Place, typename T>
+class ElementwiseDivGradKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    ElementwiseGradCompute<Place, T, ElementwiseDivGradFunctor<T>,
+                           ElementwiseDivGradFunctor<T>,
+                           ElementwiseDivBroadCastGradFunctor<T>,
+                           ElementwiseDivBroadCast2GradFunctor<T>>(ctx);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/operators/elementwise_mul_op.cc

@@ -17,104 +17,25 @@
 namespace paddle {
 namespace operators {
 
-using Tensor = framework::Tensor;
-
-class ElementWiseMulOp : public framework::OperatorWithKernel {
+class ElementwiseMulOpMaker : public ElementwiseOpMaker {
  public:
-  using framework::OperatorWithKernel::OperatorWithKernel;
-
- protected:
-  void InferShape(const framework::InferShapeContext &ctx) const override {
-    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
-                            "Input(X) of ElementWiseMulOp should not be null.");
-    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"),
-                            "Input(Y) of ElementWiseMulOp should not be null.");
-    PADDLE_ENFORCE_NOT_NULL(
-        ctx.OutputVar("Out"),
-        "Output(Out) of ElementWiseMulOp should not be null.");
-
-    auto x_dim = ctx.Input<Tensor>("X")->dims();
-    auto y_dim = ctx.Input<Tensor>("Y")->dims();
-    PADDLE_ENFORCE_GE(x_dim.size(), y_dim.size(),
-                      "Rank of first input must >= rank of second input.")
-    ctx.Output<framework::Tensor>("Out")->Resize(x_dim);
-    ctx.ShareLoD("X", /*->*/ "Out");
-  }
-};
-
-class ElementWiseMulOpMaker : public framework::OpProtoAndCheckerMaker {
- public:
-  ElementWiseMulOpMaker(framework::OpProto *proto,
-                        framework::OpAttrChecker *op_checker)
-      : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("X", "The first input of elementwise mul op");
-    AddInput("Y", "The second input of elementwise mul op");
-    AddAttr<int>("axis",
-                 R"DOC(
-When shape(Y) does not equal shape(X),Y will be broadcasted 
-to match the shape of X and axis should be dimension index Y in X
-        )DOC")
-        .SetDefault(-1)
-        .EqualGreaterThan(-1);
-
-    AddOutput("Out", "The output of elementwise mul op");
-    AddComment(R"DOC(
-Limited elementwise multiple operator.The equation is: Out = X ⊙ Y.
-1. The shape of Y should be same with X or
-2. Y's shape is a subset of X. 
-   Y will be broadcasted to match the shape of X and axis should be dimension index Y in X.
-   example:
-
-      shape(X) = (2, 3, 4, 5), shape(Y) = (,)
-      shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
-      shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
-      shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
-      shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
-
-Both the input X and Y can carry the LoD (Level of Details) information,
-or not. But the output only shares the LoD with input X.
-)DOC");
+  ElementwiseMulOpMaker(framework::OpProto* proto,
+                        framework::OpAttrChecker* op_checker)
+      : ElementwiseOpMaker(proto, op_checker) {
+    SetComment("Mul", "Out = X ⊙ Y");
+    AddComment(comment_);
   }
 };
 
-class ElementWiseMulOpGrad : public framework::OperatorWithKernel {
- public:
-  using framework::OperatorWithKernel::OperatorWithKernel;
-
- protected:
-  void InferShape(const framework::InferShapeContext &ctx) const override {
-    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null");
-    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) should not be null");
-    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
-                            "Input(Out@GRAD) should not be null");
-
-    auto x_dims = ctx.Input<Tensor>("X")->dims();
-    auto y_dims = ctx.Input<Tensor>("Y")->dims();
-    auto out_dims = ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
-    auto *x_grad = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
-    auto *y_grad = ctx.Output<framework::Tensor>(framework::GradVarName("Y"));
-
-    PADDLE_ENFORCE_GE(x_dims.size(), y_dims.size(),
-                      "Rank of first input must >= rank of second input.")
-
-    if (x_grad) {
-      x_grad->Resize(x_dims);
-    }
-
-    if (y_grad) {
-      y_grad->Resize(y_dims);
-    }
-  }
-};
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP(elementwise_mul, ops::ElementWiseMulOp, ops::ElementWiseMulOpMaker,
-            elementwise_mul_grad, ops::ElementWiseMulOpGrad);
+REGISTER_OP(elementwise_mul, ops::ElementwiseOp, ops::ElementwiseMulOpMaker,
+            elementwise_mul_grad, ops::ElementwiseOpGrad);
 REGISTER_OP_CPU_KERNEL(
     elementwise_mul,
-    ops::ElementWiseMulKernel<paddle::platform::CPUPlace, float>);
+    ops::ElementwiseMulKernel<paddle::platform::CPUPlace, float>);
 REGISTER_OP_CPU_KERNEL(
     elementwise_mul_grad,
-    ops::ElementWiseMulGradKernel<paddle::platform::CPUPlace, float>);
+    ops::ElementwiseMulGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/elementwise_mul_op.cu

@@ -19,7 +19,7 @@ namespace ops = paddle::operators;
 
 REGISTER_OP_GPU_KERNEL(
     elementwise_mul,
-    ops::ElementWiseMulKernel<paddle::platform::GPUPlace, float>);
+    ops::ElementwiseMulKernel<paddle::platform::GPUPlace, float>);
 REGISTER_OP_GPU_KERNEL(
     elementwise_mul_grad,
-    ops::ElementWiseMulGradKernel<paddle::platform::GPUPlace, float>);
+    ops::ElementwiseMulGradKernel<paddle::platform::GPUPlace, float>);

paddle/operators/elementwise_mul_op.h

@@ -13,171 +13,104 @@
    limitations under the License. */
 
 #pragma once
-#include "paddle/framework/eigen.h"
-#include "paddle/framework/op_registry.h"
+#include "paddle/operators/elementwise_op.h"
 
 namespace paddle {
 namespace operators {
-/*
- * Out = X ⊙ Y
- * 1. shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
- *    pre=2, n=3*4, post=5
- * 2. shape(X) = (2, 3, 4, 5), shape(Y) = (4,5)
- *    pre=2*3, n=4*5, post=1
- */
-
-inline void get_mid_dims(const framework::DDim& x_dims,
-                         const framework::DDim& y_dims, const int axis,
-                         int& pre, int& n, int& post) {
-  pre = 1;
-  n = 1;
-  post = 1;
-  for (int i = 0; i < axis; ++i) {
-    pre *= x_dims[i];
-  }
-
-  for (int i = 0; i < y_dims.size(); ++i) {
-    PADDLE_ENFORCE_EQ(x_dims[i + axis], y_dims[i],
-                      "Broadcast dimension mismatch.");
-    n *= y_dims[i];
-  }
-
-  for (int i = axis + y_dims.size(); i < x_dims.size(); ++i) {
-    post *= x_dims[i];
-  }
-}
 
 template <typename Place, typename T>
-class ElementWiseMulKernel : public framework::OpKernel {
+class ElementwiseMulKernel : public framework::OpKernel {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
-    using Tensor = framework::Tensor;
-
-    auto* x = ctx.Input<Tensor>("X");
-    auto* y = ctx.Input<Tensor>("Y");
-    auto* z = ctx.Output<Tensor>("Out");
-    z->mutable_data<T>(ctx.GetPlace());
+    ElementwiseCompute<EigenMulFunctor, Place, T>(ctx);
+  }
+};
 
+template <typename T>
+struct ElementwiseMulGradFunctor {
+  template <typename Device, typename X, typename Y, typename Z, typename dX,
+            typename dY, typename dZ>
+  void operator()(Device d, X x, Y y, Z z, dX dx, dY dy, dZ dz) {
     auto x_e = framework::EigenVector<T>::Flatten(*x);
     auto y_e = framework::EigenVector<T>::Flatten(*y);
-    auto z_e = framework::EigenVector<T>::Flatten(*z);
+    auto dz_e = framework::EigenVector<T>::Flatten(*dz);
 
-    auto x_dims = x->dims();
-    auto y_dims = y->dims();
-    PADDLE_ENFORCE_GE(x_dims.size(), y_dims.size(),
-                      "Rank of first input must >= rank of second input.")
-
-    if (x_dims == y_dims || product(y_dims) == 1) {
-      z_e.device(ctx.GetEigenDevice<Place>()) = x_e * y_e;
-      return;
+    if (dx) {
+      auto dx_e = framework::EigenVector<T>::Flatten(*dx);
+      dx_e.device(d) = dz_e * y_e;
     }
 
-    int axis = ctx.Attr<int>("axis");
-    axis = (axis == -1 ? x_dims.size() - y_dims.size() : axis);
-    PADDLE_ENFORCE(axis >= 0 && axis < x_dims.size(),
-                   "Axis should be in range [0, x_dims)");
-
-    int pre, n, post;
-    get_mid_dims(x_dims, y_dims, axis, pre, n, post);
-    if (post == 1) {
-      auto y_bcast = y_e.reshape(Eigen::DSizes<int, 2>(1, n))
-                         .broadcast(Eigen::DSizes<int, 2>(pre, 1))
-                         .reshape(Eigen::DSizes<int, 1>(x_e.size()));
-      z_e.device(ctx.GetEigenDevice<Place>()) = x_e * y_bcast;
-      return;
-    } else {
-      auto y_bcast = y_e.reshape(Eigen::DSizes<int, 3>(1, n, 1))
-                         .broadcast(Eigen::DSizes<int, 3>(pre, 1, post))
-                         .reshape(Eigen::DSizes<int, 1>(x_e.size()));
-      z_e.device(ctx.GetEigenDevice<Place>()) = x_e * y_bcast;
-      return;
+    if (dy) {
+      auto dy_e = framework::EigenVector<T>::Flatten(*dy);
+      dy_e.device(d) = x_e * dz_e;
     }
   }
 };
 
-template <typename Place, typename T>
-class ElementWiseMulGradKernel : public framework::OpKernel {
- public:
-  void Compute(const framework::ExecutionContext& ctx) const override {
-    using Tensor = framework::Tensor;
-
-    auto* x = ctx.Input<Tensor>("X");
-    auto* y = ctx.Input<Tensor>("Y");
-    auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
-
+template <typename T>
+struct ElementwiseMulBroadCastGradFunctor {
+  template <typename Device, typename X, typename Y, typename Z, typename dX,
+            typename dY, typename dZ, typename Pre, typename N>
+  void operator()(Device d, X x, Y y, Z z, dX dx, dY dy, dZ dz, Pre pre, N n) {
     auto x_e = framework::EigenVector<T>::Flatten(*x);
     auto y_e = framework::EigenVector<T>::Flatten(*y);
-    auto dout_e = framework::EigenVector<T>::Flatten(*dout);
+    auto dz_e = framework::EigenVector<T>::Flatten(*dz);
 
-    auto x_dims = x->dims();
-    auto y_dims = y->dims();
+    auto y_e_bcast = y_e.reshape(Eigen::DSizes<int, 2>(1, n))
+                         .broadcast(Eigen::DSizes<int, 2>(pre, 1))
+                         .reshape(Eigen::DSizes<int, 1>(x_e.size()));
 
-    auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
-    auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
     if (dx) {
-      dx->mutable_data<T>(ctx.GetPlace());
+      auto dx_e = framework::EigenVector<T>::Flatten(*dx);
+      dx_e.device(d) = dz_e * y_e_bcast;
     }
+
     if (dy) {
-      dy->mutable_data<T>(ctx.GetPlace());
+      auto dy_e = framework::EigenVector<T>::Flatten(*dy);
+      dy_e.device(d) = (x_e * dz_e)
+                           .reshape(Eigen::DSizes<int, 2>(pre, n))
+                           .sum(Eigen::array<int, 1>{{0}});
     }
+  }
+};
 
-    if (x_dims == y_dims || product(y_dims) == 1) {
-      if (dx) {
-        auto dx_e = framework::EigenVector<T>::Flatten(*dx);
-        dx_e.device(ctx.GetEigenDevice<Place>()) = dout_e * y_e;
-      }
-
-      if (dy) {
-        auto dy_e = framework::EigenVector<T>::Flatten(*dy);
-        dy_e.device(ctx.GetEigenDevice<Place>()) = x_e * dout_e;
-      }
-      return;
+template <typename T>
+struct ElementwiseMulBroadCast2GradFunctor {
+  template <typename Device, typename X, typename Y, typename Z, typename dX,
+            typename dY, typename dZ, typename Pre, typename N, typename Post>
+  void operator()(Device d, X x, Y y, Z z, dX dx, dY dy, dZ dz, Pre pre, N n,
+                  Post post) {
+    auto x_e = framework::EigenVector<T>::Flatten(*x);
+    auto y_e = framework::EigenVector<T>::Flatten(*y);
+    auto dz_e = framework::EigenVector<T>::Flatten(*dz);
+
+    auto y_e_bcast = y_e.reshape(Eigen::DSizes<int, 3>(1, n, 1))
+                         .broadcast(Eigen::DSizes<int, 3>(pre, 1, post))
+                         .reshape(Eigen::DSizes<int, 1>(x_e.size()));
+    if (dx) {
+      auto dx_e = framework::EigenVector<T>::Flatten(*dx);
+      dx_e.device(d) = dz_e * y_e_bcast;
     }
 
-    int axis = ctx.Attr<int>("axis");
-    axis = (axis == -1 ? x_dims.size() - y_dims.size() : axis);
-
-    int pre, n, post;
-    get_mid_dims(x_dims, y_dims, axis, pre, n, post);
-
-    // TODO(gongweibao): wrap reshape to a function.
-    if (post == 1) {
-      auto y_e_bcast = y_e.reshape(Eigen::DSizes<int, 2>(1, n))
-                           .broadcast(Eigen::DSizes<int, 2>(pre, 1))
-                           .reshape(Eigen::DSizes<int, 1>(x_e.size()));
-      if (dx) {
-        auto dx_e = framework::EigenVector<T>::Flatten(*dx);
-        dx_e.device(ctx.GetEigenDevice<Place>()) = dout_e * y_e_bcast;
-      }
-
-      if (dy) {
-        auto dy_e = framework::EigenVector<T>::Flatten(*dy);
-        dy_e.device(ctx.GetEigenDevice<Place>()) =
-            (x_e * dout_e)
-                .reshape(Eigen::DSizes<int, 2>(pre, n))
-                .sum(Eigen::array<int, 1>{{0}});
-      }
-      return;
-    } else {
-      auto y_e_bcast = y_e.reshape(Eigen::DSizes<int, 3>(1, n, 1))
-                           .broadcast(Eigen::DSizes<int, 3>(pre, 1, post))
-                           .reshape(Eigen::DSizes<int, 1>(x_e.size()));
-      if (dx) {
-        auto dx_e = framework::EigenVector<T>::Flatten(*dx);
-        dx_e.device(ctx.GetEigenDevice<Place>()) = dout_e * y_e_bcast;
-      }
-
-      if (dy) {
-        auto dy_e = framework::EigenVector<T>::Flatten(*dy);
-        dy_e.device(ctx.GetEigenDevice<Place>()) =
-            (x_e * dout_e)
-                .reshape(Eigen::DSizes<int, 3>(pre, n, post))
-                .sum(Eigen::array<int, 2>{{0, 2}});
-      }
-      return;
+    if (dy) {
+      auto dy_e = framework::EigenVector<T>::Flatten(*dy);
+      dy_e.device(d) = (x_e * dz_e)
+                           .reshape(Eigen::DSizes<int, 3>(pre, n, post))
+                           .sum(Eigen::array<int, 2>{{0, 2}});
     }
   }
 };
 
+template <typename Place, typename T>
+class ElementwiseMulGradKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    ElementwiseGradCompute<Place, T, ElementwiseMulGradFunctor<T>,
+                           ElementwiseMulGradFunctor<T>,
+                           ElementwiseMulBroadCastGradFunctor<T>,
+                           ElementwiseMulBroadCast2GradFunctor<T>>(ctx);
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle

paddle/operators/elementwise_op.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 <iostream>
+#include "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
+#include "paddle/operators/math/math_function.h"
+
+namespace paddle {
+namespace operators {
+
+/*
+ * Out = X ⊙ Y
+ * If Y's shape does not match X' shape, they will be reshaped.
+ * For example:
+ * 1. shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
+ *    pre=2, n=3*4, post=5
+ *    x.shape(2, 12, 5) * y.shape(1,12,1).broadcast(2,12,5)
+ * 2. shape(X) = (2, 3, 4, 5), shape(Y) = (4,5)
+ *    pre=2*3, n=4*5, post=1
+ *    x.shape(2, 3, 20) * y.shape(1,1,20).broadcast(2,3,20)
+ */
+inline void get_mid_dims(const framework::DDim& x_dims,
+                         const framework::DDim& y_dims, const int axis,
+                         int& pre, int& n, int& post) {
+  pre = 1;
+  n = 1;
+  post = 1;
+  for (int i = 0; i < axis; ++i) {
+    pre *= x_dims[i];
+  }
+
+  for (int i = 0; i < y_dims.size(); ++i) {
+    PADDLE_ENFORCE_EQ(x_dims[i + axis], y_dims[i],
+                      "Broadcast dimension mismatch.");
+    n *= y_dims[i];
+  }
+
+  for (int i = axis + y_dims.size(); i < x_dims.size(); ++i) {
+    post *= x_dims[i];
+  }
+}
+
+#define EIGEN_FUNCTOR(name, eigen_op)                                          \
+  struct Eigen##name##Functor {                                                \
+    template <typename Place, typename T>                                      \
+    inline void Run(const framework::Tensor* x, const framework::Tensor* y,    \
+                    framework::Tensor* z,                                      \
+                    const framework::ExecutionContext& ctx) {                  \
+      auto x_e = framework::EigenVector<T>::Flatten(*x);                       \
+      auto y_e = framework::EigenVector<T>::Flatten(*y);                       \
+      auto z_e = framework::EigenVector<T>::Flatten(*z);                       \
+      z_e.device(ctx.GetEigenDevice<Place>()) = eigen_op(x_e, y_e);            \
+    }                                                                          \
+    template <typename Place, typename T>                                      \
+    inline void RunBroadCast(const framework::Tensor* x,                       \
+                             const framework::Tensor* y, framework::Tensor* z, \
+                             const framework::ExecutionContext& ctx, int pre,  \
+                             int n) {                                          \
+      auto x_e = framework::EigenVector<T>::Flatten(*x);                       \
+      auto y_e = framework::EigenVector<T>::Flatten(*y);                       \
+      auto z_e = framework::EigenVector<T>::Flatten(*z);                       \
+      auto y_bcast = y_e.reshape(Eigen::DSizes<int, 2>(1, n))                  \
+                         .broadcast(Eigen::DSizes<int, 2>(pre, 1))             \
+                         .reshape(Eigen::DSizes<int, 1>(x_e.size()));          \
+      z_e.device(ctx.GetEigenDevice<Place>()) = eigen_op(x_e, y_bcast);        \
+    }                                                                          \
+    template <typename Place, typename T>                                      \
+    inline void RunBroadCast2(const framework::Tensor* x,                      \
+                              const framework::Tensor* y,                      \
+                              framework::Tensor* z,                            \
+                              const framework::ExecutionContext& ctx, int pre, \
+                              int n, int post) {                               \
+      auto x_e = framework::EigenVector<T>::Flatten(*x);                       \
+      auto y_e = framework::EigenVector<T>::Flatten(*y);                       \
+      auto z_e = framework::EigenVector<T>::Flatten(*z);                       \
+      auto y_bcast = y_e.reshape(Eigen::DSizes<int, 3>(1, n, 1))               \
+                         .broadcast(Eigen::DSizes<int, 3>(pre, 1, post))       \
+                         .reshape(Eigen::DSizes<int, 1>(x_e.size()));          \
+      z_e.device(ctx.GetEigenDevice<Place>()) = eigen_op(x_e, y_bcast);        \
+    }                                                                          \
+  }
+
+template <class functor, typename Place, typename T>
+void ElementwiseCompute(const framework::ExecutionContext& ctx) {
+  using Tensor = framework::Tensor;
+
+  auto* x = ctx.Input<Tensor>("X");
+  auto* y = ctx.Input<Tensor>("Y");
+  auto* z = ctx.Output<Tensor>("Out");
+  z->mutable_data<T>(ctx.GetPlace());
+
+  auto x_dims = x->dims();
+  auto y_dims = y->dims();
+  PADDLE_ENFORCE_GE(x_dims.size(), y_dims.size(),
+                    "Rank of first input must >= rank of second input.")
+
+  if (x_dims == y_dims || product(y_dims) == 1) {
+    functor f;
+    f.template Run<Place, T>(x, y, z, ctx);
+    return;
+  }
+
+  int axis = ctx.Attr<int>("axis");
+  axis = (axis == -1 ? x_dims.size() - y_dims.size() : axis);
+  PADDLE_ENFORCE(axis >= 0 && axis < x_dims.size(),
+                 "Axis should be in range [0, x_dims)");
+
+  int pre, n, post;
+  get_mid_dims(x_dims, y_dims, axis, pre, n, post);
+  if (post == 1) {
+    functor f;
+    f.template RunBroadCast<Place, T>(x, y, z, ctx, pre, n);
+    return;
+  } else {
+    functor f;
+    f.template RunBroadCast2<Place, T>(x, y, z, ctx, pre, n, post);
+    return;
+  }
+}
+
+#define EIGEN_ADD(x, y) ((x) + (y))
+EIGEN_FUNCTOR(Add, EIGEN_ADD);
+
+#define EIGEN_SUB(x, y) ((x) - (y))
+EIGEN_FUNCTOR(Sub, EIGEN_SUB);
+
+#define EIGEN_MUL(x, y) ((x) * (y))
+EIGEN_FUNCTOR(Mul, EIGEN_MUL);
+
+#define EIGEN_DIV(x, y) ((x) / (y))
+EIGEN_FUNCTOR(Div, EIGEN_DIV);
+
+template <typename Place, typename T, typename functor, typename functor1,
+          typename broadcastfunctor, typename broadcast2functor>
+void ElementwiseGradCompute(const framework::ExecutionContext& ctx) {
+  using Tensor = framework::Tensor;
+
+  auto* x = ctx.Input<Tensor>("X");
+  auto* y = ctx.Input<Tensor>("Y");
+  auto* out = ctx.Input<Tensor>("Out");
+  auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
+
+  auto place = ctx.GetEigenDevice<Place>();
+
+  auto x_dims = x->dims();
+  auto y_dims = y->dims();
+
+  auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
+  auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
+  if (dx) {
+    dx->mutable_data<T>(ctx.GetPlace());
+  }
+  if (dy) {
+    dy->mutable_data<T>(ctx.GetPlace());
+  }
+
+  if (x_dims == y_dims) {
+    functor f;
+    f(place, x, y, out, dx, dy, dout);
+    return;
+  }
+
+  if (product(y_dims) == 1) {
+    functor1 f;
+    f(place, x, y, out, dx, dy, dout);
+    return;
+  }
+
+  int axis = ctx.Attr<int>("axis");
+  axis = (axis == -1 ? x_dims.size() - y_dims.size() : axis);
+
+  int pre, n, post;
+  get_mid_dims(x_dims, y_dims, axis, pre, n, post);
+
+  if (post == 1) {
+    broadcastfunctor f;
+    f(place, x, y, out, dx, dy, dout, pre, n);
+    return;
+  } else {
+    broadcast2functor f;
+    f(place, x, y, out, dx, dy, dout, pre, n, post);
+    return;
+  }
+}
+
+class ElementwiseOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  using Tensor = framework::Tensor;
+  void InferShape(const framework::InferShapeContext& ctx) const override {
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
+                            "Input(X) of elementwise op should not be null");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"),
+                            "Input(Y) of elementwise op should not be null");
+    PADDLE_ENFORCE_NOT_NULL(
+        ctx.OutputVar("Out"),
+        "Output(Out) of elementwise op should not be null.");
+
+    auto x_dim = ctx.Input<Tensor>("X")->dims();
+    auto y_dim = ctx.Input<Tensor>("Y")->dims();
+    PADDLE_ENFORCE_GE(x_dim.size(), y_dim.size(),
+                      "Rank of first input must >= rank of second input.")
+    ctx.Output<framework::Tensor>("Out")->Resize(x_dim);
+    ctx.ShareLoD("X", /*->*/ "Out");
+  }
+};
+
+class ElementwiseOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  ElementwiseOpMaker(framework::OpProto* proto,
+                     framework::OpAttrChecker* op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", R"DOC(
+The first input of elementwise op, it's a tensor of any dimensions.
+)DOC");
+    AddInput("Y", R"DOC(
+The sencond input of elementwise op, it's a tensor and it's dimensions
+must be small or equal to X's dimensions.
+)DOC");
+    AddAttr<int>("axis",
+                 R"DOC(
+When the shape(Y) does not equal the shape(X),Y will be broadcasted 
+to match the shape of X and axis should be dimension index Y in X
+        )DOC")
+        .SetDefault(-1)
+        .EqualGreaterThan(-1);
+
+    AddOutput("Out", "The output of elementwise op");
+    comment_ = R"DOC(
+Limited elementwise {name} operator.The equation is: Out = {equation}.
+1. The shape of Y should be same with X or
+2. Y's shape is a subset of X. 
+   Y will be broadcasted to match the shape of X and axis should be dimension index Y in X.
+
+   example:
+      shape(X) = (2, 3, 4, 5), shape(Y) = (,)
+      shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
+      shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
+      shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
+      shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
+
+Both the input X and Y can carry the LoD (Level of Details) information,
+or not. But the output only shares the LoD with input X.
+)DOC";
+    AddComment(comment_);
+  }
+
+ protected:
+  std::string comment_;
+
+  void Replace(std::string& src, std::string from, std::string to) {
+    std::size_t len_from = std::strlen(from.c_str());
+    std::size_t len_to = std::strlen(to.c_str());
+    for (std::size_t pos = src.find(from); pos != std::string::npos;
+         pos = src.find(from, pos + len_to)) {
+      src.replace(pos, len_from, to);
+    }
+  }
+
+  void SetComment(std::string name, std::string equation) {
+    Replace(comment_, "{name}", name);
+    Replace(comment_, "{equation}", equation);
+  }
+};
+
+class ElementwiseOpGrad : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+  using Tensor = framework::Tensor;
+
+ protected:
+  void InferShape(const framework::InferShapeContext& ctx) const override {
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) should not be null");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
+                            "Input(Out@GRAD) should not be null");
+
+    auto x_dims = ctx.Input<Tensor>("X")->dims();
+    auto y_dims = ctx.Input<Tensor>("Y")->dims();
+    auto out_dims = ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
+    auto* x_grad = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
+    auto* y_grad = ctx.Output<framework::Tensor>(framework::GradVarName("Y"));
+
+    PADDLE_ENFORCE_GE(x_dims.size(), y_dims.size(),
+                      "Rank of first input must >= rank of second input.")
+
+    if (x_grad) {
+      x_grad->Resize(x_dims);
+    }
+
+    if (y_grad) {
+      y_grad->Resize(y_dims);
+    }
+  }
+};
+}  // namespace operators
+}  // namespace paddle

paddle/operators/elementwise_sub_op.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 "paddle/operators/elementwise_sub_op.h"
+
+namespace paddle {
+namespace operators {
+class ElementwiseSubOpMaker : public ElementwiseOpMaker {
+ public:
+  ElementwiseSubOpMaker(framework::OpProto* proto,
+                        framework::OpAttrChecker* op_checker)
+      : ElementwiseOpMaker(proto, op_checker) {
+    SetComment("Sub", "Out = X - Y");
+    AddComment(comment_);
+  }
+};
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP(elementwise_sub, ops::ElementwiseOp, ops::ElementwiseSubOpMaker,
+            elementwise_sub_grad, ops::ElementwiseOpGrad);
+REGISTER_OP_CPU_KERNEL(
+    elementwise_sub,
+    ops::ElementwiseSubKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(
+    elementwise_sub_grad,
+    ops::ElementwiseSubGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/elementwise_sub_op.cu

+/* 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. */
+
+#define EIGEN_USE_GPU
+#include "paddle/operators/elementwise_sub_op.h"
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_GPU_KERNEL(
+    elementwise_sub,
+    ops::ElementwiseSubKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(
+    elementwise_sub_grad,
+    ops::ElementwiseSubGradKernel<paddle::platform::GPUPlace, float>);

paddle/operators/elementwise_sub_op.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. */
+
+#include "paddle/operators/elementwise_op.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename Place, typename T>
+class ElementwiseSubKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    ElementwiseCompute<EigenSubFunctor, Place, T>(ctx);
+  }
+};
+
+template <typename T>
+struct ElementwiseSubGradFunctor {
+  template <typename Device, typename X, typename Y, typename Z, typename dX,
+            typename dY, typename dZ>
+  void operator()(Device d, X x, Y y, Z z, dX dx, dY dy, dZ dz) {
+    auto dz_e = framework::EigenVector<T>::Flatten(*dz);
+    if (dx) {
+      auto dx_e = framework::EigenVector<T>::Flatten(*dx);
+      dx_e.device(d) = dz_e;
+    }
+    if (dy) {
+      auto dy_e = framework::EigenVector<T>::Flatten(*dy);
+      dy_e.device(d) = (-1.0) * dz_e;
+    }
+  }
+};
+
+template <typename T>
+struct ElementwiseSubOneGradFunctor {
+  template <typename Device, typename X, typename Y, typename Z, typename dX,
+            typename dY, typename dZ>
+  void operator()(Device d, X x, Y y, Z z, dX dx, dY dy, dZ dz) {
+    auto dz_e = framework::EigenVector<T>::Flatten(*dz);
+    if (dx) {
+      auto dx_e = framework::EigenVector<T>::Flatten(*dx);
+      dx_e.device(d) = dz_e;
+    }
+    if (dy) {
+      auto dy_e = framework::EigenVector<T>::Flatten(*dy);
+      dy_e.device(d) = (-1.0) * dz_e.sum();
+    }
+  }
+};
+
+template <typename T>
+struct ElementwiseSubBroadCastGradFunctor {
+  template <typename Device, typename X, typename Y, typename Z, typename dX,
+            typename dY, typename dZ, typename Pre, typename N>
+  void operator()(Device d, X x, Y y, Z z, dX dx, dY dy, dZ dz, Pre pre, N n) {
+    auto dz_e = framework::EigenVector<T>::Flatten(*dz);
+    if (dx) {
+      auto dx_e = framework::EigenVector<T>::Flatten(*dx);
+      dx_e.device(d) = dz_e;
+    }
+
+    if (dy) {
+      auto dy_e = framework::EigenVector<T>::Flatten(*dy);
+      dy_e.device(d) = (-1.0) *
+                       dz_e.reshape(Eigen::DSizes<int, 2>(pre, n))
+                           .sum(Eigen::array<int, 1>{{0}});
+    }
+  }
+};
+
+template <typename T>
+struct ElementwiseSubBroadCast2GradFunctor {
+  template <typename Device, typename X, typename Y, typename Z, typename dX,
+            typename dY, typename dZ, typename Pre, typename N, typename Post>
+  void operator()(Device d, X x, Y y, Z z, dX dx, dY dy, dZ dz, Pre pre, N n,
+                  Post post) {
+    auto dz_e = framework::EigenVector<T>::Flatten(*dz);
+    if (dx) {
+      auto dx_e = framework::EigenVector<T>::Flatten(*dx);
+      dx_e.device(d) = dz_e;
+    }
+
+    if (dy) {
+      auto dy_e = framework::EigenVector<T>::Flatten(*dy);
+      dy_e.device(d) = (-1.0) *
+                       dz_e.reshape(Eigen::DSizes<int, 3>(pre, n, post))
+                           .sum(Eigen::array<int, 2>{{0, 2}});
+    }
+  }
+};
+
+template <typename Place, typename T>
+class ElementwiseSubGradKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    ElementwiseGradCompute<Place, T, ElementwiseSubGradFunctor<T>,
+                           ElementwiseSubOneGradFunctor<T>,
+                           ElementwiseSubBroadCastGradFunctor<T>,
+                           ElementwiseSubBroadCast2GradFunctor<T>>(ctx);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

python/paddle/v2/framework/tests/test_elementwise_add_op.py

+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestElementwiseOp(OpTest):
+    def setUp(self):
+        self.op_type = "elementwise_add"
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [13, 17]).astype("float32"),
+            'Y': np.random.uniform(0.1, 1, [13, 17]).astype("float32")
+        }
+        self.outputs = {'Out': np.add(self.inputs['X'], self.inputs['Y'])}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad_normal(self):
+        self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.005)
+
+    def test_check_grad_ingore_x(self):
+        self.check_grad(
+            ['Y'], 'Out', max_relative_error=0.005, no_grad_set=set("X"))
+
+    def test_check_grad_ingore_y(self):
+        self.check_grad(
+            ['X'], 'Out', max_relative_error=0.005, no_grad_set=set('Y'))
+
+
+class TestElementwiseAddOp_Vector(TestElementwiseOp):
+    def setUp(self):
+        self.op_type = "elementwise_add"
+        self.inputs = {
+            'X': np.random.random((32, )).astype("float32"),
+            'Y': np.random.random((32, )).astype("float32")
+        }
+        self.outputs = {'Out': np.add(self.inputs['X'], self.inputs['Y'])}
+
+
+class TestElementwiseAddOp_broadcast_0(TestElementwiseOp):
+    def setUp(self):
+        self.op_type = "elementwise_add"
+        self.inputs = {
+            'X': np.random.rand(2, 3, 4).astype(np.float32),
+            'Y': np.random.rand(2).astype(np.float32)
+        }
+
+        self.attrs = {'axis': 0}
+        self.outputs = {
+            'Out': self.inputs['X'] + self.inputs['Y'].reshape(2, 1, 1)
+        }
+
+
+class TestElementwiseAddOp_broadcast_1(TestElementwiseOp):
+    def setUp(self):
+        self.op_type = "elementwise_add"
+        self.inputs = {
+            'X': np.random.rand(2, 3, 4).astype(np.float32),
+            'Y': np.random.rand(3).astype(np.float32)
+        }
+
+        self.attrs = {'axis': 1}
+        self.outputs = {
+            'Out': self.inputs['X'] + self.inputs['Y'].reshape(1, 3, 1)
+        }
+
+
+class TestElementwiseAddOp_broadcast_2(TestElementwiseOp):
+    def setUp(self):
+        self.op_type = "elementwise_add"
+        self.inputs = {
+            'X': np.random.rand(2, 3, 4).astype(np.float32),
+            'Y': np.random.rand(4).astype(np.float32)
+        }
+
+        self.outputs = {
+            'Out': self.inputs['X'] + self.inputs['Y'].reshape(1, 1, 4)
+        }
+
+
+class TestElementwiseAddOp_broadcast_3(TestElementwiseOp):
+    def setUp(self):
+        self.op_type = "elementwise_add"
+        self.inputs = {
+            'X': np.random.rand(2, 3, 4, 5).astype(np.float32),
+            'Y': np.random.rand(3, 4).astype(np.float32)
+        }
+
+        self.attrs = {'axis': 1}
+        self.outputs = {
+            'Out': self.inputs['X'] + self.inputs['Y'].reshape(1, 3, 4, 1)
+        }
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/v2/framework/tests/test_elementwise_div_op.py

+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class ElementwiseDivOp(OpTest):
+    def setUp(self):
+        self.op_type = "elementwise_div"
+        """ Warning
+        CPU gradient check error!
+        'X': np.random.random((32,84)).astype("float32"),
+        'Y': np.random.random((32,84)).astype("float32")
+        """
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [13, 17]).astype("float32"),
+            'Y': np.random.uniform(0.1, 1, [13, 17]).astype("float32")
+        }
+        self.outputs = {'Out': np.divide(self.inputs['X'], self.inputs['Y'])}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad_normal(self):
+        self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.05)
+
+    def test_check_grad_ingore_x(self):
+        self.check_grad(
+            ['Y'], 'Out', max_relative_error=0.05, no_grad_set=set("X"))
+
+    def test_check_grad_ingore_y(self):
+        self.check_grad(
+            ['X'], 'Out', max_relative_error=0.05, no_grad_set=set('Y'))
+
+
+class TestElementwiseDivOp_Vector(ElementwiseDivOp):
+    def setUp(self):
+        self.op_type = "elementwise_div"
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [32]).astype("float32"),
+            'Y': np.random.uniform(0.1, 1, [32]).astype("float32")
+        }
+        self.outputs = {'Out': np.divide(self.inputs['X'], self.inputs['Y'])}
+
+
+class TestElementwiseDivOp_broadcast_0(ElementwiseDivOp):
+    def setUp(self):
+        self.op_type = "elementwise_div"
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [2, 3, 4]).astype("float32"),
+            'Y': np.random.uniform(0.1, 1, [2]).astype("float32")
+        }
+
+        self.attrs = {'axis': 0}
+        self.outputs = {
+            'Out':
+            np.divide(self.inputs['X'], self.inputs['Y'].reshape(2, 1, 1))
+        }
+
+
+class TestElementwiseDivOp_broadcast_1(ElementwiseDivOp):
+    def setUp(self):
+        self.op_type = "elementwise_div"
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [2, 3, 4]).astype("float32"),
+            'Y': np.random.uniform(0.1, 1, [3]).astype("float32")
+        }
+
+        self.attrs = {'axis': 1}
+        self.outputs = {
+            'Out':
+            np.divide(self.inputs['X'], self.inputs['Y'].reshape(1, 3, 1))
+        }
+
+
+class TestElementwiseDivOp_broadcast_2(ElementwiseDivOp):
+    def setUp(self):
+        self.op_type = "elementwise_div"
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [2, 3, 4]).astype("float32"),
+            'Y': np.random.uniform(0.1, 1, [4]).astype("float32")
+        }
+
+        self.outputs = {
+            'Out':
+            np.divide(self.inputs['X'], self.inputs['Y'].reshape(1, 1, 4))
+        }
+
+
+class TestElementwiseDivOp_broadcast_3(ElementwiseDivOp):
+    def setUp(self):
+        self.op_type = "elementwise_div"
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [2, 3, 4, 5]).astype("float32"),
+            'Y': np.random.uniform(0.1, 1, [3, 4]).astype("float32")
+        }
+
+        self.attrs = {'axis': 1}
+        self.outputs = {
+            'Out':
+            np.divide(self.inputs['X'], self.inputs['Y'].reshape(1, 3, 4, 1))
+        }
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/v2/framework/tests/test_elementwise_mul_op.py

@@ -3,14 +3,9 @@ import numpy as np
 from op_test import OpTest
 
 
-class TestElementwiseMulOp_Matrix(OpTest):
+class ElementwiseMulOp(OpTest):
     def setUp(self):
         self.op_type = "elementwise_mul"
-        """ Warning
-        CPU gradient check error!
-        'X': np.random.random((32,84)).astype("float32"),
-        'Y': np.random.random((32,84)).astype("float32")
-        """
         self.inputs = {
             'X': np.random.uniform(0.1, 1, [13, 17]).astype("float32"),
             'Y': np.random.uniform(0.1, 1, [13, 17]).astype("float32")
@@ -32,7 +27,7 @@ class TestElementwiseMulOp_Matrix(OpTest):
             ['X'], 'Out', max_relative_error=0.1, no_grad_set=set('Y'))
 
 
-class TestElementwiseMulOp_Vector(OpTest):
+class TestElementwiseMulOp_Vector(ElementwiseMulOp):
     def setUp(self):
         self.op_type = "elementwise_mul"
         self.inputs = {
@@ -41,22 +36,8 @@ class TestElementwiseMulOp_Vector(OpTest):
         }
         self.outputs = {'Out': np.multiply(self.inputs['X'], self.inputs['Y'])}
 
-    def test_check_output(self):
-        self.check_output()
-
-    def test_check_grad_normal(self):
-        self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.1)
-
-    def test_check_grad_ingore_x(self):
-        self.check_grad(
-            ['Y'], 'Out', max_relative_error=0.1, no_grad_set=set("X"))
-
-    def test_check_grad_ingore_y(self):
-        self.check_grad(
-            ['X'], 'Out', max_relative_error=0.1, no_grad_set=set('Y'))
-
 
-class TestElementwiseMulOp_broadcast_0(OpTest):
+class TestElementwiseMulOp_broadcast_0(ElementwiseMulOp):
     def setUp(self):
         self.op_type = "elementwise_mul"
         self.inputs = {
@@ -69,22 +50,8 @@ class TestElementwiseMulOp_broadcast_0(OpTest):
             'Out': self.inputs['X'] * self.inputs['Y'].reshape(2, 1, 1)
         }
 
-    def test_check_output(self):
-        self.check_output()
-
-    def test_check_grad_normal(self):
-        self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.1)
-
-    def test_check_grad_ingore_x(self):
-        self.check_grad(
-            ['Y'], 'Out', max_relative_error=0.1, no_grad_set=set("X"))
-
-    def test_check_grad_ingore_y(self):
-        self.check_grad(
-            ['X'], 'Out', max_relative_error=0.1, no_grad_set=set('Y'))
-
 
-class TestElementwiseMulOp_broadcast_1(OpTest):
+class TestElementwiseMulOp_broadcast_1(ElementwiseMulOp):
     def setUp(self):
         self.op_type = "elementwise_mul"
         self.inputs = {
@@ -97,22 +64,8 @@ class TestElementwiseMulOp_broadcast_1(OpTest):
             'Out': self.inputs['X'] * self.inputs['Y'].reshape(1, 3, 1)
         }
 
-    def test_check_output(self):
-        self.check_output()
-
-    def test_check_grad_normal(self):
-        self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.1)
-
-    def test_check_grad_ingore_x(self):
-        self.check_grad(
-            ['Y'], 'Out', max_relative_error=0.1, no_grad_set=set("X"))
-
-    def test_check_grad_ingore_y(self):
-        self.check_grad(
-            ['X'], 'Out', max_relative_error=0.1, no_grad_set=set('Y'))
-
 
-class TestElementwiseMulOp_broadcast_2(OpTest):
+class TestElementwiseMulOp_broadcast_2(ElementwiseMulOp):
     def setUp(self):
         self.op_type = "elementwise_mul"
         self.inputs = {
@@ -124,22 +77,8 @@ class TestElementwiseMulOp_broadcast_2(OpTest):
             'Out': self.inputs['X'] * self.inputs['Y'].reshape(1, 1, 4)
         }
 
-    def test_check_output(self):
-        self.check_output()
-
-    def test_check_grad_normal(self):
-        self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.1)
-
-    def test_check_grad_ingore_x(self):
-        self.check_grad(
-            ['Y'], 'Out', max_relative_error=0.1, no_grad_set=set("X"))
-
-    def test_check_grad_ingore_y(self):
-        self.check_grad(
-            ['X'], 'Out', max_relative_error=0.1, no_grad_set=set('Y'))
-
 
-class TestElementwiseMulOp_broadcast_3(OpTest):
+class TestElementwiseMulOp_broadcast_3(ElementwiseMulOp):
     def setUp(self):
         self.op_type = "elementwise_mul"
         self.inputs = {

python/paddle/v2/framework/tests/test_elementwise_sub_op.py

+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestElementwiseOp(OpTest):
+    def setUp(self):
+        self.op_type = "elementwise_sub"
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [13, 17]).astype("float32"),
+            'Y': np.random.uniform(0.1, 1, [13, 17]).astype("float32")
+        }
+        self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad_normal(self):
+        self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.005)
+
+    def test_check_grad_ingore_x(self):
+        self.check_grad(
+            ['Y'], 'Out', max_relative_error=0.005, no_grad_set=set("X"))
+
+    def test_check_grad_ingore_y(self):
+        self.check_grad(
+            ['X'], 'Out', max_relative_error=0.005, no_grad_set=set('Y'))
+
+
+class TestElementwiseSubOp_Vector(TestElementwiseOp):
+    def setUp(self):
+        self.op_type = "elementwise_sub"
+        self.inputs = {
+            'X': np.random.random((32, )).astype("float32"),
+            'Y': np.random.random((32, )).astype("float32")
+        }
+        self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']}
+
+
+class TestElementwiseSubOp_broadcast_0(TestElementwiseOp):
+    def setUp(self):
+        self.op_type = "elementwise_sub"
+        self.inputs = {
+            'X': np.random.rand(2, 3, 4).astype(np.float32),
+            'Y': np.random.rand(2).astype(np.float32)
+        }
+
+        self.attrs = {'axis': 0}
+        self.outputs = {
+            'Out': self.inputs['X'] - self.inputs['Y'].reshape(2, 1, 1)
+        }
+
+
+class TestElementwiseSubOp_broadcast_1(TestElementwiseOp):
+    def setUp(self):
+        self.op_type = "elementwise_sub"
+        self.inputs = {
+            'X': np.random.rand(2, 3, 4).astype(np.float32),
+            'Y': np.random.rand(3).astype(np.float32)
+        }
+
+        self.attrs = {'axis': 1}
+        self.outputs = {
+            'Out': self.inputs['X'] - self.inputs['Y'].reshape(1, 3, 1)
+        }
+
+
+class TestElementwiseSubOp_broadcast_2(TestElementwiseOp):
+    def setUp(self):
+        self.op_type = "elementwise_sub"
+        self.inputs = {
+            'X': np.random.rand(2, 3, 4).astype(np.float32),
+            'Y': np.random.rand(4).astype(np.float32)
+        }
+
+        self.outputs = {
+            'Out': self.inputs['X'] - self.inputs['Y'].reshape(1, 1, 4)
+        }
+
+
+class TestElementwiseSubOp_broadcast_3(TestElementwiseOp):
+    def setUp(self):
+        self.op_type = "elementwise_sub"
+        self.inputs = {
+            'X': np.random.rand(2, 3, 4, 5).astype(np.float32),
+            'Y': np.random.rand(3, 4).astype(np.float32)
+        }
+
+        self.attrs = {'axis': 1}
+        self.outputs = {
+            'Out': self.inputs['X'] - self.inputs['Y'].reshape(1, 3, 4, 1)
+        }
+
+
+if __name__ == '__main__':
+    unittest.main()