split elementwise_op.h into two header files

paddle/operators/elementwise_add_op.cc

@@ -13,6 +13,7 @@
    limitations under the License. */
 
 #include "paddle/operators/elementwise_add_op.h"
+#include "paddle/operators/elementwise_op.h"
 
 namespace paddle {
 namespace operators {

paddle/operators/elementwise_add_op.h

@@ -14,7 +14,7 @@
 
 #pragma once
 
-#include "paddle/operators/elementwise_op.h"
+#include "paddle/operators/elementwise_op_function.h"
 
 namespace paddle {
 namespace operators {

paddle/operators/elementwise_div_op.cc

@@ -13,6 +13,7 @@
    limitations under the License. */
 
 #include "paddle/operators/elementwise_div_op.h"
+#include "paddle/operators/elementwise_op.h"
 
 namespace paddle {
 namespace operators {

paddle/operators/elementwise_div_op.h

@@ -14,7 +14,7 @@
 
 #pragma once
 
-#include "paddle/operators/elementwise_op.h"
+#include "paddle/operators/elementwise_op_function.h"
 
 namespace paddle {
 namespace operators {

paddle/operators/elementwise_mul_op.cc

@@ -13,6 +13,7 @@
    limitations under the License. */
 
 #include "paddle/operators/elementwise_mul_op.h"
+#include "paddle/operators/elementwise_op.h"
 
 namespace paddle {
 namespace operators {

paddle/operators/elementwise_mul_op.h

@@ -13,7 +13,7 @@
    limitations under the License. */
 
 #pragma once
-#include "paddle/operators/elementwise_op.h"
+#include "paddle/operators/elementwise_op_function.h"
 
 namespace paddle {
 namespace operators {

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
+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
+    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. */
+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;

paddle/operators/elementwise_op_function.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/operator.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;
+  }
+}
+}  // namespace operators
+}  // namespace paddle

paddle/operators/elementwise_sub_op.cc

@@ -13,6 +13,7 @@
    limitations under the License. */
 
 #include "paddle/operators/elementwise_sub_op.h"
+#include "paddle/operators/elementwise_op.h"
 
 namespace paddle {
 namespace operators {

paddle/operators/elementwise_sub_op.h

@@ -13,7 +13,7 @@
    limitations under the License. */
 
 #pragma once
-#include "paddle/operators/elementwise_op.h"
+#include "paddle/operators/elementwise_op_function.h"
 
 namespace paddle {
 namespace operators {