Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into add_program_proto

cmake/util.cmake

@@ -25,7 +25,7 @@ function(target_circle_link_libraries TARGET_NAME)
             endif()
         endforeach()
         if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang" OR "${CMAKE_CXX_COMPILER_ID}" STREQUAL "AppleClang")
-            if(IOS AND NOT IOS_ENABLE_BITCODE)
+            if(NOT IOS_ENABLE_BITCODE)
                 list(APPEND LIBS "-undefined dynamic_lookup")
             endif()
         endif()

paddle/framework/ddim.cc

@@ -292,5 +292,13 @@ DDim flatten_to_2d(const DDim& src, int num_col_dims) {
 
 DDim flatten_to_1d(const DDim& src) { return make_ddim({product(src)}); }
 
+DDim stride(const DDim& ddim) {
+  std::vector<int64_t> strides(ddim.size());
+  strides[ddim.size() - 1] = 1;
+  for (int i = ddim.size() - 2; i >= 0; --i) {
+    strides[i] = strides[i + 1] * ddim[i + 1];
+  }
+  return framework::make_ddim(strides);
+}
 }  // namespace framework
 }  // namespace paddle

paddle/framework/ddim.h

@@ -121,6 +121,7 @@ DDim flatten_to_2d(const DDim& src, int num_col_dims);
 
 DDim flatten_to_1d(const DDim& src);
 
+DDim stride(const DDim& ddim);
 }  // namespace framework
 }  // namespace paddle
 

paddle/operators/CMakeLists.txt

@@ -55,6 +55,13 @@ function(op_library TARGET)
         set(pybind_flag 1)
     endif()
 
+    # activation_op contains several operators
+    if ("${TARGET}" STREQUAL "activation_op")
+        set(pybind_flag 1)
+        # It's enough to just adding one operator to pybind
+        file(APPEND ${pybind_file} "USE_OP(sigmoid);\n")
+    endif()
+
     # pybind USE_NO_KERNEL_OP
     file(READ ${TARGET}.cc TARGET_CONTENT)
     string(REGEX MATCH "OperatorWithKernel" regex_result "${TARGET_CONTENT}")

paddle/operators/activation_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/activation_op.h"
+
+namespace paddle {
+namespace operators {
+
+class ActivationOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(const framework::InferShapeContext &ctx) const override {
+    ctx.Output<framework::LoDTensor>("Y")->Resize(
+        ctx.Input<framework::Tensor>("X")->dims());
+  }
+};
+
+class ActivationOpGrad : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(const framework::InferShapeContext &ctx) const override {
+    ctx.Output<framework::LoDTensor>(framework::GradVarName("X"))
+        ->Resize(ctx.Input<framework::Tensor>("Y")->dims());
+  }
+};
+
+class SigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  SigmoidOpMaker(framework::OpProto *proto,
+                 framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "Input of Sigmoid operator");
+    AddOutput("Y", "Output of Sigmoid operator");
+    AddComment("Sigmoid activation operator, sigmoid = 1 / (1 + exp(-x))");
+  }
+};
+
+class ExpOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  ExpOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "Input of Exp operator");
+    AddOutput("Y", "Output of Exp operator");
+    AddComment("Exp activation operator, exp(x) = e^x");
+  }
+};
+
+class ReluOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  ReluOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "Input of Relu operator");
+    AddOutput("Y", "Output of Relu operator");
+    AddComment("Relu activation operator, relu(x) = max(x, 0)");
+  }
+};
+
+class TanhOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  TanhOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "Input of Tanh operator");
+    AddOutput("Y", "Output of Tanh operator");
+    AddComment(
+        "Tanh activation operator, tanh = (exp(x) - exp(-x)) / (exp(x) + "
+        "exp(-x))");
+  }
+};
+
+class SqrtOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  SqrtOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "Input of Sqrt operator");
+    AddOutput("Y", "Output of Sqrt operator");
+    AddComment("Sqrt activation operator, sqrt(x) = x^(1/2)");
+  }
+};
+
+class AbsOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  AbsOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "Input of Abs operator");
+    AddOutput("Y", "Output of Abs operator");
+    AddComment("Abs activation operator, abs(x) = |x|");
+  }
+};
+
+class ReciprocalOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  ReciprocalOpMaker(framework::OpProto *proto,
+                    framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "Input of Reciprocal operator");
+    AddOutput("Y", "Output of Reciprocal operator");
+    AddComment("Reciprocal activation operator, reciprocal(x) = 1 / x");
+  }
+};
+
+class LogOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  LogOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "Input of Log operator");
+    AddOutput("Y", "Output of Log operator");
+    AddComment("Log activation operator, log(x) = natural logarithm of x");
+  }
+};
+
+class SquareOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  SquareOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "Input of Square operator");
+    AddOutput("Y", "Output of Square operator");
+    AddComment("Square activation operator, square(x) = x^2");
+  }
+};
+
+template <typename AttrType>
+class BReluOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  BReluOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "Input of BRelu operator");
+    AddOutput("Y", "Output of BRelu operator");
+    AddComment("BRelu activation operator, brelu = max(min(x, t_min), t_max)");
+    AddAttr<AttrType>("t_min", "The min marginal value of BRelu")
+        .SetDefault(static_cast<AttrType>(0));
+    AddAttr<AttrType>("t_max", "The max marginal value of BRelu")
+        .SetDefault(static_cast<AttrType>(24));
+  }
+};
+
+template <typename AttrType>
+class SoftReluOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  SoftReluOpMaker(framework::OpProto *proto,
+                  framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "Input of SoftRelu operator");
+    AddOutput("Y", "Output of SoftRelu operator");
+    AddComment(
+        "SoftRelu activation operator, soft_relu = log(1 + exp(max(min(x, "
+        "threshold), threshold)))");
+    AddAttr<AttrType>("threshold", "The threshold value of SoftRelu")
+        .SetDefault(static_cast<AttrType>(40));
+  }
+};
+
+template <typename AttrType>
+class PowOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  PowOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "Input of Pow operator");
+    AddOutput("Y", "Output of Pow operator");
+    AddComment("Pow activation operator, pow(x, factor) = x^factor");
+    AddAttr<AttrType>("factor", "The exponential factor of Pow")
+        .SetDefault(static_cast<AttrType>(1));
+  }
+};
+
+template <typename AttrType>
+class STanhOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  STanhOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "Input of STanh operator");
+    AddOutput("Y", "Output of STanh operator");
+    AddComment("STanh activation operator, stanh = b * tanh(a * x)");
+    AddAttr<AttrType>("scale_a", "The scale parameter of a for the input")
+        .SetDefault(static_cast<AttrType>(2 / 3));
+    AddAttr<AttrType>("scale_b", "The scale parameter of b for the input")
+        .SetDefault(static_cast<AttrType>(1.7159));
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP(sigmoid, ops::ActivationOp, ops::SigmoidOpMaker, sigmoid_grad,
+            ops::ActivationOpGrad);
+REGISTER_OP_CPU_KERNEL(sigmoid,
+                       ops::ActivationKernel<paddle::platform::CPUPlace, float,
+                                             ops::SigmoidFunctor<float>>);
+REGISTER_OP_CPU_KERNEL(
+    sigmoid_grad, ops::ActivationGradKernel<paddle::platform::CPUPlace, float,
+                                            ops::SigmoidGradFunctor<float>>);
+
+REGISTER_OP(exp, ops::ActivationOp, ops::ExpOpMaker, exp_grad,
+            ops::ActivationOpGrad);
+REGISTER_OP_CPU_KERNEL(
+    exp,
+    ops::ActivationKernel<paddle::platform::CPUPlace, float, ops::ExpFunctor>);
+REGISTER_OP_CPU_KERNEL(exp_grad,
+                       ops::ActivationGradKernel<paddle::platform::CPUPlace,
+                                                 float, ops::ExpGradFunctor>);
+
+REGISTER_OP(relu, ops::ActivationOp, ops::ReluOpMaker, relu_grad,
+            ops::ActivationOpGrad);
+REGISTER_OP_CPU_KERNEL(relu,
+                       ops::ActivationKernel<paddle::platform::CPUPlace, float,
+                                             ops::ReluFunctor<float>>);
+REGISTER_OP_CPU_KERNEL(
+    relu_grad, ops::ActivationGradKernel<paddle::platform::CPUPlace, float,
+                                         ops::ReluGradFunctor<float>>);
+
+REGISTER_OP(tanh, ops::ActivationOp, ops::TanhOpMaker, tanh_grad,
+            ops::ActivationOpGrad);
+REGISTER_OP_CPU_KERNEL(
+    tanh,
+    ops::ActivationKernel<paddle::platform::CPUPlace, float, ops::TanhFunctor>);
+REGISTER_OP_CPU_KERNEL(
+    tanh_grad, ops::ActivationGradKernel<paddle::platform::CPUPlace, float,
+                                         ops::TanhGradFunctor<float>>);
+
+REGISTER_OP(sqrt, ops::ActivationOp, ops::SqrtOpMaker, sqrt_grad,
+            ops::ActivationOpGrad);
+REGISTER_OP_CPU_KERNEL(
+    sqrt,
+    ops::ActivationKernel<paddle::platform::CPUPlace, float, ops::SqrtFunctor>);
+REGISTER_OP_CPU_KERNEL(
+    sqrt_grad, ops::ActivationGradKernel<paddle::platform::CPUPlace, float,
+                                         ops::SqrtGradFunctor<float>>);
+
+REGISTER_OP(abs, ops::ActivationOp, ops::AbsOpMaker, abs_grad,
+            ops::ActivationOpGrad);
+REGISTER_OP_CPU_KERNEL(
+    abs,
+    ops::ActivationKernel<paddle::platform::CPUPlace, float, ops::AbsFunctor>);
+REGISTER_OP_CPU_KERNEL(abs_grad,
+                       ops::ActivationGradKernel<paddle::platform::CPUPlace,
+                                                 float, ops::AbsGradFunctor>);
+
+REGISTER_OP(reciprocal, ops::ActivationOp, ops::ReciprocalOpMaker,
+            reciprocal_grad, ops::ActivationOpGrad);
+REGISTER_OP_CPU_KERNEL(reciprocal,
+                       ops::ActivationKernel<paddle::platform::CPUPlace, float,
+                                             ops::ReciprocalFunctor<float>>);
+REGISTER_OP_CPU_KERNEL(
+    reciprocal_grad,
+    ops::ActivationGradKernel<paddle::platform::CPUPlace, float,
+                              ops::ReciprocalGradFunctor<float>>);
+
+REGISTER_OP(log, ops::ActivationOp, ops::LogOpMaker, log_grad,
+            ops::ActivationOpGrad);
+REGISTER_OP_CPU_KERNEL(
+    log,
+    ops::ActivationKernel<paddle::platform::CPUPlace, float, ops::LogFunctor>);
+REGISTER_OP_CPU_KERNEL(
+    log_grad, ops::ActivationGradKernel<paddle::platform::CPUPlace, float,
+                                        ops::LogGradFunctor<float>>);
+
+REGISTER_OP(square, ops::ActivationOp, ops::SquareOpMaker, square_grad,
+            ops::ActivationOpGrad);
+REGISTER_OP_CPU_KERNEL(square,
+                       ops::ActivationKernel<paddle::platform::CPUPlace, float,
+                                             ops::SquareFunctor>);
+REGISTER_OP_CPU_KERNEL(
+    square_grad, ops::ActivationGradKernel<paddle::platform::CPUPlace, float,
+                                           ops::SquareGradFunctor<float>>);
+
+REGISTER_OP(brelu, ops::ActivationOp, ops::BReluOpMaker<float>, brelu_grad,
+            ops::ActivationOpGrad);
+REGISTER_OP_CPU_KERNEL(brelu,
+                       ops::BReluKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(brelu_grad,
+                       ops::BReluGradKernel<paddle::platform::CPUPlace, float>);
+
+REGISTER_OP(soft_relu, ops::ActivationOp, ops::SoftReluOpMaker<float>,
+            soft_relu_grad, ops::ActivationOpGrad);
+REGISTER_OP_CPU_KERNEL(soft_relu,
+                       ops::SoftReluKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(
+    soft_relu_grad, ops::SoftReluGradKernel<paddle::platform::CPUPlace, float>);
+
+REGISTER_OP(pow, ops::ActivationOp, ops::PowOpMaker<float>, pow_grad,
+            ops::ActivationOpGrad);
+REGISTER_OP_CPU_KERNEL(pow, ops::PowKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(pow_grad,
+                       ops::PowGradKernel<paddle::platform::CPUPlace, float>);
+
+REGISTER_OP(stanh, ops::ActivationOp, ops::STanhOpMaker<float>, stanh_grad,
+            ops::ActivationOpGrad);
+REGISTER_OP_CPU_KERNEL(stanh,
+                       ops::STanhKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(stanh_grad,
+                       ops::STanhGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/activation_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/activation_op.h"
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_GPU_KERNEL(sigmoid,
+                       ops::ActivationKernel<paddle::platform::GPUPlace, float,
+                                             ops::SigmoidFunctor<float>>);
+REGISTER_OP_GPU_KERNEL(
+    sigmoid_grad, ops::ActivationGradKernel<paddle::platform::GPUPlace, float,
+                                            ops::SigmoidGradFunctor<float>>);
+
+REGISTER_OP_GPU_KERNEL(
+    exp,
+    ops::ActivationKernel<paddle::platform::GPUPlace, float, ops::ExpFunctor>);
+REGISTER_OP_GPU_KERNEL(exp_grad,
+                       ops::ActivationGradKernel<paddle::platform::GPUPlace,
+                                                 float, ops::ExpGradFunctor>);
+REGISTER_OP_GPU_KERNEL(relu,
+                       ops::ActivationKernel<paddle::platform::GPUPlace, float,
+                                             ops::ReluFunctor<float>>);
+REGISTER_OP_GPU_KERNEL(
+    relu_grad, ops::ActivationGradKernel<paddle::platform::GPUPlace, float,
+                                         ops::ReluGradFunctor<float>>);
+
+REGISTER_OP_GPU_KERNEL(
+    tanh,
+    ops::ActivationKernel<paddle::platform::GPUPlace, float, ops::TanhFunctor>);
+REGISTER_OP_GPU_KERNEL(
+    tanh_grad, ops::ActivationGradKernel<paddle::platform::GPUPlace, float,
+                                         ops::TanhGradFunctor<float>>);
+
+REGISTER_OP_GPU_KERNEL(
+    sqrt,
+    ops::ActivationKernel<paddle::platform::GPUPlace, float, ops::SqrtFunctor>);
+REGISTER_OP_GPU_KERNEL(
+    sqrt_grad, ops::ActivationGradKernel<paddle::platform::GPUPlace, float,
+                                         ops::SqrtGradFunctor<float>>);
+
+REGISTER_OP_GPU_KERNEL(
+    abs,
+    ops::ActivationKernel<paddle::platform::GPUPlace, float, ops::AbsFunctor>);
+REGISTER_OP_GPU_KERNEL(abs_grad,
+                       ops::ActivationGradKernel<paddle::platform::GPUPlace,
+                                                 float, ops::AbsGradFunctor>);
+
+REGISTER_OP_GPU_KERNEL(reciprocal,
+                       ops::ActivationKernel<paddle::platform::GPUPlace, float,
+                                             ops::ReciprocalFunctor<float>>);
+REGISTER_OP_GPU_KERNEL(
+    reciprocal_grad,
+    ops::ActivationGradKernel<paddle::platform::GPUPlace, float,
+                              ops::ReciprocalGradFunctor<float>>);
+
+REGISTER_OP_GPU_KERNEL(
+    log,
+    ops::ActivationKernel<paddle::platform::GPUPlace, float, ops::LogFunctor>);
+REGISTER_OP_GPU_KERNEL(
+    log_grad, ops::ActivationGradKernel<paddle::platform::GPUPlace, float,
+                                        ops::LogGradFunctor<float>>);
+
+REGISTER_OP_GPU_KERNEL(square,
+                       ops::ActivationKernel<paddle::platform::GPUPlace, float,
+                                             ops::SquareFunctor>);
+REGISTER_OP_GPU_KERNEL(
+    square_grad, ops::ActivationGradKernel<paddle::platform::GPUPlace, float,
+                                           ops::SquareGradFunctor<float>>);
+
+REGISTER_OP_GPU_KERNEL(brelu,
+                       ops::BReluKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(brelu_grad,
+                       ops::BReluGradKernel<paddle::platform::GPUPlace, float>);
+
+REGISTER_OP_GPU_KERNEL(soft_relu,
+                       ops::SoftReluKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(
+    soft_relu_grad, ops::SoftReluGradKernel<paddle::platform::GPUPlace, float>);
+
+REGISTER_OP_GPU_KERNEL(pow, ops::PowKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(pow_grad,
+                       ops::PowGradKernel<paddle::platform::GPUPlace, float>);
+
+REGISTER_OP_GPU_KERNEL(stanh,
+                       ops::STanhKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(stanh_grad,
+                       ops::STanhGradKernel<paddle::platform::GPUPlace, float>);

paddle/operators/activation_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 "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename Place, typename T, typename Functor>
+class ActivationKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* X = context.Input<framework::Tensor>("X");
+    auto* Y = context.Output<framework::Tensor>("Y");
+    Y->mutable_data<T>(context.GetPlace());
+
+    auto x = framework::EigenVector<T>::Flatten(*X);
+    auto y = framework::EigenVector<T>::Flatten(*Y);
+    auto place = context.GetEigenDevice<Place>();
+    Functor functor;
+    functor(place, x, y);
+  }
+};
+
+template <typename Place, typename T, typename Functor>
+class ActivationGradKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* X = context.Input<framework::Tensor>("X");
+    auto* Y = context.Input<framework::Tensor>("Y");
+    auto* dY = context.Input<framework::Tensor>(framework::GradVarName("Y"));
+    auto* dX = context.Output<framework::Tensor>(framework::GradVarName("X"));
+    dX->mutable_data<T>(context.GetPlace());
+
+    auto dy = framework::EigenVector<T>::Flatten(*dY);
+    auto x = framework::EigenVector<T>::Flatten(*X);
+    auto y = framework::EigenVector<T>::Flatten(*Y);
+    auto dx = framework::EigenVector<T>::Flatten(*dX);
+    auto place = context.GetEigenDevice<Place>();
+    Functor functor;
+    functor(place, x, y, dy, dx);
+  }
+};
+
+// sigmoid(x) = 1 / (1 + exp(-x))
+template <typename T>
+struct SigmoidFunctor {
+  template <typename Device, typename X, typename Y>
+  void operator()(Device d, X x, Y y) {
+    y.device(d) = static_cast<T>(1) / (static_cast<T>(1) + (-x).exp());
+  }
+};
+
+template <typename T>
+struct SigmoidGradFunctor {
+  template <typename Device, typename X, typename Y, typename dY, typename dX>
+  void operator()(Device d, X x, Y y, dY dy, dX dx) {
+    dx.device(d) = dy * y * (static_cast<T>(1) - y);
+  }
+};
+
+// exp(x) = e^x
+struct ExpFunctor {
+  template <typename Device, typename X, typename Y>
+  void operator()(Device d, X x, Y y) {
+    y.device(d) = x.exp();
+  }
+};
+
+struct ExpGradFunctor {
+  template <typename Device, typename X, typename Y, typename dY, typename dX>
+  void operator()(Device d, X x, Y y, dY dy, dX dx) {
+    dx.device(d) = dy * y;
+  }
+};
+
+// relu(x) = max(x, 0)
+template <typename T>
+struct ReluFunctor {
+  template <typename Device, typename X, typename Y>
+  void operator()(Device d, X x, Y y) {
+    y.device(d) = x.cwiseMax(static_cast<T>(0));
+  }
+};
+
+template <typename T>
+struct ReluGradFunctor {
+  template <typename Device, typename X, typename Y, typename dY, typename dX>
+  void operator()(Device d, X x, Y y, dY dy, dX dx) {
+    dx.device(d) = dy * (x > static_cast<T>(0)).template cast<T>();
+  }
+};
+
+// tanh(x) = (exp(x) - exp(-x)) / (exp(x) + exp(-x))
+struct TanhFunctor {
+  template <typename Device, typename X, typename Y>
+  void operator()(Device d, X x, Y y) {
+    y.device(d) = x.tanh();
+  }
+};
+
+template <typename T>
+struct TanhGradFunctor {
+  template <typename Device, typename X, typename Y, typename dY, typename dX>
+  void operator()(Device d, X x, Y y, dY dy, dX dx) {
+    dx.device(d) = dy * (static_cast<T>(1) - y * y);
+  }
+};
+
+// sqrt(x) = x^(1/2)
+struct SqrtFunctor {
+  template <typename Device, typename X, typename Y>
+  void operator()(Device d, X x, Y y) {
+    y.device(d) = x.sqrt();
+  }
+};
+
+template <typename T>
+struct SqrtGradFunctor {
+  template <typename Device, typename X, typename Y, typename dY, typename dX>
+  void operator()(Device d, X x, Y y, dY dy, dX dx) {
+    const Y y_conj = Eigen::numext::conj(y);
+    dx.device(d) = static_cast<T>(0.5) * dy / y_conj;
+  }
+};
+
+// abs(x) = |x|
+struct AbsFunctor {
+  template <typename Device, typename X, typename Y>
+  void operator()(Device d, X x, Y y) {
+    y.device(d) = x.abs();
+  }
+};
+
+struct AbsGradFunctor {
+  template <typename Device, typename X, typename Y, typename dY, typename dX>
+  void operator()(Device d, X x, Y y, dY dy, dX dx) {
+    dx.device(d) = dy * x.sign();
+  }
+};
+
+// reciprocal(x) = 1 / x
+template <typename T>
+struct ReciprocalFunctor {
+  template <typename Device, typename X, typename Y>
+  void operator()(Device d, X x, Y y) {
+    y.device(d) = static_cast<T>(1) / x;
+  }
+};
+
+template <typename T>
+struct ReciprocalGradFunctor {
+  template <typename Device, typename X, typename Y, typename dY, typename dX>
+  void operator()(Device d, X x, Y y, dY dy, dX dx) {
+    dx.device(d) = dy * static_cast<T>(-1) * y * y;
+  }
+};
+
+// log(x) = natural logarithm of x
+struct LogFunctor {
+  template <typename Device, typename X, typename Y>
+  void operator()(Device d, X x, Y y) {
+    y.device(d) = x.log();
+  }
+};
+
+template <typename T>
+struct LogGradFunctor {
+  template <typename Device, typename X, typename Y, typename dY, typename dX>
+  void operator()(Device d, X x, Y y, dY dy, dX dx) {
+    dx.device(d) = dy * (static_cast<T>(1) / x);
+  }
+};
+
+// square(x) = x^2
+struct SquareFunctor {
+  template <typename Device, typename X, typename Y>
+  void operator()(Device d, X x, Y y) {
+    y.device(d) = x.square();
+  }
+};
+
+template <typename T>
+struct SquareGradFunctor {
+  template <typename Device, typename X, typename Y, typename dY, typename dX>
+  void operator()(Device d, X x, Y y, dY dy, dX dx) {
+    dx.device(d) = dy * static_cast<T>(2) * x;
+  }
+};
+
+template <typename Place, typename T, typename AttrType = T>
+class BReluKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* X = context.Input<framework::Tensor>("X");
+    auto* Y = context.Output<framework::Tensor>("Y");
+    auto t_min = static_cast<T>(context.Attr<AttrType>("t_min"));
+    auto t_max = static_cast<T>(context.Attr<AttrType>("t_max"));
+    Y->mutable_data<T>(context.GetPlace());
+
+    auto x = framework::EigenVector<T>::Flatten(*X);
+    auto y = framework::EigenVector<T>::Flatten(*Y);
+    auto place = context.GetEigenDevice<Place>();
+    y.device(place) = x.cwiseMax(t_min).cwiseMin(t_max);
+  }
+};
+
+template <typename Place, typename T, typename AttrType = T>
+class BReluGradKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* X = context.Input<framework::Tensor>("X");
+    auto* dY = context.Input<framework::Tensor>(framework::GradVarName("Y"));
+    auto* dX = context.Output<framework::Tensor>(framework::GradVarName("X"));
+    auto t_min = static_cast<T>(context.Attr<AttrType>("t_min"));
+    auto t_max = static_cast<T>(context.Attr<AttrType>("t_max"));
+    dX->mutable_data<T>(context.GetPlace());
+
+    auto dy = framework::EigenVector<T>::Flatten(*dY);
+    auto x = framework::EigenVector<T>::Flatten(*X);
+    auto dx = framework::EigenVector<T>::Flatten(*dX);
+    auto place = context.GetEigenDevice<Place>();
+
+    dx.device(place) = dy * ((x > t_min) * (x < t_max)).template cast<T>();
+  }
+};
+
+template <typename Place, typename T, typename AttrType = T>
+class SoftReluKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* X = context.Input<framework::Tensor>("X");
+    auto* Y = context.Output<framework::Tensor>("Y");
+    auto threshold = static_cast<T>(context.Attr<AttrType>("threshold"));
+    Y->mutable_data<T>(context.GetPlace());
+
+    auto x = framework::EigenVector<T>::Flatten(*X);
+    auto y = framework::EigenVector<T>::Flatten(*Y);
+    auto place = context.GetEigenDevice<Place>();
+    auto temp = x.cwiseMax(-threshold).cwiseMin(threshold).eval();
+    y.device(place) = (static_cast<T>(1) + temp.exp()).log();
+  }
+};
+
+template <typename Place, typename T, typename AttrType = T>
+class SoftReluGradKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* X = context.Input<framework::Tensor>("X");
+    auto* Y = context.Input<framework::Tensor>("Y");
+    auto* dY = context.Input<framework::Tensor>(framework::GradVarName("Y"));
+    auto* dX = context.Output<framework::Tensor>(framework::GradVarName("X"));
+    auto threshold = static_cast<T>(context.Attr<AttrType>("threshold"));
+    dX->mutable_data<T>(context.GetPlace());
+
+    auto x = framework::EigenVector<T>::Flatten(*X);
+    auto y = framework::EigenVector<T>::Flatten(*Y);
+    auto dy = framework::EigenVector<T>::Flatten(*dY);
+    auto dx = framework::EigenVector<T>::Flatten(*dX);
+    auto place = context.GetEigenDevice<Place>();
+    auto temp = ((x > -threshold) * (x < threshold)).template cast<T>().eval();
+    dx.device(place) = dy * (static_cast<T>(1) - (-y).exp()) * temp;
+  }
+};
+
+template <typename Place, typename T, typename AttrType = T>
+class PowKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* X = context.Input<framework::Tensor>("X");
+    auto* Y = context.Output<framework::Tensor>("Y");
+    auto factor = static_cast<T>(context.Attr<AttrType>("factor"));
+    Y->mutable_data<T>(context.GetPlace());
+
+    auto x = framework::EigenVector<T>::Flatten(*X);
+    auto y = framework::EigenVector<T>::Flatten(*Y);
+    auto place = context.GetEigenDevice<Place>();
+    y.device(place) = x.pow(factor);
+  }
+};
+
+template <typename Place, typename T, typename AttrType = T>
+class PowGradKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* X = context.Input<framework::Tensor>("X");
+    auto* dY = context.Input<framework::Tensor>(framework::GradVarName("Y"));
+    auto* dX = context.Output<framework::Tensor>(framework::GradVarName("X"));
+    auto factor = static_cast<T>(context.Attr<AttrType>("factor"));
+    dX->mutable_data<T>(context.GetPlace());
+
+    auto dy = framework::EigenVector<T>::Flatten(*dY);
+    auto x = framework::EigenVector<T>::Flatten(*X);
+    auto dx = framework::EigenVector<T>::Flatten(*dX);
+    auto place = context.GetEigenDevice<Place>();
+
+    dx.device(place) = dy * factor * x.pow(factor - static_cast<T>(1));
+  }
+};
+
+template <typename Place, typename T, typename AttrType = T>
+class STanhKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* X = context.Input<framework::Tensor>("X");
+    auto* Y = context.Output<framework::Tensor>("Y");
+    auto scale_a = static_cast<T>(context.Attr<AttrType>("scale_a"));
+    auto scale_b = static_cast<T>(context.Attr<AttrType>("scale_b"));
+    Y->mutable_data<T>(context.GetPlace());
+
+    auto x = framework::EigenVector<T>::Flatten(*X);
+    auto y = framework::EigenVector<T>::Flatten(*Y);
+    auto place = context.GetEigenDevice<Place>();
+    y.device(place) = scale_b * (scale_a * x).tanh();
+  }
+};
+
+template <typename Place, typename T, typename AttrType = T>
+class STanhGradKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* X = context.Input<framework::Tensor>("X");
+    auto* dY = context.Input<framework::Tensor>(framework::GradVarName("Y"));
+    auto* dX = context.Output<framework::Tensor>(framework::GradVarName("X"));
+    auto scale_a = static_cast<T>(context.Attr<AttrType>("scale_a"));
+    auto scale_b = static_cast<T>(context.Attr<AttrType>("scale_b"));
+    dX->mutable_data<T>(context.GetPlace());
+
+    auto dy = framework::EigenVector<T>::Flatten(*dY);
+    auto x = framework::EigenVector<T>::Flatten(*X);
+    auto dx = framework::EigenVector<T>::Flatten(*dX);
+    auto place = context.GetEigenDevice<Place>();
+
+    auto temp = (scale_a * x).tanh() * (scale_a * x).tanh();
+    dx.device(place) = dy * scale_a * scale_b * (static_cast<T>(1) - temp);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/operators/crop_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/crop_op.h"
+#include <boost/lexical_cast.hpp>
+
+namespace paddle {
+namespace operators {
+
+using framework::Tensor;
+using framework::LoDTensor;
+
+class CropOp : 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) of CropOp should not be null.");
+    PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
+                            "Output(Out) of CropOp should not be null.");
+    auto x_dim = ctx.Input<LoDTensor>("X")->dims();
+    auto *y = ctx.Input<LoDTensor>("Y");
+    auto *out = ctx.Output<LoDTensor>("Out");
+    if (y == nullptr) {
+      auto shape = Attr<std::vector<int>>("shape");
+      PADDLE_ENFORCE_EQ(
+          int64_t(shape.size()), x_dim.size(),
+          "Shape size should be equal to dimention size of input tensor.");
+      std::vector<int64_t> tensor_shape(shape.size());
+      for (size_t i = 0; i < shape.size(); ++i) {
+        tensor_shape[i] = static_cast<int64_t>(shape[i]);
+      }
+      out->Resize(framework::make_ddim(tensor_shape));
+    } else {
+      PADDLE_ENFORCE_EQ(framework::arity(x_dim), framework::arity(y->dims()),
+                        "Tensor rank of both CropOp's "
+                        "inputs must be same.");
+      out->Resize(y->dims());
+    }
+  }
+};
+
+class CropOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  CropOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X",
+             "The input of pad op. "
+             "The input should be a k-D tensor(k > 0 and k < 7)");
+    AddInput("Y",
+             "The input used as reference for cropping"
+             " with the same dimension as X. ");
+    AddOutput("Out",
+              "The output of crop op "
+              "with the same dimension as X.");
+    AddAttr<std::vector<int>>("offsets",
+                              "A list<int> describing offsets to be cropped."
+                              "The size of offsets list should be as same as "
+                              "dimension size of  input X.");
+    AddAttr<std::vector<int>>("shape",
+                              "A list<int> describing the shape of output."
+                              "The size of shape list should be as same as "
+                              "dimension size of  input X.")
+        .SetDefault(std::vector<int>());
+    AddComment(R"DOC(
+Crop Operator.
+Crop input into output, as specified by offsets and shape.
+
+There are two ways to set shape: 
+1. referenc input: crop input X as shape as reference input.
+                    The dimension of reference input should 
+                    be as same as input X.
+2. shape list: crop input X by shape described by a list<int>.
+               The size of shape list should be as same as 
+               dimension size of  input X.
+
+The input should be a k-D tensor(k > 0 and k < 7). As an example:
+
+Given:
+
+    X = [[0, 1, 2, 0, 0]
+         [0, 3, 4, 0, 0]
+         [0, 0, 0, 0, 0]]
+
+and 
+
+    offsets = [0, 1]
+
+and
+ 
+    shape = [2, 2]
+
+then we get 
+
+    Out = [[1, 2],
+           [3, 4]]
+
+)DOC");
+  }
+};
+
+class CropOpGrad : 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(framework::GradVarName("Out")),
+                            "Input(Out@GRAD) should not be null");
+    auto x_dims = ctx.Input<LoDTensor>("X")->dims();
+    auto *x_grad = ctx.Output<LoDTensor>(framework::GradVarName("X"));
+    if (x_grad != nullptr) {
+      x_grad->Resize(x_dims);
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP(crop, ops::CropOp, ops::CropOpMaker, crop_grad, ops::CropOpGrad);
+REGISTER_OP_CPU_KERNEL(crop, ops::CropKernel<float>);
+REGISTER_OP_CPU_KERNEL(crop_grad,
+                       ops::CropGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/sigmoid_op.cu → paddle/operators/crop_op.cu

@@ -13,11 +13,9 @@
    limitations under the License. */
 
 #define EIGEN_USE_GPU
-#include "paddle/operators/sigmoid_op.h"
+#include "paddle/operators/crop_op.h"
 
 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>);
+REGISTER_OP_GPU_KERNEL(crop, ops::CropKernel<float>);
+REGISTER_OP_GPU_KERNEL(crop_grad,
+                       ops::CropGradKernel<paddle::platform::GPUPlace, float>);

paddle/operators/crop_op.h

+/* Copyright (c) 2016 CropdleCropdle 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/strided_memcpy.h"
+
+namespace paddle {
+namespace operators {  // Internal
+
+template <typename T, size_t D, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
+using framework::Tensor;
+
+template <typename T>
+class CropKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* x = context.Input<Tensor>("X");
+    auto* out = context.Output<Tensor>("Out");
+    const T* x_data = x->data<T>();
+    T* out_data = out->mutable_data<T>(context.GetPlace());
+    auto x_stride = framework::stride(x->dims());
+    auto out_stride = framework::stride(out->dims());
+    auto offsets = context.Attr<std::vector<int>>("offsets");
+    PADDLE_ENFORCE_EQ(
+        x->dims().size(), offsets.size(),
+        "Offsets size should be equal to dimension size of input tensor.");
+    int64_t offset = 0;
+    for (int i = 0; i < offsets.size(); ++i) {
+      offset += (x_stride[i] * offsets[i]);
+    }
+    StridedMemcpy<T>(context.device_context(), x_data + offset, x_stride,
+                     out->dims(), out_stride, out_data);
+  }
+};
+
+template <typename Place, typename T, size_t D>
+void CropGradFunction(const framework::ExecutionContext& context) {
+  auto* d_x = context.Output<Tensor>(framework::GradVarName("X"));
+  if (d_x != nullptr) {
+    auto* d_out = context.Input<Tensor>(framework::GradVarName("Out"));
+    d_x->mutable_data<T>(context.GetPlace());
+    auto offsets = context.Attr<std::vector<int>>("offsets");
+    Eigen::array<std::pair<int, int>, D> paddings;
+    for (int i = 0; i < D; ++i) {
+      paddings[i].first = offsets[i];
+      paddings[i].second = d_x->dims()[i] - d_out->dims()[i] - offsets[i];
+    }
+    auto d_x_tensor = EigenTensor<T, D>::From(*d_x);
+    auto d_out_tensor = EigenTensor<T, D>::From(*d_out);
+    d_x_tensor.device(context.GetEigenDevice<Place>()) =
+        d_out_tensor.pad(paddings, 0);
+  }
+}
+
+template <typename Place, typename T>
+class CropGradKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    size_t rank =
+        context.Input<Tensor>(framework::GradVarName("Out"))->dims().size();
+    switch (rank) {
+      case 1:
+        CropGradFunction<Place, T, 1>(context);
+        break;
+      case 2:
+        CropGradFunction<Place, T, 2>(context);
+        break;
+      case 3:
+        CropGradFunction<Place, T, 3>(context);
+        break;
+      case 4:
+        CropGradFunction<Place, T, 4>(context);
+        break;
+      case 5:
+        CropGradFunction<Place, T, 5>(context);
+        break;
+      case 6:
+        CropGradFunction<Place, T, 6>(context);
+        break;
+      default:
+        PADDLE_THROW(
+            "CropOp only support tensors with no more than 6 dimensions.");
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/operators/gemm_conv2d_op.h

@@ -75,9 +75,6 @@ class GemmConv2DKernel : public framework::OpKernel {
     framework::DDim output_matrix_shape = {output_channels,
                                            output_height * output_width};
 
-    auto* device_context =
-        const_cast<platform::DeviceContext*>(context.device_context_);
-
     // convolution operator: im2col + gemm
     int in_step = input_channels / groups;
     int out_step = output_channels / groups;
@@ -87,14 +84,14 @@ class GemmConv2DKernel : public framework::OpKernel {
       for (int g = 0; g < groups; g++) {
         // im2col
         Tensor in_slice = in_batch.Slice<T>(g * in_step, (g + 1) * in_step);
-        im2col(in_slice, col, strides[0], strides[1], paddings[0], paddings[1],
-               device_context);
+        im2col(context.device_context(), in_slice, col, strides[0], strides[1],
+               paddings[0], paddings[1]);
 
         // gemm
         Tensor out_slice = out_batch.Slice<T>(g * out_step, (g + 1) * out_step);
         Tensor filter_slice = filter.Slice<T>(g * out_step, (g + 1) * out_step);
-        math::matmul<Place, T>(filter_slice, false, col_matrix, false, T(1.0),
-                               &out_slice, T(0.0), device_context);
+        math::matmul<Place, T>(context.device_context(), filter_slice, false,
+                               col_matrix, false, T(1.0), &out_slice, T(0.0));
       }
     }
   }
@@ -160,9 +157,6 @@ class GemmConvGrad2DKernel : public framework::OpKernel {
                                            filter.numel() / filter.dims()[0]};
     filter.Resize(filter_matrix_shape);
 
-    auto* device_context =
-        const_cast<platform::DeviceContext*>(context.device_context_);
-
     // convolution backward input operator:  gemm + col2im
     // convolution backward weight operator: im2col + gemm
     int in_step = input_channels / groups;
@@ -184,14 +178,15 @@ class GemmConvGrad2DKernel : public framework::OpKernel {
               out_grad_batch.Slice<T>(g * out_step, (g + 1) * out_step);
           Tensor filter_slice =
               filter.Slice<T>(g * out_step, (g + 1) * out_step);
-          math::matmul<Place, T>(filter_slice, true, out_grad_slice, false,
-                                 T(1.0), &col_matrix, T(0.0), device_context);
+          math::matmul<Place, T>(context.device_context(), filter_slice, true,
+                                 out_grad_slice, false, T(1.0), &col_matrix,
+                                 T(0.0));
 
           // col2im
           Tensor in_grad_slice =
               in_grad_batch.Slice<T>(g * in_step, (g + 1) * in_step);
-          col2im(in_grad_slice, col, strides[0], strides[1], paddings[0],
-                 paddings[1], device_context);
+          col2im(context.device_context(), in_grad_slice, col, strides[0],
+                 strides[1], paddings[0], paddings[1]);
         }
       }
     }
@@ -212,15 +207,15 @@ class GemmConvGrad2DKernel : public framework::OpKernel {
           Tensor out_grad_slice =
               out_grad_batch.Slice<T>(g * out_step, (g + 1) * out_step);
           Tensor in_slice = in_batch.Slice<T>(g * in_step, (g + 1) * in_step);
-          im2col(in_slice, col, strides[0], strides[1], paddings[0],
-                 paddings[1], device_context);
+          im2col(context.device_context(), in_slice, col, strides[0],
+                 strides[1], paddings[0], paddings[1]);
 
           // gemm
           Tensor filter_grad_slice =
               filter_grad_.Slice<T>(g * out_step, (g + 1) * out_step);
-          math::matmul<Place, T>(out_grad_slice, false, col_matrix, true,
-                                 T(1.0), &filter_grad_slice, T(1.0),
-                                 device_context);
+          math::matmul<Place, T>(context.device_context(), out_grad_slice,
+                                 false, col_matrix, true, T(1.0),
+                                 &filter_grad_slice, T(1.0));
         }
       }
     }

paddle/operators/math/im2col.cc

@@ -27,9 +27,10 @@ template <class T>
 class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO,
                     platform::CPUPlace, T> {
  public:
-  void operator()(const framework::Tensor& im, framework::Tensor& col,
+  void operator()(const platform::DeviceContext& context,
+                  const framework::Tensor& im, framework::Tensor& col,
                   int stride_height, int stride_width, int padding_height,
-                  int padding_width, platform::DeviceContext* context) {
+                  int padding_width) {
     PADDLE_ENFORCE(im.dims().size() == 3);
     PADDLE_ENFORCE(col.dims().size() == 5);
 
@@ -79,9 +80,9 @@ template <class T>
 class Col2ImFunctor<paddle::operators::math::ColFormat::kCFO,
                     platform::CPUPlace, T> {
  public:
-  void operator()(framework::Tensor& im, const framework::Tensor& col,
-                  int stride_height, int stride_width, int padding_height,
-                  int padding_width, platform::DeviceContext* context) {
+  void operator()(const platform::DeviceContext& context, framework::Tensor& im,
+                  const framework::Tensor& col, int stride_height,
+                  int stride_width, int padding_height, int padding_width) {
     PADDLE_ENFORCE(im.dims().size() == 3);
     PADDLE_ENFORCE(col.dims().size() == 5);
     int input_channels = im.dims()[0];
@@ -137,9 +138,10 @@ template <class T>
 class Im2ColFunctor<paddle::operators::math::ColFormat::kOCF,
                     platform::CPUPlace, T> {
  public:
-  void operator()(const framework::Tensor& im, framework::Tensor& col,
+  void operator()(const platform::DeviceContext& context,
+                  const framework::Tensor& im, framework::Tensor& col,
                   int stride_height, int stride_width, int padding_height,
-                  int padding_width, platform::DeviceContext* context) {
+                  int padding_width) {
     PADDLE_ENFORCE(im.dims().size() == 3);
     PADDLE_ENFORCE(col.dims().size() == 5);
     int input_channels = im.dims()[0];
@@ -197,9 +199,9 @@ template <class T>
 class Col2ImFunctor<paddle::operators::math::ColFormat::kOCF,
                     platform::CPUPlace, T> {
  public:
-  void operator()(framework::Tensor& im, const framework::Tensor& col,
-                  int stride_height, int stride_width, int padding_height,
-                  int padding_width, platform::DeviceContext* context) {
+  void operator()(const platform::DeviceContext& context, framework::Tensor& im,
+                  const framework::Tensor& col, int stride_height,
+                  int stride_width, int padding_height, int padding_width) {
     PADDLE_ENFORCE(im.dims().size() == 3);
     PADDLE_ENFORCE(col.dims().size() == 5);
     int input_channels = im.dims()[0];

paddle/operators/math/im2col.cu

@@ -64,9 +64,10 @@ template <class T>
 class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO,
                     platform::GPUPlace, T> {
  public:
-  void operator()(const framework::Tensor& im, framework::Tensor& col,
+  void operator()(const platform::DeviceContext& context,
+                  const framework::Tensor& im, framework::Tensor& col,
                   int stride_height, int stride_width, int padding_height,
-                  int padding_width, platform::DeviceContext* context) {
+                  int padding_width) {
     PADDLE_ENFORCE(im.dims().size() == 3);
     PADDLE_ENFORCE(col.dims().size() == 5);
 
@@ -84,9 +85,9 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO,
     int block_y = (blocks + 512 - 1) / 512;
     dim3 threads(1024, 1);
     dim3 grid(block_x, block_y);
-    im2col<T><<<
-        grid, threads, 0,
-        reinterpret_cast<platform::CUDADeviceContext*>(context)->stream()>>>(
+    im2col<T><<<grid, threads, 0,
+                reinterpret_cast<const platform::CUDADeviceContext&>(context)
+                    .stream()>>>(
         im.data<T>(), num_outputs, input_height, input_width, filter_height,
         filter_width, stride_height, stride_width, padding_height,
         padding_width, output_height, output_width, col.data<T>());
@@ -149,9 +150,9 @@ template <class T>
 class Col2ImFunctor<paddle::operators::math::ColFormat::kCFO,
                     platform::GPUPlace, T> {
  public:
-  void operator()(framework::Tensor& im, const framework::Tensor& col,
-                  int stride_height, int stride_width, int padding_height,
-                  int padding_width, platform::DeviceContext* context) {
+  void operator()(const platform::DeviceContext& context, framework::Tensor& im,
+                  const framework::Tensor& col, int stride_height,
+                  int stride_width, int padding_height, int padding_width) {
     PADDLE_ENFORCE(im.dims().size() == 3);
     PADDLE_ENFORCE(col.dims().size() == 5);
 
@@ -174,9 +175,9 @@ class Col2ImFunctor<paddle::operators::math::ColFormat::kCFO,
 
     // To avoid involving atomic operations, we will launch one kernel per
     // bottom dimension, and then in the kernel add up the top dimensions.
-    col2im<T><<<
-        grid, threads, 0,
-        reinterpret_cast<platform::CUDADeviceContext*>(context)->stream()>>>(
+    col2im<T><<<grid, threads, 0,
+                reinterpret_cast<const platform::CUDADeviceContext&>(context)
+                    .stream()>>>(
         num_kernels, col.data<T>(), input_height + 2 * padding_height,
         input_width + 2 * padding_width, input_channels, filter_height,
         filter_width, stride_height, stride_width, padding_height,
@@ -235,9 +236,10 @@ template <class T>
 class Im2ColFunctor<paddle::operators::math::ColFormat::kOCF,
                     platform::GPUPlace, T> {
  public:
-  void operator()(const framework::Tensor& im, framework::Tensor& col,
+  void operator()(const platform::DeviceContext& context,
+                  const framework::Tensor& im, framework::Tensor& col,
                   int stride_height, int stride_width, int padding_height,
-                  int padding_width, platform::DeviceContext* context) {
+                  int padding_width) {
     PADDLE_ENFORCE(im.dims().size() == 3);
     PADDLE_ENFORCE(col.dims().size() == 5);
     int input_channels = im.dims()[0];
@@ -268,9 +270,9 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kOCF,
     dim3 threads(block_dim_x, block_dim_y,
                  std::min(block_dim_z, input_channels));
     dim3 grid(output_width, output_height);
-    im2colOCF<T><<<
-        grid, threads, 0,
-        reinterpret_cast<platform::CUDADeviceContext*>(context)->stream()>>>(
+    im2colOCF<T><<<grid, threads, 0,
+                   reinterpret_cast<const platform::CUDADeviceContext&>(context)
+                       .stream()>>>(
         im.data<T>(), col.data<T>(), input_channels, input_height, input_width,
         filter_height, filter_width, stride_height, stride_width,
         padding_height, padding_width, output_height, output_width);
@@ -318,9 +320,9 @@ template <class T>
 class Col2ImFunctor<paddle::operators::math::ColFormat::kOCF,
                     platform::GPUPlace, T> {
  public:
-  void operator()(framework::Tensor& im, const framework::Tensor& col,
-                  int stride_height, int stride_width, int padding_height,
-                  int padding_width, platform::DeviceContext* context) {
+  void operator()(const platform::DeviceContext& context, framework::Tensor& im,
+                  const framework::Tensor& col, int stride_height,
+                  int stride_width, int padding_height, int padding_width) {
     PADDLE_ENFORCE(im.dims().size() == 3);
     PADDLE_ENFORCE(col.dims().size() == 5);
     int input_channels = im.dims()[0];
@@ -351,9 +353,9 @@ class Col2ImFunctor<paddle::operators::math::ColFormat::kOCF,
     dim3 threads(block_dim_x, block_dim_y,
                  std::min(block_dim_z, input_channels));
     dim3 grid(output_width, output_height);
-    col2imOCF<T><<<
-        grid, threads, 0,
-        reinterpret_cast<platform::CUDADeviceContext*>(context)->stream()>>>(
+    col2imOCF<T><<<grid, threads, 0,
+                   reinterpret_cast<const platform::CUDADeviceContext&>(context)
+                       .stream()>>>(
         im.data<T>(), col.data<T>(), input_channels, input_height, input_width,
         filter_height, filter_width, stride_height, stride_width,
         padding_height, padding_width, output_height, output_width);

paddle/operators/math/im2col.h

@@ -72,17 +72,18 @@ enum class ColFormat { kCFO = 0, kOCF = 1 };
 template <ColFormat Format, typename Place, typename T>
 class Im2ColFunctor {
  public:
-  void operator()(const framework::Tensor& im, framework::Tensor& col,
+  void operator()(const platform::DeviceContext& context,
+                  const framework::Tensor& im, framework::Tensor& col,
                   int stride_height, int stride_width, int padding_height,
-                  int padding_width, platform::DeviceContext* context);
+                  int padding_width);
 };
 
 template <ColFormat Format, typename Place, typename T>
 class Col2ImFunctor {
  public:
-  void operator()(framework::Tensor& im, const framework::Tensor& col,
-                  int stride_height, int stride_width, int padding_height,
-                  int padding_width, platform::DeviceContext* context);
+  void operator()(const platform::DeviceContext& context, framework::Tensor& im,
+                  const framework::Tensor& col, int stride_height,
+                  int stride_width, int padding_height, int padding_width);
 };
 
 }  // namespace math

paddle/operators/math/im2col_test.cc

@@ -78,8 +78,8 @@ void testIm2col() {
     PADDLE_THROW("no GPU support");
 #endif  // PADDLE_ONLY_CPU
   }
-  im2col(input, output_cfo, stride, stride, padding, padding, context);
-  im2col_ocf(input, output_ocf, stride, stride, padding, padding, context);
+  im2col(*context, input, output_cfo, stride, stride, padding, padding);
+  im2col_ocf(*context, input, output_ocf, stride, stride, padding, padding);
 
   float* out_cfo_ptr;
   if (paddle::platform::is_cpu_place(*place)) {

paddle/operators/rank_loss_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/rank_loss_op.h"
+
+namespace paddle {
+namespace operators {
+
+class RankLossOp : public framework::OperatorWithKernel {
+ public:
+  RankLossOp(const std::string &type, const framework::VariableNameMap &inputs,
+             const framework::VariableNameMap &outputs,
+             const framework::AttributeMap &attrs)
+      : OperatorWithKernel(type, inputs, outputs, attrs) {}
+
+ protected:
+  void InferShape(const framework::InferShapeContext &ctx) const override {
+    // input check
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Label"),
+                            "Input(Label) shouldn't be null");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Left"),
+                            "Input(Left) shouldn't be null");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Right"),
+                            "Input(Right) shouldn't be null");
+    auto label_dims = ctx.Input<framework::Tensor>("Label")->dims();
+    auto left_dims = ctx.Input<framework::Tensor>("Left")->dims();
+    auto right_dims = ctx.Input<framework::Tensor>("Right")->dims();
+    PADDLE_ENFORCE((label_dims == left_dims) && (left_dims == right_dims),
+                   "All inputs must have the same size");
+    PADDLE_ENFORCE((label_dims.size() == 2) && (label_dims[1] == 1),
+                   "All inputs must be row vector with size batch_size x 1.");
+    ctx.Output<framework::LoDTensor>("Out")->Resize(label_dims);
+  }
+};
+
+class RankLossOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  RankLossOpMaker(framework::OpProto *proto,
+                  framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("Label",
+             "The label indicating A ranked higher than B or not, row vector.");
+    AddInput("Left", "The output of RankNet for doc A, vector.");
+    AddInput("Right", "The output of RankNet for doc B, vetor");
+    AddOutput("Out", "The output loss of RankLoss operator, vector.");
+    AddComment(R"DOC(RankLoss operator
+
+Rank loss operator for RankNet[1]. RankNet is a pairwise ranking model with
+one training sample consisting of a pair of doc A and B, and the label P
+indicating that A is ranked higher than B or not:
+
+P = {0, 1} or {0, 0.5, 1}, where 0.5 means no information about the rank of
+the input pair.
+
+The RankLoss operator contains three inputs: Left (o_i), Right (o_j) and Label
+(P_{i,j}), which represent the output of RankNet for two docs and the label
+respectively, and yields the rank loss C_{i,j} by following the expression
+
+\f[
+  C_{i,j} = -\tilde{P_{ij}} * o_{i,j} + log(1 + e^{o_{i,j}}) \\
+  o_{i,j} =  o_i - o_j  \\
+  \tilde{P_{i,j}} = \left \{0, 0.5, 1 \right \} \ or \ \left \{0, 1 \right \}
+\f]
+
+The operator can take inputs of one sample or in batch.
+
+[1]. Chris Burges, Tal Shaked, Erin Renshaw, et al. Learning to
+     Rank using Gradient Descent.
+     http://icml.cc/2015/wp-content/uploads/2015/06/icml_ranking.pdf
+)DOC");
+  }
+};
+
+class RankLossGradOp : public framework::OperatorWithKernel {
+ public:
+  RankLossGradOp(const std::string &type,
+                 const framework::VariableNameMap &inputs,
+                 const framework::VariableNameMap &outputs,
+                 const framework::AttributeMap &attrs)
+      : OperatorWithKernel(type, inputs, outputs, attrs) {}
+
+ protected:
+  void InferShape(const framework::InferShapeContext &ctx) const override {
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Label"),
+                            "Input(Label) shouldn't be null.");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Left"),
+                            "Input(Left) shouldn't be null.");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Right"),
+                            "Input(Right) shouldn't be null.");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
+                            "Input(Out@GRAD) shouldn't be null.");
+    auto dims = ctx.Input<framework::Tensor>("Left")->dims();
+    auto *left_grad =
+        ctx.Output<framework::LoDTensor>(framework::GradVarName("Left"));
+    auto *right_grad =
+        ctx.Output<framework::LoDTensor>(framework::GradVarName("Right"));
+    if (left_grad) {
+      left_grad->Resize(dims);
+    }
+    if (right_grad) {
+      right_grad->Resize(dims);
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+namespace ops = paddle::operators;
+
+REGISTER_OP(rank_loss, ops::RankLossOp, ops::RankLossOpMaker, rank_loss_grad,
+            ops::RankLossGradOp);
+REGISTER_OP_CPU_KERNEL(rank_loss,
+                       ops::RankLossKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(
+    rank_loss_grad, ops::RankLossGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/rank_loss_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. */
+
+#include "paddle/operators/rank_loss_op.h"
+
+REGISTER_OP_GPU_KERNEL(
+    rank_loss,
+    paddle::operators::RankLossKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(
+    rank_loss_grad,
+    paddle::operators::RankLossGradKernel<paddle::platform::GPUPlace, float>);

paddle/operators/rank_loss_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 "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename Place, typename T>
+class RankLossKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const {
+    auto* out_t = ctx.Output<framework::LoDTensor>("Out");
+    auto* label_t = ctx.Input<framework::Tensor>("Label");
+    auto* left_t = ctx.Input<framework::Tensor>("Left");
+    auto* right_t = ctx.Input<framework::Tensor>("Right");
+    out_t->mutable_data<T>(ctx.GetPlace());
+
+    auto out = framework::EigenVector<T>::Flatten(*out_t);
+    auto label = framework::EigenVector<T>::Flatten(*label_t);
+    auto left = framework::EigenVector<T>::Flatten(*left_t);
+    auto right = framework::EigenVector<T>::Flatten(*right_t);
+
+    auto& dev = ctx.GetEigenDevice<Place>();
+    out.device(dev) =
+        (1. + (left - right).exp()).log() - label * (left - right);
+  }
+};
+
+template <typename Place, typename T>
+class RankLossGradKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const {
+    auto* d_left_t =
+        ctx.Output<framework::LoDTensor>(framework::GradVarName("Left"));
+    auto* d_right_t =
+        ctx.Output<framework::LoDTensor>(framework::GradVarName("Right"));
+
+    auto* d_out_t = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
+    auto* label_t = ctx.Input<framework::Tensor>("Label");
+    auto* left_t = ctx.Input<framework::Tensor>("Left");
+    auto* right_t = ctx.Input<framework::Tensor>("Right");
+
+    auto& dev = ctx.GetEigenDevice<Place>();
+    auto d_out = framework::EigenVector<T>::Flatten(*d_out_t);
+    auto label = framework::EigenVector<T>::Flatten(*label_t);
+    auto left = framework::EigenVector<T>::Flatten(*left_t);
+    auto right = framework::EigenVector<T>::Flatten(*right_t);
+
+    // compute d_left
+    if (d_left_t) {
+      d_left_t->mutable_data<T>(ctx.GetPlace());
+      auto d_left = framework::EigenVector<T>::Flatten(*d_left_t);
+      d_left.device(dev) = d_out * (1. / (1. + (right - left).exp()) - label);
+    }
+    // compute d_right
+    if (d_right_t) {
+      d_right_t->mutable_data<T>(ctx.GetPlace());
+      auto d_right = framework::EigenVector<T>::Flatten(*d_right_t);
+      d_right.device(dev) =
+          -d_out * (1.0 / (1. + (right - left).exp()) - label);
+    }
+  }
+};
+}  // namespace operators
+}  // namespace paddle

paddle/operators/sigmoid_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/sigmoid_op.h"
-
-namespace paddle {
-namespace operators {
-
-class SigmoidOp : 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) of SigmoidOp should not be null.");
-    PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Y"),
-                            "Output(Y) of SigmoidOp should not be null.");
-
-    ctx.Output<framework::LoDTensor>("Y")->Resize(
-        ctx.Input<Tensor>("X")->dims());
-  }
-};
-
-class SigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
- public:
-  SigmoidOpMaker(framework::OpProto *proto,
-                 framework::OpAttrChecker *op_checker)
-      : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("X", "sigmoid input");
-    AddOutput("Y", "sigmoid output");
-    AddComment("Sigmoid function");
-  }
-};
-
-class SigmoidOpGrad : public framework::OperatorWithKernel {
- public:
-  using framework::OperatorWithKernel::OperatorWithKernel;
-
- protected:
-  void InferShape(const framework::InferShapeContext &ctx) const override {
-    ctx.Output<framework::LoDTensor>(framework::GradVarName("X"))
-        ->Resize(ctx.Input<Tensor>("Y")->dims());
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle
-
-namespace ops = paddle::operators;
-REGISTER_OP(sigmoid, ops::SigmoidOp, ops::SigmoidOpMaker, sigmoid_grad,
-            ops::SigmoidOpGrad);
-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.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"
-
-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 framework::OpKernel {
- public:
-  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());
-
-    // The clipping is used in Paddle's raw implenmention
-    auto X = EigenVector<T>::Flatten(*input);
-    auto Y = EigenVector<T>::Flatten(*output);
-    auto place = context.GetEigenDevice<Place>();
-
-    Y.device(place) = 1. / (1. + (-X).exp());
-  }
-};
-
-template <typename Place, typename T>
-class SigmoidGradKernel : public framework::OpKernel {
- public:
-  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"));
-
-    dX_t->mutable_data<T>(context.GetPlace());
-
-    auto dX = EigenVector<T>::Flatten(*dX_t);
-    auto Y = EigenVector<T>::Flatten(*Y_t);
-    auto dY = EigenVector<T>::Flatten(*dY_t);
-    dX.device(context.GetEigenDevice<Place>()) = dY * Y * (1. - Y);
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle

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

+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestExp(OpTest):
+    def setUp(self):
+        self.op_type = "exp"
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [11, 17]).astype("float32")
+        }
+        self.outputs = {'Y': np.exp(self.inputs['X'])}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Y', max_relative_error=0.007)
+
+
+class TestSigmoid(OpTest):
+    def setUp(self):
+        self.op_type = "sigmoid"
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [11, 17]).astype("float32")
+        }
+        self.outputs = {'Y': 1 / (1 + np.exp(-self.inputs['X']))}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Y', max_relative_error=0.008)
+
+
+class TestTanh(OpTest):
+    def setUp(self):
+        self.op_type = "tanh"
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [11, 17]).astype("float32")
+        }
+        self.outputs = {'Y': np.tanh(self.inputs['X'])}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Y', max_relative_error=0.007)
+
+
+class TestSqrt(OpTest):
+    def setUp(self):
+        self.op_type = "sqrt"
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [11, 17]).astype("float32")
+        }
+        self.outputs = {'Y': np.sqrt(self.inputs['X'])}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Y', max_relative_error=0.007)
+
+
+class TestAbs(OpTest):
+    def setUp(self):
+        self.op_type = "abs"
+        x = np.random.uniform(-1, 1, [4, 4]).astype("float32")
+        # Because we set delta = 0.005 in caculating numeric gradient,
+        # if x is too small, such as 0.002, x_neg will be -0.003
+        # x_pos will be 0.007, so the numeric gradient is unaccurate.
+        # we should avoid this
+        x[np.abs(x) < 0.005] = 0.02
+        self.inputs = {'X': x}
+        self.outputs = {'Y': np.abs(self.inputs['X'])}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Y', max_relative_error=0.007)
+
+
+class TestRelu(OpTest):
+    def setUp(self):
+        self.op_type = "relu"
+        x = np.random.uniform(-1, 1, [11, 17]).astype("float32")
+        # The same reason with TestAbs
+        x[np.abs(x) < 0.005] = 0.02
+        self.inputs = {'X': x}
+        self.outputs = {'Y': np.maximum(self.inputs['X'], 0)}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Y', max_relative_error=0.007)
+
+
+class TestBRelu(OpTest):
+    def setUp(self):
+        self.op_type = "brelu"
+        x = np.random.uniform(-1, 1, [4, 4]).astype("float32")
+        t_min = 1
+        t_max = 4
+        # The same with TestAbs
+        x[np.abs(x - t_min) < 0.005] = t_min + 0.02
+        x[np.abs(x - t_max) < 0.005] = t_max + 0.02
+
+        self.inputs = {'X': x}
+        self.attrs = {'t_min': t_min, 't_max': t_max}
+        t = np.copy(x)
+        t[t < t_min] = t_min
+        t[t > t_max] = t_max
+        self.outputs = {'Y': t}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Y', max_relative_error=0.02)
+
+
+class TestSoftRelu(OpTest):
+    def setUp(self):
+        self.op_type = "soft_relu"
+        x = np.random.uniform(-3, 3, [4, 4]).astype("float32")
+        threshold = 2
+        # The same reason with TestAbs
+        x[np.abs(x - threshold) < 0.005] = threshold + 0.02
+        x[np.abs(x + threshold) < 0.005] = -threshold + 0.02
+        self.inputs = {'X': x}
+        self.attrs = {'threshold': threshold}
+        t = np.copy(x)
+        t[t < -threshold] = -threshold
+        t[t > threshold] = threshold
+        self.outputs = {'Y': np.log((np.exp(t) + 1))}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Y', max_relative_error=0.02)
+
+
+class TestReciprocal(OpTest):
+    def setUp(self):
+        self.op_type = "reciprocal"
+        self.inputs = {'X': np.random.uniform(1, 2, [11, 17]).astype("float32")}
+        self.outputs = {'Y': np.reciprocal(self.inputs['X'])}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Y', max_relative_error=0.01)
+
+
+class TestLog(OpTest):
+    def setUp(self):
+        self.op_type = "log"
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [11, 17]).astype("float32")
+        }
+        self.outputs = {'Y': np.log(self.inputs['X'])}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Y', max_relative_error=0.007)
+
+
+class TestSquare(OpTest):
+    def setUp(self):
+        self.op_type = "square"
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [11, 17]).astype("float32")
+        }
+        self.outputs = {'Y': np.square(self.inputs['X'])}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Y', max_relative_error=0.007)
+
+
+class TestPow(OpTest):
+    def setUp(self):
+        self.op_type = "pow"
+        self.inputs = {'X': np.random.uniform(1, 2, [11, 17]).astype("float32")}
+        self.attrs = {'factor': 3}
+        self.outputs = {'Y': np.power(self.inputs['X'], 3)}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Y', max_relative_error=0.02)
+
+
+class TestSTanh(OpTest):
+    def setUp(self):
+        self.op_type = "stanh"
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [11, 17]).astype("float32")
+        }
+        scale_a = 2.0 / 3.0
+        scale_b = 1.7159
+        self.attrs = {'scale_a': scale_a, 'scale_b': scale_b}
+        self.outputs = {'Y': scale_b * np.tanh(self.inputs['X'] * scale_a)}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Y', max_relative_error=0.007)
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/v2/framework/tests/test_conv2d_op.py

@@ -73,13 +73,22 @@ class TestConv2dOp(OpTest):
         self.check_output()
 
     def test_check_grad(self):
-        self.check_grad(set(['Input', 'Filter']), 'Output')
+        self.check_grad(
+            set(['Input', 'Filter']), 'Output', max_relative_error=0.05)
 
     def test_check_grad_no_filter(self):
-        self.check_grad(['Input'], 'Output', no_grad_set=set(['Filter']))
+        self.check_grad(
+            ['Input'],
+            'Output',
+            max_relative_error=0.05,
+            no_grad_set=set(['Filter']))
 
     def test_check_grad_no_input(self):
-        self.check_grad(['Filter'], 'Output', no_grad_set=set(['Input']))
+        self.check_grad(
+            ['Filter'],
+            'Output',
+            max_relative_error=0.05,
+            no_grad_set=set(['Input']))
 
     def init_groups(self):
         self.groups = 1

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

+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+def crop(data, offsets, crop_shape):
+    def indexOf(shape, index):
+        result = []
+        for dim in reversed(shape):
+            result.append(index % dim)
+            index = index / dim
+        return result[::-1]
+
+    result = []
+    for i, value in enumerate(data.flatten()):
+        index = indexOf(data.shape, i)
+        selected = True
+        if len(index) == len(offsets):
+            for j, offset in enumerate(offsets):
+                selected = selected and index[j] >= offset and index[
+                    j] < crop_shape[j] + offset
+            if selected:
+                result.append(value)
+    return np.array(result).reshape(crop_shape)
+
+
+class TestCropOp(OpTest):
+    def setUp(self):
+        self.op_type = "crop"
+        self.crop_by_input = False
+        self.attrs = {}
+        self.initTestCase()
+        self.attrs['offsets'] = self.offsets
+        if self.crop_by_input:
+            self.inputs = {
+                'X': np.random.random(self.x_shape).astype("float32"),
+                'Y': np.random.random(self.crop_shape).astype("float32")
+            }
+        else:
+            self.attrs['shape'] = self.crop_shape
+            self.inputs = {
+                'X': np.random.random(self.x_shape).astype("float32"),
+            }
+        self.outputs = {
+            'Out': crop(self.inputs['X'], self.offsets, self.crop_shape)
+        }
+
+    def initTestCase(self):
+        self.x_shape = (8, 8)
+        self.crop_shape = (2, 2)
+        self.offsets = [1, 2]
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad_normal(self):
+        self.check_grad(['X'], 'Out', max_relative_error=0.006)
+
+
+class TestCase1(TestCropOp):
+    def initTestCase(self):
+        self.x_shape = (16, 8, 32)
+        self.crop_shape = [2, 2, 3]
+        self.offsets = [1, 5, 3]
+
+
+class TestCase2(TestCropOp):
+    def initTestCase(self):
+        self.x_shape = (4, 8)
+        self.crop_shape = [4, 8]
+        self.offsets = [0, 0]
+
+
+class TestCase3(TestCropOp):
+    def initTestCase(self):
+        self.x_shape = (4, 8, 16)
+        self.crop_shape = [2, 2, 3]
+        self.offsets = [1, 5, 3]
+        self.crop_by_input = True
+
+
+class TestCase4(TestCropOp):
+    def initTestCase(self):
+        self.x_shape = (4, 4)
+        self.crop_shape = [4, 4]
+        self.offsets = [0, 0]
+        self.crop_by_input = True
+
+
+if __name__ == '__main__':
+    unittest.main()

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

+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestRankLossOp(OpTest):
+    def setUp(self):
+        self.op_type = "rank_loss"
+        batch_size = 5
+        # labels_{i} = {0, 1.0} or {0, 0.5, 1.0}
+        label = np.random.randint(0, 2, size=(batch_size, 1)).astype("float32")
+        left = np.random.random((batch_size, 1)).astype("float32")
+        right = np.random.random((batch_size, 1)).astype("float32")
+        loss = np.log(1.0 + np.exp(left - right)) - label * (left - right)
+        self.inputs = {'Label': label, 'Left': left, 'Right': right}
+        self.outputs = {'Out': loss}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(["Left", "Right"], "Out")
+
+    def test_check_grad_ignore_left(self):
+        self.check_grad(["Right"], "Out", no_grad_set=set('Left'))
+
+    def test_check_grad_ignore_right(self):
+        self.check_grad(["Left"], "Out", no_grad_set=set('Right'))
+
+
+if __name__ == '__main__':
+    unittest.main()

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

-import unittest
-import numpy as np
-from op_test import OpTest
-
-
-class TestSigmoidOp(OpTest):
-    def setUp(self):
-        self.op_type = "sigmoid"
-        self.inputs = {
-            'X': np.random.uniform(0.1, 1, [11, 17]).astype("float32")
-        }
-        self.outputs = {'Y': 1 / (1 + np.exp(-self.inputs['X']))}
-
-    def test_check_output(self):
-        self.check_output()
-
-    def test_check_grad(self):
-        self.check_grad(["X"], "Y", max_relative_error=0.007)
-
-
-if __name__ == '__main__':
-    unittest.main()