diff --git a/paddle/function/CMakeLists.txt b/paddle/function/CMakeLists.txt
index 0b3126155d0c0872a70fc83260d4ea34161cb717..70b25406a4afda1378c3e4ee27b6ac4dee97a1ce 100644
--- a/paddle/function/CMakeLists.txt
+++ b/paddle/function/CMakeLists.txt
@@ -17,6 +17,7 @@ if(WITH_TESTING)
     # file(GLOB test_files . *OpTest.cpp)
     # add_executable(${test_bin} EXCLUDE_FROM_ALL ${test_files})
     add_simple_unittest(CrossMapNormalOpTest)
+    add_simple_unittest(PadOpTest)
     add_unittest(ContextProjectionOpTest
         ContextProjectionOpTest.cpp
         ../gserver/tests/TestUtil.cpp)
diff --git a/paddle/function/PadOp.cpp b/paddle/function/PadOp.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..e10011da2a1750573e79ac94f5f1f61215091404
--- /dev/null
+++ b/paddle/function/PadOp.cpp
@@ -0,0 +1,185 @@
+/* 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 "PadOp.h"
+#include "paddle/math/Vector.h"
+
+namespace paddle {
+
+template <>
+void Pad<DEVICE_TYPE_CPU>(real* outputs,
+                          const real* inputs,
+                          const int num,
+                          const int inC,
+                          const int inH,
+                          const int inW,
+                          const int padc0,
+                          const int padc1,
+                          const int padh0,
+                          const int padh1,
+                          const int padw0,
+                          const int padw1) {
+  int outC = inC + padc0 + padc1;
+  int outH = inH + padh0 + padh1;
+  int outW = inW + padw0 + padw1;
+  for (int i = 0; i < num; i++) {
+    for (int c = 0; c < inC; c++) {
+      for (int h = 0; h < inH; h++) {
+        int inoff = ((i * inC + c) * inH + h) * inW;
+        int outoff = ((i * outC + c + padc0) * outH + h + padh0) * outW + padw0;
+        memcpy(outputs + outoff, inputs + inoff, inW * sizeof(real));
+      }
+    }
+  }
+}
+
+template <>
+void PadGrad<DEVICE_TYPE_CPU>(real* inGrad,
+                              const real* outGrad,
+                              const int num,
+                              const int inC,
+                              const int inH,
+                              const int inW,
+                              const int padc0,
+                              const int padc1,
+                              const int padh0,
+                              const int padh1,
+                              const int padw0,
+                              const int padw1) {
+  int outC = inC + padc0 + padc1;
+  int outH = inH + padh0 + padh1;
+  int outW = inW + padw0 + padw1;
+  for (int i = 0; i < num; i++) {
+    for (int c = 0; c < inC; c++) {
+      for (int h = 0; h < inH; h++) {
+        int inoff = ((i * inC + c) * inH + h) * inW;
+        int outoff = ((i * outC + c + padc0) * outH + h + padh0) * outW + padw0;
+        CpuVector inG = CpuVector(inW, inGrad + inoff);
+        CpuVector outG = CpuVector(inW, const_cast<real*>(outGrad + outoff));
+        inG += outG;
+      }
+    }
+  }
+}
+
+/**
+ * \param inputs[0] input value.
+ * \param outputs[0] output value.
+ */
+template <DeviceType Device>
+class PadFunc : public FunctionBase {
+public:
+  void init(const FuncConfig& config) override {
+    padc0_ = config.get<int>("padc0");
+    padc1_ = config.get<int>("padc1");
+    padh0_ = config.get<int>("padh0");
+    padh1_ = config.get<int>("padh1");
+    padw0_ = config.get<int>("padw0");
+    padw1_ = config.get<int>("padw1");
+  }
+
+  void calc(const Arguments& inputs,
+            const Arguments& outputs,
+            const Arguments& inouts) override {
+    CHECK_EQ(1, inputs.size());
+    CHECK_EQ(1, outputs.size());
+    CHECK_EQ(0, inouts.size());
+
+    size_t num = inputs[0].dims_[0];
+    size_t inC = inputs[0].dims_[1];
+    size_t inH = inputs[0].dims_[2];
+    size_t inW = inputs[0].dims_[3];
+
+    Pad<Device>(outputs[0].getData(),
+                inputs[0].getData(),
+                num,
+                inC,
+                inH,
+                inW,
+                padc0_,
+                padc1_,
+                padh0_,
+                padh1_,
+                padw0_,
+                padw1_);
+  }
+
+private:
+  int padc0_;
+  int padc1_;
+  int padh0_;
+  int padh1_;
+  int padw0_;
+  int padw1_;
+};
+
+/**
+ * \param inputs[0] input grad.
+ * \param outputs[0] output grad.
+ */
+template <DeviceType Device>
+class PadGradFunc : public FunctionBase {
+public:
+  void init(const FuncConfig& config) override {
+    padc0_ = config.get<int>("padc0");
+    padc1_ = config.get<int>("padc1");
+    padh0_ = config.get<int>("padh0");
+    padh1_ = config.get<int>("padh1");
+    padw0_ = config.get<int>("padw0");
+    padw1_ = config.get<int>("padw1");
+  }
+
+  void calc(const Arguments& inputs,
+            const Arguments& outputs,
+            const Arguments& inouts) override {
+    CHECK_EQ(1, inputs.size());
+    CHECK_EQ(0, outputs.size());
+    CHECK_EQ(1, inouts.size());
+
+    size_t n = inouts[0].dims_[0];
+    size_t inC = inouts[0].dims_[1];
+    size_t inH = inouts[0].dims_[2];
+    size_t inW = inouts[0].dims_[3];
+
+    PadGrad<Device>(inouts[0].getData(),
+                    inputs[0].getData(),
+                    n,
+                    inC,
+                    inH,
+                    inW,
+                    padc0_,
+                    padc1_,
+                    padh0_,
+                    padh1_,
+                    padw0_,
+                    padw1_);
+  }
+
+private:
+  int padc0_;
+  int padc1_;
+  int padh0_;
+  int padh1_;
+  int padw0_;
+  int padw1_;
+};
+
+REGISTER_TYPED_FUNC(Pad, CPU, PadFunc);
+REGISTER_TYPED_FUNC(PadGrad, CPU, PadGradFunc);
+#ifndef PADDLE_ONLY_CPU
+REGISTER_TYPED_FUNC(Pad, GPU, PadFunc);
+REGISTER_TYPED_FUNC(PadGrad, GPU, PadGradFunc);
+#endif
+
+}  // namespace paddle
diff --git a/paddle/function/PadOp.h b/paddle/function/PadOp.h
new file mode 100644
index 0000000000000000000000000000000000000000..4a5e8fe33857aa5dddaff14e0351909d697b462f
--- /dev/null
+++ b/paddle/function/PadOp.h
@@ -0,0 +1,96 @@
+/* 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 "Function.h"
+
+namespace paddle {
+
+/**
+ * \brief  This funtion pads zeros to inputs according to the specify dimension.
+ *         The data structure of image data is NCHW.
+ *
+ * \param[out]  outputs  save results.
+ * \param[in]   inputs   input data.
+ * \param[in]   num      batch size of input data.
+ * \param[in]   inC      channel number of input data.
+ * \param[in]   inH      height of input data.
+ * \param[in]   inH      with of input data.
+ * \param[in]   padc0    how many values to add before the data in dimension of
+ * channel.
+ * \param[in]   padc1    how many values to add after the data in dimension of
+ * channel.
+ * \param[in]   padh0    how many values to add before the data in dimension of
+ * height.
+ * \param[in]   padh1    how many values to add after the data in dimension of
+ * height.
+ * \param[in]   padw0    how many values to add before the data in dimension of
+ * width.
+ * \param[in]   padw1    how many values to add after the data in dimension of
+ * width.
+ *
+ */
+template <DeviceType Device>
+void Pad(real* outputs,
+         const real* inputs,
+         const int num,
+         const int inC,
+         const int inH,
+         const int inW,
+         const int padc0,
+         const int padc1,
+         const int padh0,
+         const int padh1,
+         const int padw0,
+         const int padw1);
+
+/**
+ * \brief   Padding operation backward.
+ *          The data structure of image data is NCHW.
+ *
+ * \param[out]  inGrad   gradients of previous layer.
+ * \param[in]   outGrad  output gradients.
+ * \param[in]   num      batch size of input data.
+ * \param[in]   inC      channel number of input data.
+ * \param[in]   inH      height of input data.
+ * \param[in]   inH      with of input data.
+ * \param[in]   padc0    how many values to add before the data in dimension of
+ * channel.
+ * \param[in]   padc1    how many values to add after the data in dimension of
+ * channel.
+ * \param[in]   padh0    how many values to add before the data in dimension of
+ * height.
+ * \param[in]   padh1    how many values to add after the data in dimension of
+ * height.
+ * \param[in]   padw0    how many values to add before the data in dimension of
+ * width.
+ * \param[in]   padw1    how many values to add after the data in dimension of
+ * width.
+ *
+ */
+template <DeviceType Device>
+void PadGrad(real* inGrad,
+             const real* outGrad,
+             const int num,
+             const int inC,
+             const int inH,
+             const int inW,
+             const int padc0,
+             const int padc1,
+             const int padh0,
+             const int padh1,
+             const int padw0,
+             const int padw1);
+}  // namespace paddle
diff --git a/paddle/function/PadOpGpu.cu b/paddle/function/PadOpGpu.cu
new file mode 100644
index 0000000000000000000000000000000000000000..578d6e86d7534eef9958c4fc0b953b392b2c5bc8
--- /dev/null
+++ b/paddle/function/PadOpGpu.cu
@@ -0,0 +1,102 @@
+/* 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 "hl_base.h"
+#include "PadOp.h"
+
+namespace paddle {
+
+__global__ void KePad(real* outputs, const real* inputs,
+                      int inC, int inH, int inW,
+                      int padc, int padh, int padw,
+                      int outC, int outH, int outW, int nthreads) {
+  const int idx = threadIdx.x + blockIdx.x * blockDim.x;
+  if (idx < nthreads) {
+    const int w = idx % inW;
+    const int h = (idx / inW) % inH;
+    const int c = (idx / inW / inH) % inC;
+    const int n = idx / inW / inH / inC;
+
+    const int off = ((n * outC + c + padc) * outH + h + padh) * outW + padw + w;
+    outputs[off] = inputs[idx];
+  }
+}
+
+template <>
+void Pad<DEVICE_TYPE_GPU>(real* outputs,
+                          const real* inputs,
+                          const int num,
+                          const int inC,
+                          const int inH,
+                          const int inW,
+                          const int padc0,
+                          const int padc1,
+                          const int padh0,
+                          const int padh1,
+                          const int padw0,
+                          const int padw1) {
+  size_t nth = num * inC * inH * inW;
+  int blockSize = 1024;
+  int gridSize = (nth + 1024 - 1) / 1024;
+  int outC = inC + padc0 + padc1;
+  int outH = inH + padh0 + padh1;
+  int outW = inW + padw0 + padw1;
+  KePad<<<gridSize, blockSize, 0, STREAM_DEFAULT>>>
+    (outputs, inputs, inC, inH, inW, padc0, padh0, padw0,
+     outC, outH, outW, nth);
+  CHECK_SYNC("Pad");
+}
+
+__global__ void KePadDiff(real* inGrad, const real* outGrad,
+                          int inC, int inH, int inW,
+                          int padc, int padh, int padw,
+                          int outC, int outH, int outW, int nthreads) {
+  const int idx = threadIdx.x + blockIdx.x * blockDim.x;
+  if (idx < nthreads) {
+    const int w = idx % inW;
+    const int h = (idx / inW) % inH;
+    const int c = (idx / inW / inH) % inC;
+    const int n = idx / inW / inH / inC;
+
+    const int off = ((n * outC + c + padc) * outH + h + padh) * outW + padw + w;
+    inGrad[idx] += outGrad[off];
+  }
+}
+
+template <>
+void PadGrad<DEVICE_TYPE_GPU>(real* inGrad,
+                              const real* outGrad,
+                              const int num,
+                              const int inC,
+                              const int inH,
+                              const int inW,
+                              const int padc0,
+                              const int padc1,
+                              const int padh0,
+                              const int padh1,
+                              const int padw0,
+                              const int padw1) {
+  int nth = num * inC * inH * inW;
+  int blockSize = 1024;
+  int gridSize = (nth + 1024 - 1) / 1024;
+  int outC = inC + padc0 + padc1;
+  int outH = inH + padh0 + padh1;
+  int outW = inW + padw0 + padw1;
+  KePadDiff <<<gridSize, blockSize, 0, STREAM_DEFAULT>>>
+    (inGrad, outGrad, inC, inH, inW, padc0, padh0, padw0,
+     outC, outH, outW, nth);
+  CHECK_SYNC("PadGrad");
+}
+
+}  // namespace paddle
diff --git a/paddle/function/PadOpTest.cpp b/paddle/function/PadOpTest.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..ee2834d793d34898c3d09877da9b953e26c3dc94
--- /dev/null
+++ b/paddle/function/PadOpTest.cpp
@@ -0,0 +1,70 @@
+/* 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 <gtest/gtest.h>
+#include "FunctionTest.h"
+
+namespace paddle {
+
+TEST(Pad, real) {
+  for (size_t numSamples : {5, 32}) {
+    for (size_t channels : {1, 5, 32}) {
+      for (size_t imgSizeH : {5, 33, 100}) {
+        for (size_t imgSizeW : {5, 32, 96}) {
+          VLOG(3) << " numSamples=" << numSamples << " channels=" << channels
+                  << " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW;
+
+          FunctionCompare compare("Pad",
+                                  FuncConfig()
+                                      .set("padc0", 2)
+                                      .set("padc1", 3)
+                                      .set("padh0", 1)
+                                      .set("padh1", 2)
+                                      .set("padw0", 3)
+                                      .set("padw1", 2));
+          Dims inDims{numSamples, channels, imgSizeH, imgSizeW};
+          Dims outDims{numSamples, channels + 5, imgSizeH + 3, imgSizeW + 5};
+          compare.cmpWithArg(
+              {Tensor(nullptr, inDims)}, {Tensor(nullptr, outDims)}, {});
+        }
+      }
+    }
+  }
+}
+
+// TEST(PadGrad, real) {
+//  for (size_t numSamples : {5, 32}) {
+//    for (size_t channels : {1, 5, 32}) {
+//      for (size_t imgSizeH : {5, 33, 100}) {
+//        for (size_t imgSizeW : {5, 32, 96}) {
+//          VLOG(3) << " numSamples=" << numSamples << " channels=" << channels
+//                  << " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW;
+//
+//          FunctionCompare compare("PadGrad",
+//                                  FuncConfig()
+//                                     .set("padc0", 2).set("padc1", 3)
+//                                     .set("padh0", 1).set("padh1", 2)
+//                                     .set("padw0", 3).set("padw1", 2));
+//          Dims inDims{numSamples, channels, imgSizeH, imgSizeW};
+//          Dims outDims{numSamples, channels + 5, imgSizeH + 3, imgSizeW + 5};
+//          compare.cmpWithArg({Tensor(nullptr, inDims)},
+//                             {Tensor(nullptr, outDims)},
+//                             {});
+//        }
+//      }
+//    }
+//  }
+//}
+
+}  // namespace paddle
diff --git a/paddle/gserver/layers/PadLayer.cpp b/paddle/gserver/layers/PadLayer.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..62c50af32dd05535d338893046374dffed3f11ee
--- /dev/null
+++ b/paddle/gserver/layers/PadLayer.cpp
@@ -0,0 +1,115 @@
+/* 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 "PadLayer.h"
+#include "paddle/utils/Stat.h"
+
+namespace paddle {
+
+REGISTER_LAYER(pad, PadLayer);
+
+bool PadLayer::init(const LayerMap& layerMap,
+                    const ParameterMap& parameterMap) {
+  /* Initialize the basic parent class */
+  Layer::init(layerMap, parameterMap);
+
+  auto& pad_conf = config_.inputs(0).pad_conf();
+  auto& img_conf = pad_conf.image_conf();
+  CHECK_EQ(config_.inputs_size(), 1);
+  inDims_.push_back(0);
+  inDims_.push_back(img_conf.channels());
+  inDims_.push_back(img_conf.has_img_size_y() ? img_conf.img_size_y()
+                                              : img_conf.img_size());
+  inDims_.push_back(img_conf.img_size());
+
+  CHECK_EQ(2UL, pad_conf.pad_c_size());
+  CHECK_EQ(2UL, pad_conf.pad_h_size());
+  CHECK_EQ(2UL, pad_conf.pad_w_size());
+  padc_.push_back(pad_conf.pad_c(0));
+  padc_.push_back(pad_conf.pad_c(1));
+  padh_.push_back(pad_conf.pad_h(0));
+  padh_.push_back(pad_conf.pad_h(1));
+  padw_.push_back(pad_conf.pad_w(0));
+  padw_.push_back(pad_conf.pad_w(1));
+
+  outDims_.resize(4);
+  setOutDims(0);
+
+  createFunction(forward_,
+                 "Pad",
+                 FuncConfig()
+                     .set("padc0", padc_[0])
+                     .set("padc1", padc_[1])
+                     .set("padh0", padh_[0])
+                     .set("padh1", padh_[1])
+                     .set("padw0", padw_[0])
+                     .set("padw1", padw_[1]));
+  createFunction(backward_,
+                 "PadGrad",
+                 FuncConfig()
+                     .set("padc0", padc_[0])
+                     .set("padc1", padc_[1])
+                     .set("padh0", padh_[0])
+                     .set("padh1", padh_[1])
+                     .set("padw0", padw_[0])
+                     .set("padw1", padw_[1]));
+
+  return true;
+}
+
+void PadLayer::setOutDims(int batchSize) {
+  outDims_[0] = batchSize;
+  outDims_[1] = inDims_[1] + padc_[0] + padc_[1];
+  outDims_[2] = inDims_[2] + padh_[0] + padh_[1];
+  outDims_[3] = inDims_[3] + padw_[0] + padw_[1];
+}
+
+void PadLayer::setTensorDim(int batchSize) {
+  CHECK_EQ(inputLayers_.size(), 1UL);
+  inDims_[0] = batchSize;
+  int h = inputLayers_[0]->getOutput().getFrameHeight();
+  if (h != 0) inDims_[2];
+  int w = inputLayers_[0]->getOutput().getFrameWidth();
+  if (w != 0) inDims_[3];
+  setOutDims(batchSize);
+}
+
+void PadLayer::forward(PassType passType) {
+  Layer::forward(passType);
+  MatrixPtr input = inputLayers_[0]->getOutputValue();
+  size_t batchSize = input->getHeight();
+  setTensorDim(batchSize);
+  int size = outDims_[1] * outDims_[2] * outDims_[3];
+  resetOutput(batchSize, size);
+  MatrixPtr outV = getOutputValue();
+  REGISTER_TIMER_INFO("PadForward", getName().c_str());
+  forward_[0]->calc({Tensor(input->getData(), inDims_)},
+                    {Tensor(outV->getData(), outDims_)},
+                    {});
+}
+
+void PadLayer::backward(const UpdateCallback& callback) {
+  (void)callback;
+
+  MatrixPtr preGrad = inputLayers_[0]->getOutputGrad();
+  if (NULL == preGrad) {
+    return;
+  }
+  MatrixPtr outGrad = getOutputGrad();
+  REGISTER_TIMER_INFO("PadBackward", getName().c_str());
+  backward_[0]->calc({Tensor(outGrad->getData(), outDims_)},
+                     {},
+                     {Tensor(preGrad->getData(), inDims_)});
+}
+}  // namespace paddle
diff --git a/paddle/gserver/layers/PadLayer.h b/paddle/gserver/layers/PadLayer.h
new file mode 100644
index 0000000000000000000000000000000000000000..834622a7af396e870865667b6672d896cd1627d7
--- /dev/null
+++ b/paddle/gserver/layers/PadLayer.h
@@ -0,0 +1,45 @@
+/* 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 "Layer.h"
+
+namespace paddle {
+
+/**
+ * @brief response normalization across feature maps
+ * namely normalize in number of size_ channels
+ */
+class PadLayer : public Layer {
+public:
+  explicit PadLayer(const LayerConfig& config) : Layer(config) {}
+
+  ~PadLayer() {}
+
+  bool init(const LayerMap& layerMap, const ParameterMap& parameterMap);
+  void forward(PassType passType);
+  void backward(const UpdateCallback& callback = nullptr);
+
+protected:
+  void setOutDims(int batchSize);
+  void setTensorDim(int batchSize);
+
+  std::vector<int> padc_;
+  std::vector<int> padh_;
+  std::vector<int> padw_;
+  Dims inDims_;
+  Dims outDims_;
+};
+}  // namespace paddle
diff --git a/paddle/gserver/tests/test_LayerGrad.cpp b/paddle/gserver/tests/test_LayerGrad.cpp
index 2cc25f6b211e367fc82c07c30082c3e12c04e53d..3094b3a4a02e3e1cd1001c60982038a3d1175a4b 100644
--- a/paddle/gserver/tests/test_LayerGrad.cpp
+++ b/paddle/gserver/tests/test_LayerGrad.cpp
@@ -32,1534 +32,1580 @@ DECLARE_double(checkgrad_eps);
 DECLARE_bool(thread_local_rand_use_global_seed);
 DECLARE_bool(prev_batch_state);
 
-TEST(Operator, dot_mul) {
+// TEST(Operator, dot_mul) {
+//   TestConfig config;
+//   config.layerConfig.set_size(10);
+//
+//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
+//   config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
+//   config.layerConfig.add_inputs();
+//   config.layerConfig.add_inputs();
+//
+//   OperatorConfig& operatorConf = *config.layerConfig.add_operator_confs();
+//   operatorConf.set_type("dot_mul");
+//   operatorConf.set_dotmul_scale(-1);
+//
+//   testOperatorGrad(config, operatorConf, 100, false, false);
+// }
+//
+// TEST(Projection, context) {
+//   for (auto contextStart : {-5, -3, -1, 0, 3}) {
+//     for (auto contextLength : {1, 2, 5, 7}) {
+//       for (auto batchSize : {1, 2, 5, 20, 50}) {
+//         for (auto trainablePadding : {false, true}) {
+//           LOG(INFO) << " contextStart=" << contextStart
+//                     << " contextLength=" << contextLength
+//                     << " batchSize=" << batchSize
+//                     << " trainablePadding=" << trainablePadding;
+//           ProjectionConfig conf;
+//           conf.set_type("context");
+//           conf.set_input_size(10);
+//           conf.set_context_start(contextStart);
+//           conf.set_context_length(contextLength);
+//           conf.set_trainable_padding(trainablePadding);
+//           conf.set_output_size(conf.context_length() * conf.input_size());
+//           int pad =
+//               std::max(0, -conf.context_start()) +
+//               std::max(0, conf.context_start() + conf.context_length() - 1);
+//           for (auto useGpu : {false, true}) {
+//             testProjectionGrad(
+//                 conf,
+//                 INPUT_SEQUENCE_DATA,
+//                 trainablePadding ? conf.input_size() * pad : 0,
+//                 batchSize,
+//                 useGpu,
+//                 contextStart + contextLength <= 1);  // = testState
+//           }
+//         }
+//       }
+//     }
+//   }
+// }
+//
+// TEST(Projection, trans_fc) {
+//   ProjectionConfig conf;
+//   conf.set_type("trans_fc");
+//   conf.set_input_size(50);
+//   conf.set_output_size(20);
+//   for (auto useGpu : {false, true}) {
+//     testProjectionGrad(conf,
+//                        INPUT_DATA,
+//                        /* parameterSize */ 1000,
+//                        /* batchSize */ 100,
+//                        useGpu);
+//   }
+// }
+//
+// TEST(Projection, fc) {
+//   ProjectionConfig conf;
+//   conf.set_type("fc");
+//   conf.set_input_size(10);
+//   conf.set_output_size(20);
+//   for (auto useGpu : {false, true}) {
+//     testProjectionGrad(conf,
+//                        INPUT_DATA,
+//                        /* parameterSize */ 200,
+//                        /* batchSize */ 100,
+//                        useGpu);
+//   }
+// }
+//
+// TEST(Projection, dot_mul) {
+//   ProjectionConfig conf;
+//   conf.set_type("dot_mul");
+//   conf.set_input_size(20);
+//   conf.set_output_size(20);
+//   for (auto useGpu : {false, true}) {
+//     testProjectionGrad(conf,
+//                        INPUT_DATA,
+//                        /* parameterSize */ 20,
+//                        /* batchSize */ 100,
+//                        useGpu);
+//   }
+// }
+//
+// TEST(Projection, table) {
+//   ProjectionConfig conf;
+//   conf.set_type("table");
+//   conf.set_input_size(10);
+//   conf.set_output_size(20);
+//   for (auto useGpu : {false, true}) {
+//     testProjectionGrad(conf,
+//                        INPUT_LABEL,
+//                        /* parameterSize */ 200,
+//                        /* batchSize */ 100,
+//                        useGpu);
+//   }
+// }
+//
+// TEST(Projection, identity) {
+//   ProjectionConfig conf;
+//   conf.set_type("identity");
+//   conf.set_input_size(10);
+//   conf.set_output_size(10);
+//   for (auto useGpu : {false, true}) {
+//     testProjectionGrad(conf,
+//                        INPUT_DATA,
+//                        /* parameterSize */ 0,
+//                        /* batchSize */ 100,
+//                        useGpu);
+//   }
+// }
+//
+// TEST(Projection, scaling) {
+//   ProjectionConfig conf;
+//   conf.set_type("scaling");
+//   conf.set_input_size(10);
+//   conf.set_output_size(10);
+//   for (auto useGpu : {false}) {
+//     testProjectionGrad(conf,
+//                        INPUT_DATA,
+//                        /* parameterSize */ 1,
+//                        /* batchSize */ 100,
+//                        useGpu);
+//   }
+// }
+//
+// void testProjectionConv(size_t groups) {
+//   const int NUM_FILTERS = 18;
+//   const int FILTER_SIZE = 2;
+//   const int FILTER_SIZE_Y = 3;
+//   const int CHANNELS = 3;
+//   const int IMAGE_SIZE = 16;
+//
+//   ProjectionConfig conf;
+//   conf.set_type("conv");
+//   conf.set_num_filters(NUM_FILTERS);
+//
+//   ConvConfig* conv = conf.mutable_conv_conf();
+//   conv->set_filter_size(FILTER_SIZE);
+//   conv->set_filter_size_y(FILTER_SIZE_Y);
+//   conv->set_channels(CHANNELS);
+//   conv->set_padding(0);
+//   conv->set_padding_y(1);
+//   conv->set_stride(2);
+//   conv->set_stride_y(2);
+//   conv->set_groups(groups);
+//   conv->set_filter_channels(conv->channels() / conv->groups());
+//   conv->set_img_size(IMAGE_SIZE);
+//   int output_x = outputSize(conv->img_size(),
+//                             conv->filter_size(),
+//                             conv->padding(),
+//                             conv->stride(),
+//                             /* caffeMode */ true);
+//   int output_y = outputSize(conv->img_size(),
+//                             conv->filter_size_y(),
+//                             conv->padding_y(),
+//                             conv->stride_y(),
+//                             /* caffeMode */ true);
+//   conv->set_output_x(output_x);
+//   conf.set_input_size(IMAGE_SIZE * IMAGE_SIZE * CHANNELS);
+//   conf.set_output_size(output_x * output_y * NUM_FILTERS);
+//
+//   testProjectionGrad(conf,
+//                      INPUT_DATA,
+//                      /* parameterSize */ NUM_FILTERS * CHANNELS * FILTER_SIZE
+//                      *
+//                          FILTER_SIZE_Y / groups,
+//                      /* batchSize */ 100,
+//                      true,
+//                      false,
+//                      NUM_FILTERS,
+//                      true);
+// }
+//
+// #ifndef PADDLE_ONLY_CPU
+// TEST(Projection, conv) {
+//   testProjectionConv(1);
+//   testProjectionConv(3);
+// }
+// #endif
+//
+// TEST(Layer, BilinearInterpLayer) {
+//   TestConfig config;
+//   config.layerConfig.set_type("bilinear_interp");
+//   config.biasSize = 0;
+//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 4096, 0});
+//
+//   LayerInputConfig* input = config.layerConfig.add_inputs();
+//   BilinearInterpConfig* bilinear = input->mutable_bilinear_interp_conf();
+//   ImageConfig* image = bilinear->mutable_image_conf();
+//   image->set_img_size(32);
+//   image->set_img_size_y(32);
+//   image->set_channels(4);
+//
+//   for (auto useGpu : {false, true}) {
+//     for (auto outSize : {32, 64}) {
+//       bilinear->set_out_size_x(outSize);
+//       bilinear->set_out_size_y(outSize);
+//       testLayerGrad(config, "bilinear_interp", 10, false, useGpu);
+//     }
+//   }
+// }
+//
+// TEST(Layer, concat) {
+//   TestConfig config;
+//   config.biasSize = 0;
+//   config.layerConfig.set_type("concat");
+//   config.layerConfig.set_size(15);
+//   config.layerConfig.set_active_type("sigmoid");
+//
+//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 5, 0});
+//   config.layerConfig.add_inputs();
+//   config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
+//   config.layerConfig.add_inputs();
+//
+//   for (auto useGpu : {false, true}) {
+//     testLayerGrad(config, "concat", 100, false, useGpu);
+//   }
+// }
+//
+// TEST(Layer, AddtoLayer) {
+//   TestConfig config;
+//   config.biasSize = 0;
+//   config.layerConfig.set_type("addto");
+//   config.layerConfig.set_size(10);
+//   config.layerConfig.set_active_type("sigmoid");
+//
+//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
+//   config.layerConfig.add_inputs();
+//   config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
+//   config.layerConfig.add_inputs();
+//
+//   for (auto useGpu : {false, true}) {
+//     testLayerGrad(config, "addto", 100, false, useGpu);
+//   }
+// }
+//
+// TEST(Layer, CRFLayer) {
+//   TestConfig config;
+//   config.layerConfig.set_type("crf");
+//   config.layerConfig.set_size(10);
+//   config.biasSize = 0;
+//
+//   config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 10, 120});
+//   config.inputDefs.push_back({INPUT_SEQUENCE_LABEL, "layer_1", 10, 0});
+//   config.layerConfig.add_inputs();
+//   config.layerConfig.add_inputs();
+//
+//   // Not support GPU now
+//   testLayerGrad(config,
+//                 "crf",
+//                 100,
+//                 /* trans */ false,
+//                 /* useGpu */ false,
+//                 false /*useWeight*/,
+//                 0.03 /*epsilon*/);
+// }
+//
+// TEST(Layer, CTCLayer) {
+//   TestConfig config;
+//   config.layerConfig.set_type("ctc");
+//   config.layerConfig.set_norm_by_times(false);
+//   config.layerConfig.set_size(10);
+//   config.biasSize = 0;
+//
+//   config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 10, 0});
+//   config.inputDefs.push_back({INPUT_SEQUENCE_LABEL, "layer_1", 10, 0});
+//   config.layerConfig.add_inputs();
+//   config.layerConfig.add_inputs();
+//
+//   for (auto useGpu : {false, true}) {
+//     testLayerGrad(config, "ctc", 100, /* trans */ false, /* useGpu */
+//     useGpu);
+//   }
+// }
+//
+// TEST(Layer, cosSimLayer) {
+//   TestConfig config;
+//   config.layerConfig.set_type("cos");
+//   config.layerConfig.set_size(1);
+//   config.biasSize = 0;
+//
+//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
+//   config.inputDefs.push_back({INPUT_DATA, "layer_1", 50, 0});
+//   config.layerConfig.add_inputs();
+//   config.layerConfig.add_inputs();
+//
+//   for (auto useGpu : {false, true}) {
+//     testLayerGrad(config, "cos", 100, false, useGpu);
+//   }
+// }
+//
+// TEST(Layer, CosSimVecMatLayer) {
+//   TestConfig config;
+//   config.layerConfig.set_type("cos_vm");
+//   config.layerConfig.set_size(5);  // output size
+//   config.layerConfig.set_cos_scale(2.0);
+//
+//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 20, 0});
+//   config.layerConfig.add_inputs();
+//   config.inputDefs.push_back({INPUT_DATA, "layer_1", 100, 0});
+//   config.layerConfig.add_inputs();
+//
+//   for (auto useGpu : {false, true}) {
+//     testLayerGrad(config, "cos_vm", 100, false, useGpu);
+//   }
+// }
+//
+// void testConvLayer(const string& type, bool trans, bool useGpu) {
+//   TestConfig config;
+//   config.biasSize = 16;
+//   config.layerConfig.set_type(type);
+//   config.layerConfig.set_num_filters(16);
+//   config.layerConfig.set_partial_sum(1);
+//   config.layerConfig.set_shared_biases(true);
+//
+//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 384, 288});
+//   LayerInputConfig* input = config.layerConfig.add_inputs();
+//   ConvConfig* conv = input->mutable_conv_conf();
+//   conv->set_filter_size(2);
+//   conv->set_filter_size_y(3);
+//   conv->set_channels(3);
+//   conv->set_padding(0);
+//   conv->set_padding_y(1);
+//   conv->set_stride(2);
+//   conv->set_stride_y(2);
+//   conv->set_groups(1);
+//   conv->set_filter_channels(conv->channels() / conv->groups());
+//   conv->set_img_size(16);
+//   conv->set_img_size_y(8);
+//   conv->set_output_x(outputSize(conv->img_size(),
+//                                 conv->filter_size(),
+//                                 conv->padding(),
+//                                 conv->stride(),
+//                                 /* caffeMode */ true));
+//   conv->set_output_y(outputSize(conv->img_size_y(),
+//                                 conv->filter_size_y(),
+//                                 conv->padding_y(),
+//                                 conv->stride_y(),
+//                                 /* caffeMode */ true));
+//   config.layerConfig.set_size(conv->output_x() * conv->output_y() *
+//                               config.layerConfig.num_filters());
+//
+//   testLayerGrad(config, "conv", 100, trans, useGpu);
+//   // Use small batch_size and useWeight=true to test biasGrad
+//   testLayerGrad(config, "conv", 2, trans, useGpu, true, 0.02);
+// }
+//
+// TEST(Layer, convLayer) {
+//   testConvLayer("exconv", /* trans= */ false, /* useGpu= */ false);
+// #ifndef PADDLE_ONLY_CPU
+//   testConvLayer("exconv", /* trans= */ false, /* useGpu= */ true);
+//   testConvLayer("cudnn_conv", /* trans= */ false, /* useGpu= */ true);
+// #endif
+// }
+//
+// void testConvTransLayer(const string& type, bool trans, bool useGpu) {
+//   TestConfig config;
+//   config.biasSize = 3;
+//   config.layerConfig.set_type(type);
+//   config.layerConfig.set_num_filters(3);
+//   config.layerConfig.set_partial_sum(1);
+//   config.layerConfig.set_shared_biases(true);
+//
+//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 1024, 288});
+//   LayerInputConfig* input = config.layerConfig.add_inputs();
+//   ConvConfig* conv = input->mutable_conv_conf();
+//   conv->set_filter_size(2);
+//   conv->set_filter_size_y(3);
+//   conv->set_channels(16);
+//   conv->set_padding(0);
+//   conv->set_padding_y(1);
+//   conv->set_stride(2);
+//   conv->set_stride_y(2);
+//   conv->set_groups(1);
+//   conv->set_filter_channels(3 / conv->groups());
+//   conv->set_img_size(16);
+//   conv->set_output_x(outputSize(conv->img_size(),
+//                                 conv->filter_size(),
+//                                 conv->padding(),
+//                                 conv->stride(),
+//                                 /* caffeMode */ true));
+//
+//   config.layerConfig.set_size(conv->img_size() * conv->img_size() *
+//                               config.layerConfig.num_filters());
+//
+//   testLayerGrad(config, "convTrans", 100, trans, useGpu);
+//   // Use small batch_size and useWeight=true to test biasGrad
+//   testLayerGrad(config, "convTrans", 2, trans, useGpu, true, 0.02);
+// }
+//
+// TEST(Layer, convTransLayer) {
+//   for (auto useGpu : {false, true}) {
+//     testConvTransLayer("exconvt", /* trans= */ false, /* useGpu= */ useGpu);
+//   }
+// }
+//
+// TEST(Layer, blockExpandLayer) {
+//   TestConfig config;
+//   config.biasSize = 0;
+//   config.layerConfig.set_type("blockexpand");
+//
+//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 6144, 0});
+//   LayerInputConfig* input = config.layerConfig.add_inputs();
+//   BlockExpandConfig* blockExpand = input->mutable_block_expand_conf();
+//   blockExpand->set_img_size_x(64);
+//   blockExpand->set_img_size_y(32);
+//   blockExpand->set_channels(3);
+//   blockExpand->set_padding_x(0);
+//   blockExpand->set_padding_y(0);
+//   blockExpand->set_block_x(4);
+//   blockExpand->set_block_y(32);
+//   blockExpand->set_stride_x(2);
+//   blockExpand->set_stride_y(2);
+//   blockExpand->set_output_x(outputSize(blockExpand->img_size_x(),
+//                                        blockExpand->block_x(),
+//                                        blockExpand->padding_x(),
+//                                        blockExpand->stride_x(),
+//                                        /* caffeMode */ false));
+//   blockExpand->set_output_y(outputSize(blockExpand->img_size_y(),
+//                                        blockExpand->block_y(),
+//                                        blockExpand->padding_y(),
+//                                        blockExpand->stride_y(),
+//                                        /* caffeMode */ false));
+//   config.layerConfig.set_size(blockExpand->block_x() * blockExpand->block_y()
+//   *
+//                               blockExpand->channels());
+//
+//   for (auto useGpu : {false, true}) {
+//     testLayerGrad(config, "blockexpand", 100, false, useGpu);
+//   }
+// }
+//
+// TEST(Layer, maxoutLayer) {
+//   TestConfig config;
+//   config.biasSize = 0;
+//   config.layerConfig.set_type("maxout");
+//
+//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 4096, 0});
+//   LayerInputConfig* input = config.layerConfig.add_inputs();
+//   MaxOutConfig* maxout = input->mutable_maxout_conf();
+//   ImageConfig* image = maxout->mutable_image_conf();
+//
+//   image->set_img_size(32);
+//   image->set_img_size_y(32);
+//   image->set_channels(4);
+//   maxout->set_groups(2);
+//
+//   for (auto useGpu : {false, true}) {
+//     testLayerGrad(config, "maxout", 10, false, useGpu);
+//   }
+// }
+// void testFcLayer(string format, size_t nnz) {
+//   TestConfig config;
+//   config.biasSize = 4096;
+//   config.layerConfig.set_type("fc");
+//   config.layerConfig.set_size(4096);
+//   config.layerConfig.set_active_type("sigmoid");
+//   config.layerConfig.set_drop_rate(0.1);
+//
+//   config.inputDefs.push_back(
+//       {INPUT_DATA, "layer_0", 8192, nnz, ParaSparse(format)});
+//   config.layerConfig.add_inputs();
+//
+//   LOG(INFO) << config.inputDefs[0].sparse.sparse << " "
+//             << config.inputDefs[0].sparse.format;
+//
+//   for (auto useGpu : {false, true}) {
+//     testLayerGrad(config,
+//                   "fc",
+//                   100,
+//                   /* trans */ false,
+//                   useGpu,
+//                   /* weight */ true);
+//   }
+// }
+//
+// TEST(Layer, fcLayer) {
+//   testFcLayer("", 4096 * 4096 * 2);
+//   testFcLayer("csc", 4096 * 40);
+//   testFcLayer("csr", 4096 * 40);
+// }
+//
+// TEST(Layer, SelectiveFullyConnectedLayer) {
+//   TestConfig config;
+//   size_t nin = 16;
+//   size_t nout = 256;
+//   config.layerConfig.set_type("selective_fc");
+//   config.layerConfig.set_size(nout);
+//   config.layerConfig.set_active_type("sigmoid");
+//   config.layerConfig.set_has_selected_colums(true);
+//   config.layerConfig.set_selective_fc_pass_generation(false);
+//   config.biasSize = nout;
+//
+//   config.inputDefs.push_back({INPUT_DATA, "input0", nin, nin * nout});
+//   config.layerConfig.add_inputs();
+//   config.inputDefs.push_back(
+//       {INPUT_SPARSE_NON_VALUE_DATA, "index", nout, 0, ParaSparse("csr",
+//       true)});
+//   config.layerConfig.add_inputs();
+//
+//   testLayerGrad(config,
+//                 "selective_fc",
+//                 100,
+//                 /* trans= */ false,
+//                 /* useGup= */ false,
+//                 false);
+// #ifndef PADDLE_ONLY_CPU
+//   testLayerGrad(config,
+//                 "selective_fc",
+//                 100,
+//                 /* trans= */ false,
+//                 /* useGup= */ true,
+//                 false);
+// #endif
+// }
+//
+// TEST(Layer, DataNormLayer) {
+//   TestConfig config;
+//   config.layerConfig.set_type("data_norm");
+//   config.layerConfig.set_size(20);
+//   config.biasSize = 0;
+//
+//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 20, 100});
+//   config.inputDefs.back().isStatic = true;
+//   config.layerConfig.add_inputs();
+//
+//   for (auto strategy : {"z-score", "min-max", "decimal-scaling"}) {
+//     config.layerConfig.set_data_norm_strategy(strategy);
+//     // The parameters are static, so not support GPU now
+//     testLayerGrad(config,
+//                   "data_norm",
+//                   200,
+//                   /* trans */ false,
+//                   /* useGpu */ false);
+//   }
+// }
+//
+// TEST(Layer, hsigmoidLayer) {
+//   TestConfig config;
+//   config.layerConfig.set_type("hsigmoid");
+//   config.layerConfig.set_num_classes(5);
+//   config.layerConfig.set_size(1);
+//   config.biasSize = config.layerConfig.num_classes() - 1;
+//
+//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 200});
+//   config.inputDefs.push_back({INPUT_LABEL, "layer_1", 5, 0});
+//   config.layerConfig.add_inputs();
+//   config.layerConfig.add_inputs();
+//
+//   // Not support GPU now
+//   testLayerGrad(config, "hsigmoid", 100, /* trans */ false, /* useGpu */
+//   false);
+// }
+//
+// TEST(Layer, multi_cross) {
+//   TestConfig config;
+//   config.layerConfig.set_type("multi-class-cross-entropy");
+//   config.biasSize = 0;
+//
+//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
+//   config.inputDefs.push_back({INPUT_LABEL, "layer_1", 10, 0});
+//   config.layerConfig.add_inputs();
+//   config.layerConfig.add_inputs();
+//
+//   for (auto useGpu : {false, true}) {
+//     testLayerGrad(
+//         config, "multi-class-cross-entropy", 100, /* trans */ false, useGpu);
+//   }
+// }
+//
+// TEST(Layer, multi_binary_label_sparse_mat) {
+//   TestConfig config;
+//   config.layerConfig.set_type("multi_binary_label_cross_entropy");
+//   config.biasSize = 0;
+//
+//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
+//   config.inputDefs.push_back({INPUT_SPARSE_NON_VALUE_DATA, "layer_1", 50,
+//   0});
+//   config.layerConfig.add_inputs();
+//   config.layerConfig.add_inputs();
+//
+//   for (auto useGpu : {false, true}) {
+//     testLayerGrad(config,
+//                   "multi_binary_label_cross_entropy",
+//                   100,
+//                   /* trans */ false,
+//                   useGpu);
+//   }
+// }
+//
+// TEST(layer, multi_binary_label_id) {
+//   TestConfig config;
+//   config.layerConfig.set_type("multi_binary_label_cross_entropy");
+//   config.biasSize = 0;
+//
+//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
+//   config.inputDefs.push_back({INPUT_LABEL, "layer_1", 10, 0});
+//   config.layerConfig.add_inputs();
+//   config.layerConfig.add_inputs();
+//
+//   for (auto useGpu : {false, true}) {
+//     testLayerGrad(config,
+//                   "multi_binary_label_cross_entropy",
+//                   100,
+//                   /* trans */ false,
+//                   useGpu);
+//   }
+// }
+//
+// TEST(Layer, multi_cross_with_selfnorm) {
+//   TestConfig config;
+//   config.layerConfig.set_type("multi_class_cross_entropy_with_selfnorm");
+//   config.layerConfig.set_softmax_selfnorm_alpha(0.1);
+//   config.biasSize = 0;
+//
+//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
+//   config.inputDefs.push_back({INPUT_LABEL, "layer_1", 10, 0});
+//   config.layerConfig.add_inputs();
+//   config.layerConfig.add_inputs();
+//
+//   // Not support GPU now
+//   testLayerGrad(config,
+//                 "multi_class_cross_entropy_with_selfnorm",
+//                 100,
+//                 /* trans */ false,
+//                 /* useGpu */ false);
+// }
+//
+// TEST(Layer, multi_cross_soft) {
+//   TestConfig config;
+//   config.layerConfig.set_type("soft_binary_class_cross_entropy");
+//   config.biasSize = 0;
+//
+//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
+//   config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 10, 0});
+//   config.layerConfig.add_inputs();
+//   config.layerConfig.add_inputs();
+//
+//   for (auto useGpu : {false, true}) {
+//     testLayerGrad(config,
+//                   "soft_binary_class_cross_entropy",
+//                   100,
+//                   /* trans */ false,
+//                   useGpu);
+//   }
+// }
+//
+// TEST(Layer, square_error) {
+//   TestConfig config;
+//   config.layerConfig.set_type("square_error");
+//   config.biasSize = 0;
+//
+//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
+//   config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 10, 0});
+//   config.layerConfig.add_inputs();
+//   config.layerConfig.add_inputs();
+//
+//   for (auto useGpu : {false, true}) {
+//     testLayerGrad(config, "square_error", 100, /* trans */ false, useGpu);
+//   }
+// }
+//
+// TEST(Layer, sparse_square_error) {
+//   TestConfig config;
+//   config.layerConfig.set_type("square_error");
+//   config.biasSize = 0;
+//
+//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
+//   config.inputDefs.push_back({INPUT_SPARSE_NON_VALUE_DATA, "layer_1", 50,
+//   0});
+//   config.layerConfig.add_inputs();
+//   config.layerConfig.add_inputs();
+//
+//   // "GpuSparseMatrix" as label is not supported
+//   testLayerGrad(config,
+//                 "square_error",
+//                 100,
+//                 /* trans */ false,
+//                 /* useGpu */ false);
+// }
+//
+// TEST(Layer, sparse_float_square_error) {
+//   TestConfig config;
+//   config.layerConfig.set_type("square_error");
+//   config.biasSize = 0;
+//
+//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
+//   config.inputDefs.push_back({INPUT_SPARSE_FLOAT_VALUE_DATA, "layer_1", 50,
+//   0});
+//   config.layerConfig.add_inputs();
+//   config.layerConfig.add_inputs();
+//
+//   // "GpuSparseMatrix" as label is not supported
+//   testLayerGrad(config,
+//                 "square_error",
+//                 100,
+//                 /* trans */ false,
+//                 /* useGpu */ false);
+// }
+//
+// TEST(Layer, square_error_weighted) {
+//   TestConfig config;
+//   config.layerConfig.set_type("square_error");
+//   config.biasSize = 0;
+//   config.testAccumulate = false;
+//
+//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
+//   config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 10, 0});
+//   config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_2", 1, 0});
+//   config.layerConfig.add_inputs();
+//   config.layerConfig.add_inputs();
+//   config.layerConfig.add_inputs();
+//
+//   for (auto useGpu : {false, true}) {
+//     testLayerGrad(config, "square_error", 100, /* trans */ false, useGpu);
+//   }
+// }
+//
+// TEST(Layer, huber_two_class) {
+//   TestConfig config;
+//   config.layerConfig.set_type("huber");
+//   config.biasSize = 0;
+//
+//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
+//   config.inputDefs.push_back({INPUT_LABEL, "layer_1", 2, 0});
+//   config.layerConfig.add_inputs();
+//   config.layerConfig.add_inputs();
+//
+//   for (auto useGpu : {false, true}) {
+//     testLayerGrad(config, "huber", 100, /* trans */ false, useGpu);
+//   }
+// }
+//
+// void testExpandLayer(string trans_type, bool hasSubseq) {
+//   TestConfig config;
+//   config.layerConfig.set_type("expand");
+//
+//   config.inputDefs.push_back(
+//       {trans_type == "non-seq" ? INPUT_DENSE_DIM_DATA : INPUT_SEQUENCE_DATA,
+//        "layer_0",
+//        10,
+//        0});
+//   config.inputDefs.push_back(
+//       {hasSubseq ? INPUT_HASSUB_SEQUENCE_DATA : INPUT_SEQUENCE_DATA,
+//        "layer_1",
+//        10,
+//        0});
+//   config.layerConfig.add_inputs();
+//   config.layerConfig.add_inputs();
+//   config.layerConfig.set_trans_type(trans_type);
+//   LOG(INFO) << " trans_type=" << trans_type << " hasSubseq=" << hasSubseq;
+//
+//   for (auto useGpu : {false, true}) {
+//     testLayerGrad(config, "expand", 30, false, useGpu);
+//   }
+// }
+//
+// TEST(Layer, ExpandLayer) {
+//   testExpandLayer("non-seq", false);  // non-seq expand to seq
+//   testExpandLayer("non-seq", true);   // non-seq expand to hasSubseq
+//   testExpandLayer("seq", true);       // seq expand to hasSubseq
+// }
+//
+// void testDegradeLayer(bool hasSubseq, string layer_type, string trans_type) {
+//   TestConfig config;
+//   config.layerConfig.set_type(layer_type);
+//   config.layerConfig.set_size(10);
+//   config.biasSize = 0;
+//
+//   config.inputDefs.push_back(
+//       {hasSubseq ? INPUT_HASSUB_SEQUENCE_DATA : INPUT_SEQUENCE_DATA,
+//        "layer_0",
+//        10,
+//        0});
+//   config.layerConfig.add_inputs();
+//   config.layerConfig.set_trans_type(trans_type);
+//
+//   auto testDegradeLayerGrad = [](TestConfig& config, string layer_type) {
+//     for (auto useGpu : {false, true}) {
+//       testLayerGrad(config, layer_type, 100, false, useGpu);
+//     }
+//   };
+//
+//   if (layer_type == "average") {
+//     for (auto strategy : {"average", "sum", "squarerootn"}) {
+//       LOG(INFO) << " hasSubseq=" << hasSubseq << " trans_type=" << trans_type
+//                 << " average_strategy=" << strategy;
+//       config.layerConfig.set_average_strategy(strategy);
+//       testDegradeLayerGrad(config, layer_type);
+//     }
+//   } else {
+//     LOG(INFO) << " hasSubseq=" << hasSubseq << " trans_type=" << trans_type;
+//     testDegradeLayerGrad(config, layer_type);
+//   }
+// }
+//
+// TEST(Layer, MaxLayer) {
+//   testDegradeLayer(false, "max", "non-seq");  // seq max to non-seq
+//   testDegradeLayer(true, "max", "non-seq");   // hasSubseq max to non-seq
+//   testDegradeLayer(true, "max", "seq");       // hasSubseq max to seq
+// }
+//
+// TEST(Layer, SequenceLastInstanceLayer) {
+//   testDegradeLayer(false,
+//                    "seqlastins",
+//                    "non-seq");  // seq seqlastins to non-seq
+//   testDegradeLayer(true,
+//                    "seqlastins",
+//                    "non-seq");  // hasSubseq seqlastins to non-seq
+//   testDegradeLayer(true, "seqlastins", "seq");  // hasSubseq seqlastins to
+//   seq
+// }
+//
+// TEST(Layer, AverageLayer) {
+//   testDegradeLayer(false, "average", "non-seq");  // seq average to non-seq
+//   testDegradeLayer(true, "average", "non-seq");  // hasSubseq average to
+//   non-seq
+//   testDegradeLayer(true, "average", "seq");      // hasSubseq average to seq
+// }
+//
+// TEST(Layer, SequenceConcatLayer) {
+//   TestConfig config;
+//   config.layerConfig.set_type("seqconcat");
+//   config.layerConfig.set_size(10);
+//   config.biasSize = 0;
+//
+//   config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 10, 0});
+//   config.layerConfig.add_inputs();
+//   config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_1", 10, 0});
+//   config.layerConfig.add_inputs();
+//
+//   for (auto useGpu : {false, true}) {
+//     testLayerGrad(config, "seqconcat", 100, false, useGpu);
+//   }
+// }
+//
+// TEST(Layer, SequenceReshapeLayer) {
+//   TestConfig config;
+//   config.layerConfig.set_type("seqreshape");
+//   config.layerConfig.set_size(10);
+//
+//   config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 100, 0});
+//   config.layerConfig.add_inputs();
+//
+//   for (auto useGpu : {false, true}) {
+//     testLayerGrad(config, "seqreshape", 100, false, useGpu);
+//   }
+// }
+//
+// TEST(Layer, ConvShiftLayer) {
+//   TestConfig config;
+//   config.layerConfig.set_type("conv_shift");
+//   config.layerConfig.set_size(10);
+//
+//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
+//   config.inputDefs.push_back({INPUT_DATA, "layer_1", 3, 0});
+//   config.layerConfig.add_inputs();
+//   config.layerConfig.add_inputs();
+//
+//   // Not support GPU now
+//   testLayerGrad(config, "conv_shift", 100, false, false);
+// }
+//
+// TEST(Layer, PowerLayer) {
+//   TestConfig config;
+//   config.layerConfig.set_type("power");
+//   config.layerConfig.set_size(10);
+//
+//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
+//   config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
+//   config.layerConfig.add_inputs();
+//   config.layerConfig.add_inputs();
+//
+//   for (auto useGpu : {false, true}) {
+//     testLayerGrad(config, "power", 100, false, useGpu);
+//   }
+// }
+//
+// TEST(Layer, ConvexCombinationLayer) {
+//   TestConfig config;
+//   config.layerConfig.set_type("convex_comb");
+//   config.layerConfig.set_size(20);
+//   config.biasSize = 0;
+//
+//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 5, 0});
+//   config.inputDefs.push_back({INPUT_DATA, "layer_1", 100, 0});
+//   config.layerConfig.add_inputs();
+//   config.layerConfig.add_inputs();
+//
+//   for (auto useGpu : {false, true}) {
+//     testLayerGrad(config, "convex_comb", 100, false, useGpu);
+//   }
+// }
+//
+// TEST(Layer, InterpolationLayer) {
+//   TestConfig config;
+//   config.layerConfig.set_type("interpolation");
+//   config.layerConfig.set_size(10);
+//   config.biasSize = 0;
+//
+//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
+//   config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
+//   config.inputDefs.push_back({INPUT_DATA, "layer_2", 10, 0});
+//   config.layerConfig.add_inputs();
+//   config.layerConfig.add_inputs();
+//   config.layerConfig.add_inputs();
+//
+//   for (auto useGpu : {false, true}) {
+//     testLayerGrad(config, "interpolation", 100, false, useGpu);
+//   }
+// }
+//
+// TEST(Layer, OuterProdLayer) {
+//   TestConfig config;
+//   config.layerConfig.set_type("out_prod");
+//   config.layerConfig.set_size(100);
+//
+//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
+//   config.layerConfig.add_inputs();
+//   config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
+//   config.layerConfig.add_inputs();
+//
+//   for (auto useGpu : {false, true}) {
+//     testLayerGrad(config, "out_prod", 100, false, useGpu);
+//   }
+// }
+//
+// TEST(Layer, SlopeInterceptLayer) {
+//   TestConfig config;
+//   config.layerConfig.set_type("slope_intercept");
+//   config.layerConfig.set_size(10);
+//   config.layerConfig.set_slope(1.0);
+//   config.layerConfig.set_intercept(0.1);
+//
+//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
+//   config.layerConfig.add_inputs();
+//
+//   for (auto useGpu : {false, true}) {
+//     testLayerGrad(config, "slope_intercept", 100, false, useGpu);
+//   }
+// }
+//
+// TEST(Layer, ScalingLayer) {
+//   TestConfig config;
+//   config.layerConfig.set_type("scaling");
+//   config.layerConfig.set_size(10);
+//   config.biasSize = 0;
+//
+//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
+//   config.layerConfig.add_inputs();
+//   config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
+//   config.layerConfig.add_inputs();
+//
+//   for (auto useGpu : {false, true}) {
+//     testLayerGrad(config, "scaling", 100, false, useGpu);
+//   }
+// }
+//
+// void testNormLayer(const string& normType, bool trans, bool useGpu) {
+//   TestConfig config;
+//   config.layerConfig.set_type("norm");
+//   config.layerConfig.set_active_type("relu");
+//
+//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 1568, 0});
+//   LayerInputConfig* input = config.layerConfig.add_inputs();
+//   NormConfig* norm = input->mutable_norm_conf();
+//   norm->set_norm_type(normType);
+//   norm->set_channels(16);
+//   norm->set_size(5);
+//   norm->set_scale(0.001);
+//   norm->set_pow(0.75);
+//   norm->set_blocked(0);
+//   norm->set_img_size(14);
+//   norm->set_img_size_y(7);
+//   norm->set_output_x(norm->img_size());
+//   norm->set_output_y(norm->img_size_y());
+//   if (norm->norm_type() == "cmrnorm" ||
+//       norm->norm_type() == "cmrnorm-projection") {
+//     norm->set_scale(norm->scale() / norm->size());
+//   } else {
+//     norm->set_scale(norm->scale() / (norm->size() * norm->size()));
+//   }
+//
+//   config.layerConfig.set_size(norm->output_x() * norm->output_y() *
+//                               norm->channels());
+//   config.biasSize = 0;
+//
+//   testLayerGrad(config, "norm", 100, trans, useGpu);
+// }
+//
+// TEST(Layer, NormLayer) {
+//   testNormLayer("cmrnorm-projection", /* trans= */ false, /* useGpu= */
+//   true);
+//   testNormLayer("cmrnorm-projection", /* trans= */ false, /* useGpu= */
+//   false);
+// }
+//
+// void setPoolConfig(TestConfig* config,
+//                    PoolConfig* pool,
+//                    const string& poolType) {
+//   (*config).biasSize = 0;
+//   (*config).layerConfig.set_type("pool");
+//   (*config).layerConfig.set_num_filters(16);
+//
+//   int kw = 3, kh = 3;
+//   int pw = 0, ph = 0;
+//   int sw = 2, sh = 2;
+//   pool->set_pool_type(poolType);
+//   pool->set_channels(16);
+//   pool->set_size_x(kw);
+//   pool->set_size_y(kh);
+//   pool->set_start(0);
+//   pool->set_padding(pw);
+//   pool->set_padding_y(ph);
+//   pool->set_stride(sw);
+//   pool->set_stride_y(sh);
+//
+//   int ow = outputSize(pool->img_size(), kw, pw, sw, /* caffeMode */ false);
+//   int oh = outputSize(pool->img_size_y(), kh, ph, sh, /* caffeMode */ false);
+//   pool->set_output_x(ow);
+//   pool->set_output_y(oh);
+// }
+//
+// void testPoolLayer(const string& poolType, bool trans, bool useGpu) {
+//   TestConfig config;
+//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 3136, 0});
+//   LayerInputConfig* input = config.layerConfig.add_inputs();
+//   PoolConfig* pool = input->mutable_pool_conf();
+//
+//   pool->set_img_size(14);
+//   pool->set_img_size_y(14);
+//   setPoolConfig(&config, pool, poolType);
+//   config.layerConfig.set_size(pool->output_x() * pool->output_y() *
+//                               pool->channels());
+//
+//   testLayerGrad(config, "pool", 100, trans, useGpu);
+// }
+//
+// #ifndef PADDLE_ONLY_CPU
+// void testPoolLayer2(const string& poolType, bool trans, bool useGpu) {
+//   TestConfig config;
+//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 3200, 0});
+//   LayerInputConfig* input = config.layerConfig.add_inputs();
+//   PoolConfig* pool = input->mutable_pool_conf();
+//
+//   pool->set_size_y(4);
+//   pool->set_stride_y(3);
+//   pool->set_img_size(10);
+//   pool->set_img_size_y(20);
+//   setPoolConfig(&config, pool, poolType);
+//   pool->set_output_y((pool->img_size_y() - pool->start() - pool->size_y()) /
+//                          ((float)pool->stride_y()) +
+//                      1.5);
+//   config.layerConfig.set_size(pool->output_x() * pool->output_y() *
+//                               pool->channels());
+//
+//   testLayerGrad(config, "pool", 100, trans, useGpu);
+// }
+// #endif
+//
+// TEST(Layer, PoolLayer) {
+//   testPoolLayer("avg-projection", /* trans= */ false, /* useGpu= */ false);
+//   testPoolLayer("max-projection", /* trans= */ false, /* useGpu= */ false);
+//
+// #ifndef PADDLE_ONLY_CPU
+//   testPoolLayer("avg-projection", /* trans= */ false, /* useGpu= */ true);
+//   testPoolLayer("max-projection", /* trans= */ false, /* useGpu= */ true);
+//   testPoolLayer("cudnn-max-pool", /* trans= */ false, /* useGpu= */ true);
+//   testPoolLayer("cudnn-avg-pool", /* trans= */ false, /* useGpu= */ true);
+//   testPoolLayer2("cudnn-max-pool", /* trans= */ false, /* useGpu= */ true);
+//   testPoolLayer2("cudnn-avg-pool", /* trans= */ false, /* useGpu= */ true);
+// #endif
+// }
+//
+// void testSppLayer(const string& poolType,
+//                   const int pyramidHeight,
+//                   bool trans,
+//                   bool useGpu) {
+//   TestConfig config;
+//   config.layerConfig.set_type("spp");
+//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 3200, 0});
+//   LayerInputConfig* input = config.layerConfig.add_inputs();
+//   SppConfig* sppConfig = input->mutable_spp_conf();
+//   sppConfig->set_pool_type(poolType);
+//   sppConfig->set_pyramid_height(pyramidHeight);
+//   ImageConfig* imageConfig = sppConfig->mutable_image_conf();
+//   imageConfig->set_channels(16);
+//   imageConfig->set_img_size(10);
+//   imageConfig->set_img_size_y(20);
+//   int outputSize = (std::pow(4, sppConfig->pyramid_height()) - 1) / (4 - 1);
+//   config.layerConfig.set_size(outputSize * imageConfig->channels());
+//   testLayerGrad(config, "spp", 100, trans, useGpu);
+// }
+//
+// TEST(Layer, SpatialPyramidPoolLayer) {
+//   for (auto useGpu : {false, true}) {
+//     for (auto pyramidHeight : {1, 2, 3}) {
+//       testSppLayer("avg-projection", pyramidHeight, false, useGpu);
+//       testSppLayer("max-projection", pyramidHeight, false, useGpu);
+//     }
+//   }
+// }
+//
+// TEST(Layer, rankCostLayer) {
+//   TestConfig config;
+//   config.layerConfig.set_type("rank-cost");
+//   config.biasSize = 0;
+//
+//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
+//   config.inputDefs.push_back({INPUT_DATA, "layer_1", 1, 0});
+//   config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_2", 1, 0});
+//   config.layerConfig.add_inputs();
+//   config.layerConfig.add_inputs();
+//   config.layerConfig.add_inputs();
+//
+//   for (auto useGpu : {false, true}) {
+//     testLayerGrad(config, "rank-cost", 100, false, useGpu);
+//   }
+// }
+//
+// TEST(Layer, sumCostLayer) {
+//   TestConfig config;
+//   config.layerConfig.set_type("sum_cost");
+//   config.biasSize = 0;
+//
+//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
+//   config.layerConfig.add_inputs();
+//
+//   for (auto useGpu : {false, true}) {
+//     testLayerGrad(config, "sum_cost", 100, false, useGpu);
+//   }
+// }
+//
+// TEST(Layer, weightedRankCostLayer) {
+//   TestConfig config;
+//   config.layerConfig.set_type("rank-cost");
+//   config.biasSize = 0;
+//
+//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
+//   config.inputDefs.push_back({INPUT_DATA, "layer_1", 1, 0});
+//   config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_2", 1, 0});
+//   config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_3", 1, 0});
+//   config.layerConfig.add_inputs();
+//   config.layerConfig.add_inputs();
+//   config.layerConfig.add_inputs();
+//   config.layerConfig.add_inputs();
+//
+//   for (auto useGpu : {false, true}) {
+//     testLayerGrad(config, "weighted-rank-cost", 100, false, useGpu);
+//   }
+// }
+//
+// TEST(Layer, TensorLayer) {
+//   TestConfig config;
+//   config.layerConfig.set_type("tensor");
+//   config.layerConfig.set_size(10);
+//   config.layerConfig.set_active_type("sigmoid");
+//   config.biasSize = config.layerConfig.size();
+//
+//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 5, 250});
+//   config.inputDefs.push_back({INPUT_DATA, "layer_1", 5, 0});
+//   config.layerConfig.add_inputs();
+//   config.layerConfig.add_inputs();
+//
+//   for (auto useGpu : {false, true}) {
+//     testLayerGrad(config, "tensor", 100, false, useGpu);
+//   }
+// }
+//
+// TEST(Layer, RecurrentLayer) {
+//   TestConfig config;
+//   config.layerConfig.set_type("recurrent");
+//   config.layerConfig.set_size(4);
+//   config.layerConfig.set_active_type("tanh");
+//   config.biasSize = 4;
+//
+//   config.inputDefs.push_back(
+//       {INPUT_SEQUENCE_DATA, "layer_0", /* dim= */ 4, /* paraSize= */ 16});
+//   config.layerConfig.add_inputs();
+//
+//   for (auto useGpu : {false, true}) {
+//     for (auto reversed : {false, true}) {
+//       config.layerConfig.set_reversed(reversed);
+//       config.testState = !reversed;
+//       testLayerGrad(config, "recurrent", 50, /* trans= */ false, useGpu);
+//     }
+//   }
+// }
+//
+// TEST(Layer, LstmLayer) {
+//   TestConfig config;
+//   config.layerConfig.set_type("lstmemory");
+//   config.layerConfig.set_size(4);
+//   config.layerConfig.set_active_type("tanh");
+//   config.layerConfig.set_active_state_type("sigmoid");
+//   config.layerConfig.set_active_gate_type("sigmoid");
+//   config.biasSize = 28;
+//
+//   config.inputDefs.push_back(
+//       {INPUT_SEQUENCE_DATA, "layer_0", /* dim= */ 16, /* paraSize= */ 64});
+//   config.layerConfig.add_inputs();
+//
+//   for (auto useGpu : {false, true}) {
+//     for (auto reversed : {false, true}) {
+//       config.layerConfig.set_reversed(reversed);
+//       config.testState = !reversed;
+//       testLayerGrad(config, "lstmemory", 100, /* trans= */ false, useGpu);
+//     }
+//   }
+//   for (auto useGpu : {true}) {
+//     config.testBatchState = true;
+//     config.layerConfig.set_reversed(false);
+//     testLayerGrad(config, "lstmemory", 10, /* trans= */ false, useGpu);
+//   }
+// }
+//
+// TEST(Layer, MDLstmLayer) {
+//   TestConfig config;
+//   config.layerConfig.set_type("mdlstmemory");
+//   config.layerConfig.set_size(4);
+//   config.layerConfig.set_active_type("sigmoid");
+//   config.layerConfig.set_active_state_type("sigmoid");
+//   config.layerConfig.set_active_gate_type("sigmoid");
+//   config.biasSize = 4 * 9;
+//
+//   config.inputDefs.push_back(
+//       {INPUT_SEQUENCE_MDIM_DATA, "layer_0", 4 * 5, 4 * 4 * 5});
+//   config.layerConfig.add_inputs();
+//   config.layerConfig.add_directions(true);
+//   config.layerConfig.add_directions(true);
+//
+//   for (auto useGpu : {false, true}) {
+//     for (int i = 0; i < 2; i++) {
+//       for (int j = 0; j < 2; j++) {
+//         config.layerConfig.set_directions(0, bool(i));
+//         config.layerConfig.set_directions(1, bool(j));
+//         testLayerGrad(config, "mdlstmemory", 100, false, useGpu);
+//       }
+//     }
+//   }
+// }
+//
+// TEST(Layer, ParameterReluLayer) {
+//   auto testParameterReluLayer = [&](size_t inputSize, size_t channels) {
+//     TestConfig config;
+//     config.layerConfig.set_type("prelu");
+//     config.inputDefs.push_back({INPUT_DATA, "layer_0", inputSize, channels});
+//     config.layerConfig.add_inputs();
+//     config.layerConfig.set_size(inputSize);
+//     config.layerConfig.set_partial_sum(inputSize /
+//                                        channels);  // size of feature map
+//     for (auto useGpu : {false, true}) {
+//       testLayerGrad(config, "prelu", 100, false, useGpu);
+//     }
+//   };
+//
+//   testParameterReluLayer(192, 1);
+//   testParameterReluLayer(192, 3);
+//   testParameterReluLayer(192, 192);
+// }
+//
+// TEST(Layer, ResizeLayer) {
+//   TestConfig config;
+//   config.biasSize = 0;
+//   config.layerConfig.set_type("resize");
+//   config.layerConfig.set_size(64);
+//
+//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 16, 0});
+//   config.layerConfig.add_inputs();
+//
+//   for (auto useGpu : {false, true}) {
+//     testLayerGrad(config, "resize", 100, false, useGpu);
+//   }
+// }
+//
+// TEST(Layer, NCELayer) {
+//   TestConfig config;
+//   size_t numClasses = 4;
+//   config.layerConfig.set_type("nce");
+//   config.layerConfig.set_size(1);
+//   config.layerConfig.set_active_type("sigmoid");
+//   config.layerConfig.set_num_classes(numClasses);
+//   config.biasSize = numClasses;
+//
+//   config.inputDefs.push_back(
+//       {INPUT_DATA, "layer_0", /* dim= */ 16, /* paraSize= */ 16 *
+//       numClasses});
+//   config.inputDefs.push_back(
+//       {INPUT_LABEL, "label", /* dim= */ numClasses, /* paraSize= */ 0});
+//   config.layerConfig.add_inputs();
+//   config.layerConfig.add_inputs();
+//
+//   for (auto withWeight : {false, true}) {
+//     if (withWeight) {
+//       config.inputDefs.push_back(
+//           {INPUT_DATA_TARGET, "weight", /* dim= */ 1, /* paraSize= */ 0});
+//       config.layerConfig.add_inputs();
+//     }
+//
+//     for (auto isIdLabel : {false, true}) {
+//       config.inputDefs[1] = {
+//           isIdLabel ? INPUT_LABEL : INPUT_SPARSE_NON_VALUE_DATA,
+//           "label",
+//           /* dim= */ numClasses,
+//           /* paraSize= */ 0};
+//
+//       for (auto withDist : {false, true}) {
+//         config.layerConfig.clear_neg_sampling_dist();
+//         if (withDist) {
+//           double sum = 0;
+//           for (size_t i = 0; i < numClasses; ++i) {
+//             real p = rand();  // NOLINT use rand_r
+//             config.layerConfig.add_neg_sampling_dist(p);
+//             sum += p;
+//           }
+//           for (size_t i = 0; i < numClasses; ++i) {
+//             real p = config.layerConfig.neg_sampling_dist(i) / sum;
+//             config.layerConfig.set_neg_sampling_dist(i, p);
+//           }
+//         }
+//         LOG(INFO) << "NCELayer "
+//                   << " isIdLabel=" << isIdLabel << " withWeight=" <<
+//                   withWeight
+//                   << " withDist=" << withDist;
+//         // Not support GPU now
+//         testLayerGrad(config,
+//                       "nce",
+//                       100,
+//                       /* trans= */ false,
+//                       /* useGpu */ false);
+//       }
+//     }
+//   }
+// }
+//
+// TEST(Layer, GatedRecurrentLayer) {
+//   TestConfig config;
+//   config.layerConfig.set_type("gated_recurrent");
+//   config.layerConfig.set_size(4);
+//   config.layerConfig.set_active_type("sigmoid");
+//   config.layerConfig.set_active_gate_type("sigmoid");
+//   config.biasSize = 12;
+//
+//   config.inputDefs.push_back(
+//       {INPUT_SEQUENCE_DATA, "layer_0", /* dim= */ 12, /* paraSize= */ 48});
+//   config.layerConfig.add_inputs();
+//
+//   for (auto useGpu : {false, true}) {
+//     for (auto reversed : {false, true}) {
+//       config.layerConfig.set_reversed(reversed);
+//       config.testState = !reversed;
+//       testLayerGrad(config, "gated_recurrent", 100, /* trans= */ false,
+//       useGpu);
+//     }
+//   }
+// }
+//
+// TEST(Layer, GruStepLayer) {
+//   TestConfig config;
+//   config.layerConfig.set_type("gru_step");
+//   config.layerConfig.set_size(4);
+//   config.layerConfig.set_active_type("sigmoid");
+//   config.layerConfig.set_active_gate_type("sigmoid");
+//   config.biasSize = 12;
+//
+//   config.inputDefs.push_back(
+//       {INPUT_DATA, "layer_0", /* dim= */ 12, /* paraSize= */ 48});
+//   config.inputDefs.push_back(
+//       {INPUT_DATA, "layer_1", /* dim= */ 4, /* paraSize= */ 0});
+//   config.layerConfig.add_inputs();
+//   config.layerConfig.add_inputs();
+//
+//   for (auto useGpu : {false, true}) {
+//     testLayerGrad(config, "gruStep", 100, /* trans= */ false, useGpu);
+//   }
+// }
+//
+// TEST(Layer, LstmStepLayer) {
+//   TestConfig config;
+//   config.layerConfig.set_type("lstm_step");
+//   config.layerConfig.set_size(4);
+//   config.layerConfig.set_active_type("sigmoid");
+//   config.layerConfig.set_active_state_type("sigmoid");
+//   config.layerConfig.set_active_gate_type("sigmoid");
+//   config.biasSize = 12;
+//   config.testAccumulate = false;
+//
+//   config.inputDefs.push_back(
+//       {INPUT_DATA, "layer_0", /* dim= */ 16, /* paraSize= */ 0});
+//   config.inputDefs.push_back(
+//       {INPUT_DATA, "layer_1", /* dim= */ 4, /* paraSize= */ 0});
+//   config.layerConfig.add_inputs();
+//   config.layerConfig.add_inputs();
+//
+//   for (auto useGpu : {false, true}) {
+//     testLayerGrad(config, "lstmStep", 100, /* trans= */ false, useGpu);
+//   }
+// }
+//
+// void testBatchNormLayer(const string& type, bool trans, bool useGpu) {
+//   TestConfig config;
+//   const int CHANNELS = 10;
+//   const int IMG_SIZE = 16;
+//   const int IMG_SIZE_Y = 8;
+//   size_t size = CHANNELS * IMG_SIZE * IMG_SIZE_Y;
+//   config.layerConfig.set_type(type);
+//   config.layerConfig.set_size(size);
+//   config.layerConfig.set_active_type("sigmoid");
+//   config.biasSize = CHANNELS;
+//   config.inputDefs.push_back({INPUT_DATA,
+//                               "layer_0",
+//                               /* dim= */ size,
+//                               /* paraSize= */ CHANNELS});
+//
+//   config.inputDefs.push_back({INPUT_DATA, "layer_1_running_mean", 1,
+//   CHANNELS});
+//   config.inputDefs.back().isStatic = true;
+//   config.inputDefs.push_back({INPUT_DATA, "layer_2_running_var", 1,
+//   CHANNELS});
+//   config.inputDefs.back().isStatic = true;
+//
+//   LayerInputConfig* input = config.layerConfig.add_inputs();
+//   config.layerConfig.add_inputs();
+//   config.layerConfig.add_inputs();
+//
+//   ImageConfig* img_conf = input->mutable_image_conf();
+//   img_conf->set_channels(CHANNELS);
+//   img_conf->set_img_size(IMG_SIZE);
+//   img_conf->set_img_size_y(IMG_SIZE_Y);
+//
+//   testLayerGrad(config,
+//                 "batch_norm",
+//                 64,
+//                 /* trans= */ trans,
+//                 useGpu,
+//                 /* useWeight */ true);
+// }
+//
+// TEST(Layer, BatchNormalizationLayer) {
+//   testBatchNormLayer("batch_norm", false, false);
+// #ifndef PADDLE_ONLY_CPU
+//   testBatchNormLayer("batch_norm", false, true);
+//   if (hl_get_cudnn_lib_version() >= int(4000)) {
+//     testBatchNormLayer("cudnn_batch_norm", false, true);
+//   }
+// #endif
+// }
+//
+// TEST(Operator, conv) {
+//   TestConfig config;
+//   const int NUM_FILTERS = 16;
+//   const int FILTER_SIZE = 2;
+//   const int FILTER_SIZE_Y = 3;
+//   const int CHANNELS = 3;
+//   const int IMAGE_SIZE = 16;
+//   const int IMAGE_SIZE_Y = 8;
+//   OperatorConfig& operatorConf = *config.layerConfig.add_operator_confs();
+//   operatorConf.set_type("conv");
+//   ConvConfig* conv = operatorConf.mutable_conv_conf();
+//   operatorConf.set_num_filters(NUM_FILTERS);
+//   conv->set_filter_size(FILTER_SIZE);
+//   conv->set_filter_size_y(FILTER_SIZE_Y);
+//   conv->set_channels(CHANNELS);
+//   conv->set_padding(0);
+//   conv->set_padding_y(1);
+//   conv->set_stride(2);
+//   conv->set_stride_y(2);
+//   conv->set_groups(1);
+//   conv->set_filter_channels(conv->channels() / conv->groups());
+//   conv->set_img_size(IMAGE_SIZE);
+//   conv->set_img_size_y(IMAGE_SIZE_Y);
+//   conv->set_output_x(outputSize(conv->img_size(),
+//                                 conv->filter_size(),
+//                                 conv->padding(),
+//                                 conv->stride(),
+//                                 /*  caffeMode */ true));
+//   conv->set_output_y(outputSize(conv->img_size_y(),
+//                                 conv->filter_size_y(),
+//                                 conv->padding_y(),
+//                                 conv->stride_y(),
+//                                 /*  caffeMode */ true));
+//   config.layerConfig.set_size(conv->output_x() * conv->output_y() *
+//                               NUM_FILTERS);
+//
+//   config.inputDefs.push_back(
+//       {INPUT_DATA, "layer_0", IMAGE_SIZE * IMAGE_SIZE_Y * CHANNELS, 0});
+//   config.inputDefs.push_back(
+//       {INPUT_DATA,
+//        "layer_1",
+//        FILTER_SIZE * FILTER_SIZE_Y * CHANNELS * NUM_FILTERS,
+//        0});
+//   config.layerConfig.add_inputs();
+//   config.layerConfig.add_inputs();
+//
+//   testOperatorGrad(config, operatorConf, 100, /*useGpu*/ true, false);
+// }
+//
+// TEST(Layer, FeatureMapExpandLayer) {
+//   TestConfig config;
+//   config.layerConfig.set_type("featmap_expand");
+//   const int CHANNELS = 10;
+//   const int INPUT_SIZE = 100;
+//   config.layerConfig.set_size(INPUT_SIZE * CHANNELS);
+//   config.layerConfig.set_num_filters(CHANNELS);
+//   config.inputDefs.push_back({INPUT_SEQUENCE_DATA,
+//                               "layer_0",
+//                               /* dim= */ INPUT_SIZE,
+//                               /* paraSize= */ 0});
+//   config.layerConfig.add_inputs();
+//   for (auto useGpu : {false, true}) {
+//     testLayerGrad(config,
+//                   "featmap_expand",
+//                   /*batch_size*/ 100,
+//                   /* trans= */ false,
+//                   useGpu,
+//                   /* useWeight */ true);
+//   }
+// }
+//
+// TEST(Layer, MultiplexLayer) {
+//   TestConfig config;
+//   const int LAYER_SIZE = 100;
+//   config.layerConfig.set_type("multiplex");
+//   config.layerConfig.set_size(LAYER_SIZE);
+//
+//   config.inputDefs.push_back({INPUT_LABEL, "layer_0", 2, 0});
+//   config.inputDefs.push_back(
+//       {INPUT_DATA, "layer_1", /* dim= */ LAYER_SIZE, /* paraSize= */ 0});
+//   config.inputDefs.push_back(
+//       {INPUT_DATA, "layer_2", /* dim= */ LAYER_SIZE, /* paraSize= */ 0});
+//   config.layerConfig.add_inputs();
+//   config.layerConfig.add_inputs();
+//   config.layerConfig.add_inputs();
+//
+//   for (auto useGpu : {false, true}) {
+//     testLayerGrad(config, "multiplex", 512, /* trans= */ false, useGpu);
+//   }
+// }
+//
+TEST(Layer, PadLayer) {
   TestConfig config;
-  config.layerConfig.set_size(10);
-
-  config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
-  config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
-  config.layerConfig.add_inputs();
-  config.layerConfig.add_inputs();
-
-  OperatorConfig& operatorConf = *config.layerConfig.add_operator_confs();
-  operatorConf.set_type("dot_mul");
-  operatorConf.set_dotmul_scale(-1);
-
-  testOperatorGrad(config, operatorConf, 100, false, false);
-}
-
-TEST(Projection, context) {
-  for (auto contextStart : {-5, -3, -1, 0, 3}) {
-    for (auto contextLength : {1, 2, 5, 7}) {
-      for (auto batchSize : {1, 2, 5, 20, 50}) {
-        for (auto trainablePadding : {false, true}) {
-          LOG(INFO) << " contextStart=" << contextStart
-                    << " contextLength=" << contextLength
-                    << " batchSize=" << batchSize
-                    << " trainablePadding=" << trainablePadding;
-          ProjectionConfig conf;
-          conf.set_type("context");
-          conf.set_input_size(10);
-          conf.set_context_start(contextStart);
-          conf.set_context_length(contextLength);
-          conf.set_trainable_padding(trainablePadding);
-          conf.set_output_size(conf.context_length() * conf.input_size());
-          int pad =
-              std::max(0, -conf.context_start()) +
-              std::max(0, conf.context_start() + conf.context_length() - 1);
-          for (auto useGpu : {false, true}) {
-            testProjectionGrad(
-                conf,
-                INPUT_SEQUENCE_DATA,
-                trainablePadding ? conf.input_size() * pad : 0,
-                batchSize,
-                useGpu,
-                contextStart + contextLength <= 1);  // = testState
-          }
-        }
-      }
-    }
-  }
-}
-
-TEST(Projection, trans_fc) {
-  ProjectionConfig conf;
-  conf.set_type("trans_fc");
-  conf.set_input_size(50);
-  conf.set_output_size(20);
-  for (auto useGpu : {false, true}) {
-    testProjectionGrad(conf,
-                       INPUT_DATA,
-                       /* parameterSize */ 1000,
-                       /* batchSize */ 100,
-                       useGpu);
-  }
-}
-
-TEST(Projection, fc) {
-  ProjectionConfig conf;
-  conf.set_type("fc");
-  conf.set_input_size(10);
-  conf.set_output_size(20);
-  for (auto useGpu : {false, true}) {
-    testProjectionGrad(conf,
-                       INPUT_DATA,
-                       /* parameterSize */ 200,
-                       /* batchSize */ 100,
-                       useGpu);
-  }
-}
-
-TEST(Projection, dot_mul) {
-  ProjectionConfig conf;
-  conf.set_type("dot_mul");
-  conf.set_input_size(20);
-  conf.set_output_size(20);
-  for (auto useGpu : {false, true}) {
-    testProjectionGrad(conf,
-                       INPUT_DATA,
-                       /* parameterSize */ 20,
-                       /* batchSize */ 100,
-                       useGpu);
-  }
-}
-
-TEST(Projection, table) {
-  ProjectionConfig conf;
-  conf.set_type("table");
-  conf.set_input_size(10);
-  conf.set_output_size(20);
-  for (auto useGpu : {false, true}) {
-    testProjectionGrad(conf,
-                       INPUT_LABEL,
-                       /* parameterSize */ 200,
-                       /* batchSize */ 100,
-                       useGpu);
-  }
-}
-
-TEST(Projection, identity) {
-  ProjectionConfig conf;
-  conf.set_type("identity");
-  conf.set_input_size(10);
-  conf.set_output_size(10);
-  for (auto useGpu : {false, true}) {
-    testProjectionGrad(conf,
-                       INPUT_DATA,
-                       /* parameterSize */ 0,
-                       /* batchSize */ 100,
-                       useGpu);
-  }
-}
-
-TEST(Projection, scaling) {
-  ProjectionConfig conf;
-  conf.set_type("scaling");
-  conf.set_input_size(10);
-  conf.set_output_size(10);
-  for (auto useGpu : {false}) {
-    testProjectionGrad(conf,
-                       INPUT_DATA,
-                       /* parameterSize */ 1,
-                       /* batchSize */ 100,
-                       useGpu);
-  }
-}
-
-void testProjectionConv(size_t groups) {
-  const int NUM_FILTERS = 18;
-  const int FILTER_SIZE = 2;
-  const int FILTER_SIZE_Y = 3;
-  const int CHANNELS = 3;
-  const int IMAGE_SIZE = 16;
-
-  ProjectionConfig conf;
-  conf.set_type("conv");
-  conf.set_num_filters(NUM_FILTERS);
-
-  ConvConfig* conv = conf.mutable_conv_conf();
-  conv->set_filter_size(FILTER_SIZE);
-  conv->set_filter_size_y(FILTER_SIZE_Y);
-  conv->set_channels(CHANNELS);
-  conv->set_padding(0);
-  conv->set_padding_y(1);
-  conv->set_stride(2);
-  conv->set_stride_y(2);
-  conv->set_groups(groups);
-  conv->set_filter_channels(conv->channels() / conv->groups());
-  conv->set_img_size(IMAGE_SIZE);
-  int output_x = outputSize(conv->img_size(),
-                            conv->filter_size(),
-                            conv->padding(),
-                            conv->stride(),
-                            /* caffeMode */ true);
-  int output_y = outputSize(conv->img_size(),
-                            conv->filter_size_y(),
-                            conv->padding_y(),
-                            conv->stride_y(),
-                            /* caffeMode */ true);
-  conv->set_output_x(output_x);
-  conf.set_input_size(IMAGE_SIZE * IMAGE_SIZE * CHANNELS);
-  conf.set_output_size(output_x * output_y * NUM_FILTERS);
-
-  testProjectionGrad(conf,
-                     INPUT_DATA,
-                     /* parameterSize */ NUM_FILTERS * CHANNELS * FILTER_SIZE *
-                         FILTER_SIZE_Y / groups,
-                     /* batchSize */ 100,
-                     true,
-                     false,
-                     NUM_FILTERS,
-                     true);
-}
-
-#ifndef PADDLE_ONLY_CPU
-TEST(Projection, conv) {
-  testProjectionConv(1);
-  testProjectionConv(3);
-}
-#endif
-
-TEST(Layer, BilinearInterpLayer) {
-  TestConfig config;
-  config.layerConfig.set_type("bilinear_interp");
-  config.biasSize = 0;
-  config.inputDefs.push_back({INPUT_DATA, "layer_0", 4096, 0});
-
-  LayerInputConfig* input = config.layerConfig.add_inputs();
-  BilinearInterpConfig* bilinear = input->mutable_bilinear_interp_conf();
-  ImageConfig* image = bilinear->mutable_image_conf();
-  image->set_img_size(32);
-  image->set_img_size_y(32);
-  image->set_channels(4);
-
-  for (auto useGpu : {false, true}) {
-    for (auto outSize : {32, 64}) {
-      bilinear->set_out_size_x(outSize);
-      bilinear->set_out_size_y(outSize);
-      testLayerGrad(config, "bilinear_interp", 10, false, useGpu);
-    }
-  }
-}
-
-TEST(Layer, concat) {
-  TestConfig config;
-  config.biasSize = 0;
-  config.layerConfig.set_type("concat");
-  config.layerConfig.set_size(15);
-  config.layerConfig.set_active_type("sigmoid");
-
-  config.inputDefs.push_back({INPUT_DATA, "layer_0", 5, 0});
-  config.layerConfig.add_inputs();
-  config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
-  config.layerConfig.add_inputs();
-
-  for (auto useGpu : {false, true}) {
-    testLayerGrad(config, "concat", 100, false, useGpu);
-  }
-}
-
-TEST(Layer, AddtoLayer) {
-  TestConfig config;
-  config.biasSize = 0;
-  config.layerConfig.set_type("addto");
-  config.layerConfig.set_size(10);
-  config.layerConfig.set_active_type("sigmoid");
-
-  config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
-  config.layerConfig.add_inputs();
-  config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
-  config.layerConfig.add_inputs();
-
-  for (auto useGpu : {false, true}) {
-    testLayerGrad(config, "addto", 100, false, useGpu);
-  }
-}
-
-TEST(Layer, CRFLayer) {
-  TestConfig config;
-  config.layerConfig.set_type("crf");
-  config.layerConfig.set_size(10);
-  config.biasSize = 0;
-
-  config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 10, 120});
-  config.inputDefs.push_back({INPUT_SEQUENCE_LABEL, "layer_1", 10, 0});
-  config.layerConfig.add_inputs();
-  config.layerConfig.add_inputs();
-
-  // Not support GPU now
-  testLayerGrad(config,
-                "crf",
-                100,
-                /* trans */ false,
-                /* useGpu */ false,
-                false /*useWeight*/,
-                0.03 /*epsilon*/);
-}
-
-TEST(Layer, CTCLayer) {
-  TestConfig config;
-  config.layerConfig.set_type("ctc");
-  config.layerConfig.set_norm_by_times(false);
-  config.layerConfig.set_size(10);
-  config.biasSize = 0;
-
-  config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 10, 0});
-  config.inputDefs.push_back({INPUT_SEQUENCE_LABEL, "layer_1", 10, 0});
-  config.layerConfig.add_inputs();
-  config.layerConfig.add_inputs();
-
-  for (auto useGpu : {false, true}) {
-    testLayerGrad(config, "ctc", 100, /* trans */ false, /* useGpu */ useGpu);
-  }
-}
-
-TEST(Layer, cosSimLayer) {
-  TestConfig config;
-  config.layerConfig.set_type("cos");
-  config.layerConfig.set_size(1);
   config.biasSize = 0;
+  config.layerConfig.set_type("pad");
 
-  config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
-  config.inputDefs.push_back({INPUT_DATA, "layer_1", 50, 0});
-  config.layerConfig.add_inputs();
-  config.layerConfig.add_inputs();
-
-  for (auto useGpu : {false, true}) {
-    testLayerGrad(config, "cos", 100, false, useGpu);
-  }
-}
-
-TEST(Layer, CosSimVecMatLayer) {
-  TestConfig config;
-  config.layerConfig.set_type("cos_vm");
-  config.layerConfig.set_size(5);  // output size
-  config.layerConfig.set_cos_scale(2.0);
-
-  config.inputDefs.push_back({INPUT_DATA, "layer_0", 20, 0});
-  config.layerConfig.add_inputs();
-  config.inputDefs.push_back({INPUT_DATA, "layer_1", 100, 0});
-  config.layerConfig.add_inputs();
-
-  for (auto useGpu : {false, true}) {
-    testLayerGrad(config, "cos_vm", 100, false, useGpu);
-  }
-}
-
-void testConvLayer(const string& type, bool trans, bool useGpu) {
-  TestConfig config;
-  config.biasSize = 16;
-  config.layerConfig.set_type(type);
-  config.layerConfig.set_num_filters(16);
-  config.layerConfig.set_partial_sum(1);
-  config.layerConfig.set_shared_biases(true);
-
-  config.inputDefs.push_back({INPUT_DATA, "layer_0", 384, 288});
+  int c = 4;
+  int h = 31;
+  int w = 36;
+  size_t size = c * h * w;
+  config.inputDefs.push_back({INPUT_DATA, "layer_0", size, 0});
   LayerInputConfig* input = config.layerConfig.add_inputs();
-  ConvConfig* conv = input->mutable_conv_conf();
-  conv->set_filter_size(2);
-  conv->set_filter_size_y(3);
-  conv->set_channels(3);
-  conv->set_padding(0);
-  conv->set_padding_y(1);
-  conv->set_stride(2);
-  conv->set_stride_y(2);
-  conv->set_groups(1);
-  conv->set_filter_channels(conv->channels() / conv->groups());
-  conv->set_img_size(16);
-  conv->set_img_size_y(8);
-  conv->set_output_x(outputSize(conv->img_size(),
-                                conv->filter_size(),
-                                conv->padding(),
-                                conv->stride(),
-                                /* caffeMode */ true));
-  conv->set_output_y(outputSize(conv->img_size_y(),
-                                conv->filter_size_y(),
-                                conv->padding_y(),
-                                conv->stride_y(),
-                                /* caffeMode */ true));
-  config.layerConfig.set_size(conv->output_x() * conv->output_y() *
-                              config.layerConfig.num_filters());
-
-  testLayerGrad(config, "conv", 100, trans, useGpu);
-  // Use small batch_size and useWeight=true to test biasGrad
-  testLayerGrad(config, "conv", 2, trans, useGpu, true, 0.02);
-}
-
-TEST(Layer, convLayer) {
-  testConvLayer("exconv", /* trans= */ false, /* useGpu= */ false);
-#ifndef PADDLE_ONLY_CPU
-  testConvLayer("exconv", /* trans= */ false, /* useGpu= */ true);
-  testConvLayer("cudnn_conv", /* trans= */ false, /* useGpu= */ true);
-#endif
-}
-
-void testConvTransLayer(const string& type, bool trans, bool useGpu) {
-  TestConfig config;
-  config.biasSize = 3;
-  config.layerConfig.set_type(type);
-  config.layerConfig.set_num_filters(3);
-  config.layerConfig.set_partial_sum(1);
-  config.layerConfig.set_shared_biases(true);
-
-  config.inputDefs.push_back({INPUT_DATA, "layer_0", 1024, 288});
-  LayerInputConfig* input = config.layerConfig.add_inputs();
-  ConvConfig* conv = input->mutable_conv_conf();
-  conv->set_filter_size(2);
-  conv->set_filter_size_y(3);
-  conv->set_channels(16);
-  conv->set_padding(0);
-  conv->set_padding_y(1);
-  conv->set_stride(2);
-  conv->set_stride_y(2);
-  conv->set_groups(1);
-  conv->set_filter_channels(3 / conv->groups());
-  conv->set_img_size(16);
-  conv->set_output_x(outputSize(conv->img_size(),
-                                conv->filter_size(),
-                                conv->padding(),
-                                conv->stride(),
-                                /* caffeMode */ true));
-
-  config.layerConfig.set_size(conv->img_size() * conv->img_size() *
-                              config.layerConfig.num_filters());
-
-  testLayerGrad(config, "convTrans", 100, trans, useGpu);
-  // Use small batch_size and useWeight=true to test biasGrad
-  testLayerGrad(config, "convTrans", 2, trans, useGpu, true, 0.02);
-}
-
-TEST(Layer, convTransLayer) {
-  for (auto useGpu : {false, true}) {
-    testConvTransLayer("exconvt", /* trans= */ false, /* useGpu= */ useGpu);
-  }
-}
-
-TEST(Layer, blockExpandLayer) {
-  TestConfig config;
-  config.biasSize = 0;
-  config.layerConfig.set_type("blockexpand");
-
-  config.inputDefs.push_back({INPUT_DATA, "layer_0", 6144, 0});
-  LayerInputConfig* input = config.layerConfig.add_inputs();
-  BlockExpandConfig* blockExpand = input->mutable_block_expand_conf();
-  blockExpand->set_img_size_x(64);
-  blockExpand->set_img_size_y(32);
-  blockExpand->set_channels(3);
-  blockExpand->set_padding_x(0);
-  blockExpand->set_padding_y(0);
-  blockExpand->set_block_x(4);
-  blockExpand->set_block_y(32);
-  blockExpand->set_stride_x(2);
-  blockExpand->set_stride_y(2);
-  blockExpand->set_output_x(outputSize(blockExpand->img_size_x(),
-                                       blockExpand->block_x(),
-                                       blockExpand->padding_x(),
-                                       blockExpand->stride_x(),
-                                       /* caffeMode */ false));
-  blockExpand->set_output_y(outputSize(blockExpand->img_size_y(),
-                                       blockExpand->block_y(),
-                                       blockExpand->padding_y(),
-                                       blockExpand->stride_y(),
-                                       /* caffeMode */ false));
-  config.layerConfig.set_size(blockExpand->block_x() * blockExpand->block_y() *
-                              blockExpand->channels());
-
-  for (auto useGpu : {false, true}) {
-    testLayerGrad(config, "blockexpand", 100, false, useGpu);
-  }
-}
-
-TEST(Layer, maxoutLayer) {
-  TestConfig config;
-  config.biasSize = 0;
-  config.layerConfig.set_type("maxout");
-
-  config.inputDefs.push_back({INPUT_DATA, "layer_0", 4096, 0});
-  LayerInputConfig* input = config.layerConfig.add_inputs();
-  MaxOutConfig* maxout = input->mutable_maxout_conf();
-  ImageConfig* image = maxout->mutable_image_conf();
-
-  image->set_img_size(32);
-  image->set_img_size_y(32);
-  image->set_channels(4);
-  maxout->set_groups(2);
-
-  for (auto useGpu : {false, true}) {
-    testLayerGrad(config, "maxout", 10, false, useGpu);
-  }
-}
-void testFcLayer(string format, size_t nnz) {
-  TestConfig config;
-  config.biasSize = 4096;
-  config.layerConfig.set_type("fc");
-  config.layerConfig.set_size(4096);
-  config.layerConfig.set_active_type("sigmoid");
-  config.layerConfig.set_drop_rate(0.1);
-
-  config.inputDefs.push_back(
-      {INPUT_DATA, "layer_0", 8192, nnz, ParaSparse(format)});
-  config.layerConfig.add_inputs();
-
-  LOG(INFO) << config.inputDefs[0].sparse.sparse << " "
-            << config.inputDefs[0].sparse.format;
-
-  for (auto useGpu : {false, true}) {
-    testLayerGrad(config,
-                  "fc",
-                  100,
-                  /* trans */ false,
-                  useGpu,
-                  /* weight */ true);
-  }
-}
-
-TEST(Layer, fcLayer) {
-  testFcLayer("", 4096 * 4096 * 2);
-  testFcLayer("csc", 4096 * 40);
-  testFcLayer("csr", 4096 * 40);
-}
-
-TEST(Layer, SelectiveFullyConnectedLayer) {
-  TestConfig config;
-  size_t nin = 16;
-  size_t nout = 256;
-  config.layerConfig.set_type("selective_fc");
-  config.layerConfig.set_size(nout);
-  config.layerConfig.set_active_type("sigmoid");
-  config.layerConfig.set_has_selected_colums(true);
-  config.layerConfig.set_selective_fc_pass_generation(false);
-  config.biasSize = nout;
-
-  config.inputDefs.push_back({INPUT_DATA, "input0", nin, nin * nout});
-  config.layerConfig.add_inputs();
-  config.inputDefs.push_back(
-      {INPUT_SPARSE_NON_VALUE_DATA, "index", nout, 0, ParaSparse("csr", true)});
-  config.layerConfig.add_inputs();
-
-  testLayerGrad(config,
-                "selective_fc",
-                100,
-                /* trans= */ false,
-                /* useGup= */ false,
-                false);
-#ifndef PADDLE_ONLY_CPU
-  testLayerGrad(config,
-                "selective_fc",
-                100,
-                /* trans= */ false,
-                /* useGup= */ true,
-                false);
-#endif
-}
-
-TEST(Layer, DataNormLayer) {
-  TestConfig config;
-  config.layerConfig.set_type("data_norm");
-  config.layerConfig.set_size(20);
-  config.biasSize = 0;
-
-  config.inputDefs.push_back({INPUT_DATA, "layer_0", 20, 100});
-  config.inputDefs.back().isStatic = true;
-  config.layerConfig.add_inputs();
-
-  for (auto strategy : {"z-score", "min-max", "decimal-scaling"}) {
-    config.layerConfig.set_data_norm_strategy(strategy);
-    // The parameters are static, so not support GPU now
-    testLayerGrad(config,
-                  "data_norm",
-                  200,
-                  /* trans */ false,
-                  /* useGpu */ false);
-  }
-}
-
-TEST(Layer, hsigmoidLayer) {
-  TestConfig config;
-  config.layerConfig.set_type("hsigmoid");
-  config.layerConfig.set_num_classes(5);
-  config.layerConfig.set_size(1);
-  config.biasSize = config.layerConfig.num_classes() - 1;
-
-  config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 200});
-  config.inputDefs.push_back({INPUT_LABEL, "layer_1", 5, 0});
-  config.layerConfig.add_inputs();
-  config.layerConfig.add_inputs();
-
-  // Not support GPU now
-  testLayerGrad(config, "hsigmoid", 100, /* trans */ false, /* useGpu */ false);
-}
-
-TEST(Layer, multi_cross) {
-  TestConfig config;
-  config.layerConfig.set_type("multi-class-cross-entropy");
-  config.biasSize = 0;
-
-  config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
-  config.inputDefs.push_back({INPUT_LABEL, "layer_1", 10, 0});
-  config.layerConfig.add_inputs();
-  config.layerConfig.add_inputs();
-
-  for (auto useGpu : {false, true}) {
-    testLayerGrad(
-        config, "multi-class-cross-entropy", 100, /* trans */ false, useGpu);
-  }
-}
-
-TEST(Layer, multi_binary_label_sparse_mat) {
-  TestConfig config;
-  config.layerConfig.set_type("multi_binary_label_cross_entropy");
-  config.biasSize = 0;
-
-  config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
-  config.inputDefs.push_back({INPUT_SPARSE_NON_VALUE_DATA, "layer_1", 50, 0});
-  config.layerConfig.add_inputs();
-  config.layerConfig.add_inputs();
-
-  for (auto useGpu : {false, true}) {
-    testLayerGrad(config,
-                  "multi_binary_label_cross_entropy",
-                  100,
-                  /* trans */ false,
-                  useGpu);
-  }
-}
-
-TEST(layer, multi_binary_label_id) {
-  TestConfig config;
-  config.layerConfig.set_type("multi_binary_label_cross_entropy");
-  config.biasSize = 0;
-
-  config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
-  config.inputDefs.push_back({INPUT_LABEL, "layer_1", 10, 0});
-  config.layerConfig.add_inputs();
-  config.layerConfig.add_inputs();
-
-  for (auto useGpu : {false, true}) {
-    testLayerGrad(config,
-                  "multi_binary_label_cross_entropy",
-                  100,
-                  /* trans */ false,
-                  useGpu);
-  }
-}
-
-TEST(Layer, multi_cross_with_selfnorm) {
-  TestConfig config;
-  config.layerConfig.set_type("multi_class_cross_entropy_with_selfnorm");
-  config.layerConfig.set_softmax_selfnorm_alpha(0.1);
-  config.biasSize = 0;
-
-  config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
-  config.inputDefs.push_back({INPUT_LABEL, "layer_1", 10, 0});
-  config.layerConfig.add_inputs();
-  config.layerConfig.add_inputs();
-
-  // Not support GPU now
-  testLayerGrad(config,
-                "multi_class_cross_entropy_with_selfnorm",
-                100,
-                /* trans */ false,
-                /* useGpu */ false);
-}
-
-TEST(Layer, multi_cross_soft) {
-  TestConfig config;
-  config.layerConfig.set_type("soft_binary_class_cross_entropy");
-  config.biasSize = 0;
-
-  config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
-  config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 10, 0});
-  config.layerConfig.add_inputs();
-  config.layerConfig.add_inputs();
-
-  for (auto useGpu : {false, true}) {
-    testLayerGrad(config,
-                  "soft_binary_class_cross_entropy",
-                  100,
-                  /* trans */ false,
-                  useGpu);
-  }
-}
-
-TEST(Layer, square_error) {
-  TestConfig config;
-  config.layerConfig.set_type("square_error");
-  config.biasSize = 0;
-
-  config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
-  config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 10, 0});
-  config.layerConfig.add_inputs();
-  config.layerConfig.add_inputs();
-
-  for (auto useGpu : {false, true}) {
-    testLayerGrad(config, "square_error", 100, /* trans */ false, useGpu);
-  }
-}
-
-TEST(Layer, sparse_square_error) {
-  TestConfig config;
-  config.layerConfig.set_type("square_error");
-  config.biasSize = 0;
-
-  config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
-  config.inputDefs.push_back({INPUT_SPARSE_NON_VALUE_DATA, "layer_1", 50, 0});
-  config.layerConfig.add_inputs();
-  config.layerConfig.add_inputs();
-
-  // "GpuSparseMatrix" as label is not supported
-  testLayerGrad(config,
-                "square_error",
-                100,
-                /* trans */ false,
-                /* useGpu */ false);
-}
-
-TEST(Layer, sparse_float_square_error) {
-  TestConfig config;
-  config.layerConfig.set_type("square_error");
-  config.biasSize = 0;
-
-  config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
-  config.inputDefs.push_back({INPUT_SPARSE_FLOAT_VALUE_DATA, "layer_1", 50, 0});
-  config.layerConfig.add_inputs();
-  config.layerConfig.add_inputs();
-
-  // "GpuSparseMatrix" as label is not supported
-  testLayerGrad(config,
-                "square_error",
-                100,
-                /* trans */ false,
-                /* useGpu */ false);
-}
-
-TEST(Layer, square_error_weighted) {
-  TestConfig config;
-  config.layerConfig.set_type("square_error");
-  config.biasSize = 0;
-  config.testAccumulate = false;
-
-  config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
-  config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 10, 0});
-  config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_2", 1, 0});
-  config.layerConfig.add_inputs();
-  config.layerConfig.add_inputs();
-  config.layerConfig.add_inputs();
-
-  for (auto useGpu : {false, true}) {
-    testLayerGrad(config, "square_error", 100, /* trans */ false, useGpu);
-  }
-}
-
-TEST(Layer, huber_two_class) {
-  TestConfig config;
-  config.layerConfig.set_type("huber");
-  config.biasSize = 0;
-
-  config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
-  config.inputDefs.push_back({INPUT_LABEL, "layer_1", 2, 0});
-  config.layerConfig.add_inputs();
-  config.layerConfig.add_inputs();
-
-  for (auto useGpu : {false, true}) {
-    testLayerGrad(config, "huber", 100, /* trans */ false, useGpu);
-  }
-}
-
-void testExpandLayer(string trans_type, bool hasSubseq) {
-  TestConfig config;
-  config.layerConfig.set_type("expand");
-
-  config.inputDefs.push_back(
-      {trans_type == "non-seq" ? INPUT_DENSE_DIM_DATA : INPUT_SEQUENCE_DATA,
-       "layer_0",
-       10,
-       0});
-  config.inputDefs.push_back(
-      {hasSubseq ? INPUT_HASSUB_SEQUENCE_DATA : INPUT_SEQUENCE_DATA,
-       "layer_1",
-       10,
-       0});
-  config.layerConfig.add_inputs();
-  config.layerConfig.add_inputs();
-  config.layerConfig.set_trans_type(trans_type);
-  LOG(INFO) << " trans_type=" << trans_type << " hasSubseq=" << hasSubseq;
-
-  for (auto useGpu : {false, true}) {
-    testLayerGrad(config, "expand", 30, false, useGpu);
-  }
-}
-
-TEST(Layer, ExpandLayer) {
-  testExpandLayer("non-seq", false);  // non-seq expand to seq
-  testExpandLayer("non-seq", true);   // non-seq expand to hasSubseq
-  testExpandLayer("seq", true);       // seq expand to hasSubseq
-}
-
-void testDegradeLayer(bool hasSubseq, string layer_type, string trans_type) {
-  TestConfig config;
-  config.layerConfig.set_type(layer_type);
-  config.layerConfig.set_size(10);
-  config.biasSize = 0;
-
-  config.inputDefs.push_back(
-      {hasSubseq ? INPUT_HASSUB_SEQUENCE_DATA : INPUT_SEQUENCE_DATA,
-       "layer_0",
-       10,
-       0});
-  config.layerConfig.add_inputs();
-  config.layerConfig.set_trans_type(trans_type);
-
-  auto testDegradeLayerGrad = [](TestConfig& config, string layer_type) {
-    for (auto useGpu : {false, true}) {
-      testLayerGrad(config, layer_type, 100, false, useGpu);
-    }
-  };
-
-  if (layer_type == "average") {
-    for (auto strategy : {"average", "sum", "squarerootn"}) {
-      LOG(INFO) << " hasSubseq=" << hasSubseq << " trans_type=" << trans_type
-                << " average_strategy=" << strategy;
-      config.layerConfig.set_average_strategy(strategy);
-      testDegradeLayerGrad(config, layer_type);
-    }
-  } else {
-    LOG(INFO) << " hasSubseq=" << hasSubseq << " trans_type=" << trans_type;
-    testDegradeLayerGrad(config, layer_type);
-  }
-}
-
-TEST(Layer, MaxLayer) {
-  testDegradeLayer(false, "max", "non-seq");  // seq max to non-seq
-  testDegradeLayer(true, "max", "non-seq");   // hasSubseq max to non-seq
-  testDegradeLayer(true, "max", "seq");       // hasSubseq max to seq
-}
-
-TEST(Layer, SequenceLastInstanceLayer) {
-  testDegradeLayer(false,
-                   "seqlastins",
-                   "non-seq");  // seq seqlastins to non-seq
-  testDegradeLayer(true,
-                   "seqlastins",
-                   "non-seq");  // hasSubseq seqlastins to non-seq
-  testDegradeLayer(true, "seqlastins", "seq");  // hasSubseq seqlastins to seq
-}
-
-TEST(Layer, AverageLayer) {
-  testDegradeLayer(false, "average", "non-seq");  // seq average to non-seq
-  testDegradeLayer(true, "average", "non-seq");  // hasSubseq average to non-seq
-  testDegradeLayer(true, "average", "seq");      // hasSubseq average to seq
-}
-
-TEST(Layer, SequenceConcatLayer) {
-  TestConfig config;
-  config.layerConfig.set_type("seqconcat");
-  config.layerConfig.set_size(10);
-  config.biasSize = 0;
-
-  config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 10, 0});
-  config.layerConfig.add_inputs();
-  config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_1", 10, 0});
-  config.layerConfig.add_inputs();
-
-  for (auto useGpu : {false, true}) {
-    testLayerGrad(config, "seqconcat", 100, false, useGpu);
-  }
-}
-
-TEST(Layer, SequenceReshapeLayer) {
-  TestConfig config;
-  config.layerConfig.set_type("seqreshape");
-  config.layerConfig.set_size(10);
-
-  config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 100, 0});
-  config.layerConfig.add_inputs();
-
-  for (auto useGpu : {false, true}) {
-    testLayerGrad(config, "seqreshape", 100, false, useGpu);
-  }
-}
-
-TEST(Layer, ConvShiftLayer) {
-  TestConfig config;
-  config.layerConfig.set_type("conv_shift");
-  config.layerConfig.set_size(10);
-
-  config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
-  config.inputDefs.push_back({INPUT_DATA, "layer_1", 3, 0});
-  config.layerConfig.add_inputs();
-  config.layerConfig.add_inputs();
-
-  // Not support GPU now
-  testLayerGrad(config, "conv_shift", 100, false, false);
-}
-
-TEST(Layer, PowerLayer) {
-  TestConfig config;
-  config.layerConfig.set_type("power");
-  config.layerConfig.set_size(10);
-
-  config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
-  config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
-  config.layerConfig.add_inputs();
-  config.layerConfig.add_inputs();
-
-  for (auto useGpu : {false, true}) {
-    testLayerGrad(config, "power", 100, false, useGpu);
-  }
-}
-
-TEST(Layer, ConvexCombinationLayer) {
-  TestConfig config;
-  config.layerConfig.set_type("convex_comb");
-  config.layerConfig.set_size(20);
-  config.biasSize = 0;
-
-  config.inputDefs.push_back({INPUT_DATA, "layer_0", 5, 0});
-  config.inputDefs.push_back({INPUT_DATA, "layer_1", 100, 0});
-  config.layerConfig.add_inputs();
-  config.layerConfig.add_inputs();
-
-  for (auto useGpu : {false, true}) {
-    testLayerGrad(config, "convex_comb", 100, false, useGpu);
-  }
-}
-
-TEST(Layer, InterpolationLayer) {
-  TestConfig config;
-  config.layerConfig.set_type("interpolation");
-  config.layerConfig.set_size(10);
-  config.biasSize = 0;
-
-  config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
-  config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
-  config.inputDefs.push_back({INPUT_DATA, "layer_2", 10, 0});
-  config.layerConfig.add_inputs();
-  config.layerConfig.add_inputs();
-  config.layerConfig.add_inputs();
-
-  for (auto useGpu : {false, true}) {
-    testLayerGrad(config, "interpolation", 100, false, useGpu);
-  }
-}
-
-TEST(Layer, OuterProdLayer) {
-  TestConfig config;
-  config.layerConfig.set_type("out_prod");
-  config.layerConfig.set_size(100);
-
-  config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
-  config.layerConfig.add_inputs();
-  config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
-  config.layerConfig.add_inputs();
-
-  for (auto useGpu : {false, true}) {
-    testLayerGrad(config, "out_prod", 100, false, useGpu);
-  }
-}
-
-TEST(Layer, SlopeInterceptLayer) {
-  TestConfig config;
-  config.layerConfig.set_type("slope_intercept");
-  config.layerConfig.set_size(10);
-  config.layerConfig.set_slope(1.0);
-  config.layerConfig.set_intercept(0.1);
-
-  config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
-  config.layerConfig.add_inputs();
-
-  for (auto useGpu : {false, true}) {
-    testLayerGrad(config, "slope_intercept", 100, false, useGpu);
-  }
-}
-
-TEST(Layer, ScalingLayer) {
-  TestConfig config;
-  config.layerConfig.set_type("scaling");
-  config.layerConfig.set_size(10);
-  config.biasSize = 0;
-
-  config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
-  config.layerConfig.add_inputs();
-  config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
-  config.layerConfig.add_inputs();
-
-  for (auto useGpu : {false, true}) {
-    testLayerGrad(config, "scaling", 100, false, useGpu);
-  }
-}
-
-void testNormLayer(const string& normType, bool trans, bool useGpu) {
-  TestConfig config;
-  config.layerConfig.set_type("norm");
-  config.layerConfig.set_active_type("relu");
-
-  config.inputDefs.push_back({INPUT_DATA, "layer_0", 1568, 0});
-  LayerInputConfig* input = config.layerConfig.add_inputs();
-  NormConfig* norm = input->mutable_norm_conf();
-  norm->set_norm_type(normType);
-  norm->set_channels(16);
-  norm->set_size(5);
-  norm->set_scale(0.001);
-  norm->set_pow(0.75);
-  norm->set_blocked(0);
-  norm->set_img_size(14);
-  norm->set_img_size_y(7);
-  norm->set_output_x(norm->img_size());
-  norm->set_output_y(norm->img_size_y());
-  if (norm->norm_type() == "cmrnorm" ||
-      norm->norm_type() == "cmrnorm-projection") {
-    norm->set_scale(norm->scale() / norm->size());
-  } else {
-    norm->set_scale(norm->scale() / (norm->size() * norm->size()));
-  }
-
-  config.layerConfig.set_size(norm->output_x() * norm->output_y() *
-                              norm->channels());
-  config.biasSize = 0;
-
-  testLayerGrad(config, "norm", 100, trans, useGpu);
-}
-
-TEST(Layer, NormLayer) {
-  testNormLayer("cmrnorm-projection", /* trans= */ false, /* useGpu= */ true);
-  testNormLayer("cmrnorm-projection", /* trans= */ false, /* useGpu= */ false);
-}
-
-void setPoolConfig(TestConfig* config,
-                   PoolConfig* pool,
-                   const string& poolType) {
-  (*config).biasSize = 0;
-  (*config).layerConfig.set_type("pool");
-  (*config).layerConfig.set_num_filters(16);
-
-  int kw = 3, kh = 3;
-  int pw = 0, ph = 0;
-  int sw = 2, sh = 2;
-  pool->set_pool_type(poolType);
-  pool->set_channels(16);
-  pool->set_size_x(kw);
-  pool->set_size_y(kh);
-  pool->set_start(0);
-  pool->set_padding(pw);
-  pool->set_padding_y(ph);
-  pool->set_stride(sw);
-  pool->set_stride_y(sh);
-
-  int ow = outputSize(pool->img_size(), kw, pw, sw, /* caffeMode */ false);
-  int oh = outputSize(pool->img_size_y(), kh, ph, sh, /* caffeMode */ false);
-  pool->set_output_x(ow);
-  pool->set_output_y(oh);
-}
-
-void testPoolLayer(const string& poolType, bool trans, bool useGpu) {
-  TestConfig config;
-  config.inputDefs.push_back({INPUT_DATA, "layer_0", 3136, 0});
-  LayerInputConfig* input = config.layerConfig.add_inputs();
-  PoolConfig* pool = input->mutable_pool_conf();
-
-  pool->set_img_size(14);
-  pool->set_img_size_y(14);
-  setPoolConfig(&config, pool, poolType);
-  config.layerConfig.set_size(pool->output_x() * pool->output_y() *
-                              pool->channels());
-
-  testLayerGrad(config, "pool", 100, trans, useGpu);
-}
-
-#ifndef PADDLE_ONLY_CPU
-void testPoolLayer2(const string& poolType, bool trans, bool useGpu) {
-  TestConfig config;
-  config.inputDefs.push_back({INPUT_DATA, "layer_0", 3200, 0});
-  LayerInputConfig* input = config.layerConfig.add_inputs();
-  PoolConfig* pool = input->mutable_pool_conf();
-
-  pool->set_size_y(4);
-  pool->set_stride_y(3);
-  pool->set_img_size(10);
-  pool->set_img_size_y(20);
-  setPoolConfig(&config, pool, poolType);
-  pool->set_output_y((pool->img_size_y() - pool->start() - pool->size_y()) /
-                         ((float)pool->stride_y()) +
-                     1.5);
-  config.layerConfig.set_size(pool->output_x() * pool->output_y() *
-                              pool->channels());
-
-  testLayerGrad(config, "pool", 100, trans, useGpu);
-}
-#endif
-
-TEST(Layer, PoolLayer) {
-  testPoolLayer("avg-projection", /* trans= */ false, /* useGpu= */ false);
-  testPoolLayer("max-projection", /* trans= */ false, /* useGpu= */ false);
-
-#ifndef PADDLE_ONLY_CPU
-  testPoolLayer("avg-projection", /* trans= */ false, /* useGpu= */ true);
-  testPoolLayer("max-projection", /* trans= */ false, /* useGpu= */ true);
-  testPoolLayer("cudnn-max-pool", /* trans= */ false, /* useGpu= */ true);
-  testPoolLayer("cudnn-avg-pool", /* trans= */ false, /* useGpu= */ true);
-  testPoolLayer2("cudnn-max-pool", /* trans= */ false, /* useGpu= */ true);
-  testPoolLayer2("cudnn-avg-pool", /* trans= */ false, /* useGpu= */ true);
-#endif
-}
-
-void testSppLayer(const string& poolType,
-                  const int pyramidHeight,
-                  bool trans,
-                  bool useGpu) {
-  TestConfig config;
-  config.layerConfig.set_type("spp");
-  config.inputDefs.push_back({INPUT_DATA, "layer_0", 3200, 0});
-  LayerInputConfig* input = config.layerConfig.add_inputs();
-  SppConfig* sppConfig = input->mutable_spp_conf();
-  sppConfig->set_pool_type(poolType);
-  sppConfig->set_pyramid_height(pyramidHeight);
-  ImageConfig* imageConfig = sppConfig->mutable_image_conf();
-  imageConfig->set_channels(16);
-  imageConfig->set_img_size(10);
-  imageConfig->set_img_size_y(20);
-  int outputSize = (std::pow(4, sppConfig->pyramid_height()) - 1) / (4 - 1);
-  config.layerConfig.set_size(outputSize * imageConfig->channels());
-  testLayerGrad(config, "spp", 100, trans, useGpu);
-}
-
-TEST(Layer, SpatialPyramidPoolLayer) {
-  for (auto useGpu : {false, true}) {
-    for (auto pyramidHeight : {1, 2, 3}) {
-      testSppLayer("avg-projection", pyramidHeight, false, useGpu);
-      testSppLayer("max-projection", pyramidHeight, false, useGpu);
-    }
-  }
-}
-
-TEST(Layer, rankCostLayer) {
-  TestConfig config;
-  config.layerConfig.set_type("rank-cost");
-  config.biasSize = 0;
-
-  config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
-  config.inputDefs.push_back({INPUT_DATA, "layer_1", 1, 0});
-  config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_2", 1, 0});
-  config.layerConfig.add_inputs();
-  config.layerConfig.add_inputs();
-  config.layerConfig.add_inputs();
-
-  for (auto useGpu : {false, true}) {
-    testLayerGrad(config, "rank-cost", 100, false, useGpu);
-  }
-}
-
-TEST(Layer, sumCostLayer) {
-  TestConfig config;
-  config.layerConfig.set_type("sum_cost");
-  config.biasSize = 0;
-
-  config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
-  config.layerConfig.add_inputs();
-
-  for (auto useGpu : {false, true}) {
-    testLayerGrad(config, "sum_cost", 100, false, useGpu);
-  }
-}
-
-TEST(Layer, weightedRankCostLayer) {
-  TestConfig config;
-  config.layerConfig.set_type("rank-cost");
-  config.biasSize = 0;
-
-  config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
-  config.inputDefs.push_back({INPUT_DATA, "layer_1", 1, 0});
-  config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_2", 1, 0});
-  config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_3", 1, 0});
-  config.layerConfig.add_inputs();
-  config.layerConfig.add_inputs();
-  config.layerConfig.add_inputs();
-  config.layerConfig.add_inputs();
-
-  for (auto useGpu : {false, true}) {
-    testLayerGrad(config, "weighted-rank-cost", 100, false, useGpu);
-  }
-}
-
-TEST(Layer, TensorLayer) {
-  TestConfig config;
-  config.layerConfig.set_type("tensor");
-  config.layerConfig.set_size(10);
-  config.layerConfig.set_active_type("sigmoid");
-  config.biasSize = config.layerConfig.size();
-
-  config.inputDefs.push_back({INPUT_DATA, "layer_0", 5, 250});
-  config.inputDefs.push_back({INPUT_DATA, "layer_1", 5, 0});
-  config.layerConfig.add_inputs();
-  config.layerConfig.add_inputs();
-
-  for (auto useGpu : {false, true}) {
-    testLayerGrad(config, "tensor", 100, false, useGpu);
-  }
-}
-
-TEST(Layer, RecurrentLayer) {
-  TestConfig config;
-  config.layerConfig.set_type("recurrent");
-  config.layerConfig.set_size(4);
-  config.layerConfig.set_active_type("tanh");
-  config.biasSize = 4;
-
-  config.inputDefs.push_back(
-      {INPUT_SEQUENCE_DATA, "layer_0", /* dim= */ 4, /* paraSize= */ 16});
-  config.layerConfig.add_inputs();
-
-  for (auto useGpu : {false, true}) {
-    for (auto reversed : {false, true}) {
-      config.layerConfig.set_reversed(reversed);
-      config.testState = !reversed;
-      testLayerGrad(config, "recurrent", 50, /* trans= */ false, useGpu);
-    }
-  }
-}
-
-TEST(Layer, LstmLayer) {
-  TestConfig config;
-  config.layerConfig.set_type("lstmemory");
-  config.layerConfig.set_size(4);
-  config.layerConfig.set_active_type("tanh");
-  config.layerConfig.set_active_state_type("sigmoid");
-  config.layerConfig.set_active_gate_type("sigmoid");
-  config.biasSize = 28;
-
-  config.inputDefs.push_back(
-      {INPUT_SEQUENCE_DATA, "layer_0", /* dim= */ 16, /* paraSize= */ 64});
-  config.layerConfig.add_inputs();
-
-  for (auto useGpu : {false, true}) {
-    for (auto reversed : {false, true}) {
-      config.layerConfig.set_reversed(reversed);
-      config.testState = !reversed;
-      testLayerGrad(config, "lstmemory", 100, /* trans= */ false, useGpu);
-    }
-  }
-  for (auto useGpu : {true}) {
-    config.testBatchState = true;
-    config.layerConfig.set_reversed(false);
-    testLayerGrad(config, "lstmemory", 10, /* trans= */ false, useGpu);
-  }
-}
-
-TEST(Layer, MDLstmLayer) {
-  TestConfig config;
-  config.layerConfig.set_type("mdlstmemory");
-  config.layerConfig.set_size(4);
-  config.layerConfig.set_active_type("sigmoid");
-  config.layerConfig.set_active_state_type("sigmoid");
-  config.layerConfig.set_active_gate_type("sigmoid");
-  config.biasSize = 4 * 9;
-
-  config.inputDefs.push_back(
-      {INPUT_SEQUENCE_MDIM_DATA, "layer_0", 4 * 5, 4 * 4 * 5});
-  config.layerConfig.add_inputs();
-  config.layerConfig.add_directions(true);
-  config.layerConfig.add_directions(true);
-
-  for (auto useGpu : {false, true}) {
-    for (int i = 0; i < 2; i++) {
-      for (int j = 0; j < 2; j++) {
-        config.layerConfig.set_directions(0, bool(i));
-        config.layerConfig.set_directions(1, bool(j));
-        testLayerGrad(config, "mdlstmemory", 100, false, useGpu);
-      }
-    }
-  }
-}
-
-TEST(Layer, ParameterReluLayer) {
-  auto testParameterReluLayer = [&](size_t inputSize, size_t channels) {
-    TestConfig config;
-    config.layerConfig.set_type("prelu");
-    config.inputDefs.push_back({INPUT_DATA, "layer_0", inputSize, channels});
-    config.layerConfig.add_inputs();
-    config.layerConfig.set_size(inputSize);
-    config.layerConfig.set_partial_sum(inputSize /
-                                       channels);  // size of feature map
-    for (auto useGpu : {false, true}) {
-      testLayerGrad(config, "prelu", 100, false, useGpu);
-    }
-  };
-
-  testParameterReluLayer(192, 1);
-  testParameterReluLayer(192, 3);
-  testParameterReluLayer(192, 192);
-}
-
-TEST(Layer, ResizeLayer) {
-  TestConfig config;
-  config.biasSize = 0;
-  config.layerConfig.set_type("resize");
-  config.layerConfig.set_size(64);
-
-  config.inputDefs.push_back({INPUT_DATA, "layer_0", 16, 0});
-  config.layerConfig.add_inputs();
-
-  for (auto useGpu : {false, true}) {
-    testLayerGrad(config, "resize", 100, false, useGpu);
-  }
-}
-
-TEST(Layer, NCELayer) {
-  TestConfig config;
-  size_t numClasses = 4;
-  config.layerConfig.set_type("nce");
-  config.layerConfig.set_size(1);
-  config.layerConfig.set_active_type("sigmoid");
-  config.layerConfig.set_num_classes(numClasses);
-  config.biasSize = numClasses;
-
-  config.inputDefs.push_back(
-      {INPUT_DATA, "layer_0", /* dim= */ 16, /* paraSize= */ 16 * numClasses});
-  config.inputDefs.push_back(
-      {INPUT_LABEL, "label", /* dim= */ numClasses, /* paraSize= */ 0});
-  config.layerConfig.add_inputs();
-  config.layerConfig.add_inputs();
-
-  for (auto withWeight : {false, true}) {
-    if (withWeight) {
-      config.inputDefs.push_back(
-          {INPUT_DATA_TARGET, "weight", /* dim= */ 1, /* paraSize= */ 0});
-      config.layerConfig.add_inputs();
-    }
-
-    for (auto isIdLabel : {false, true}) {
-      config.inputDefs[1] = {
-          isIdLabel ? INPUT_LABEL : INPUT_SPARSE_NON_VALUE_DATA,
-          "label",
-          /* dim= */ numClasses,
-          /* paraSize= */ 0};
-
-      for (auto withDist : {false, true}) {
-        config.layerConfig.clear_neg_sampling_dist();
-        if (withDist) {
-          double sum = 0;
-          for (size_t i = 0; i < numClasses; ++i) {
-            real p = rand();  // NOLINT use rand_r
-            config.layerConfig.add_neg_sampling_dist(p);
-            sum += p;
-          }
-          for (size_t i = 0; i < numClasses; ++i) {
-            real p = config.layerConfig.neg_sampling_dist(i) / sum;
-            config.layerConfig.set_neg_sampling_dist(i, p);
-          }
-        }
-        LOG(INFO) << "NCELayer "
-                  << " isIdLabel=" << isIdLabel << " withWeight=" << withWeight
-                  << " withDist=" << withDist;
-        // Not support GPU now
-        testLayerGrad(config,
-                      "nce",
-                      100,
-                      /* trans= */ false,
-                      /* useGpu */ false);
-      }
-    }
-  }
-}
-
-TEST(Layer, GatedRecurrentLayer) {
-  TestConfig config;
-  config.layerConfig.set_type("gated_recurrent");
-  config.layerConfig.set_size(4);
-  config.layerConfig.set_active_type("sigmoid");
-  config.layerConfig.set_active_gate_type("sigmoid");
-  config.biasSize = 12;
-
-  config.inputDefs.push_back(
-      {INPUT_SEQUENCE_DATA, "layer_0", /* dim= */ 12, /* paraSize= */ 48});
-  config.layerConfig.add_inputs();
-
-  for (auto useGpu : {false, true}) {
-    for (auto reversed : {false, true}) {
-      config.layerConfig.set_reversed(reversed);
-      config.testState = !reversed;
-      testLayerGrad(config, "gated_recurrent", 100, /* trans= */ false, useGpu);
-    }
-  }
-}
-
-TEST(Layer, GruStepLayer) {
-  TestConfig config;
-  config.layerConfig.set_type("gru_step");
-  config.layerConfig.set_size(4);
-  config.layerConfig.set_active_type("sigmoid");
-  config.layerConfig.set_active_gate_type("sigmoid");
-  config.biasSize = 12;
-
-  config.inputDefs.push_back(
-      {INPUT_DATA, "layer_0", /* dim= */ 12, /* paraSize= */ 48});
-  config.inputDefs.push_back(
-      {INPUT_DATA, "layer_1", /* dim= */ 4, /* paraSize= */ 0});
-  config.layerConfig.add_inputs();
-  config.layerConfig.add_inputs();
-
-  for (auto useGpu : {false, true}) {
-    testLayerGrad(config, "gruStep", 100, /* trans= */ false, useGpu);
-  }
-}
-
-TEST(Layer, LstmStepLayer) {
-  TestConfig config;
-  config.layerConfig.set_type("lstm_step");
-  config.layerConfig.set_size(4);
-  config.layerConfig.set_active_type("sigmoid");
-  config.layerConfig.set_active_state_type("sigmoid");
-  config.layerConfig.set_active_gate_type("sigmoid");
-  config.biasSize = 12;
-  config.testAccumulate = false;
-
-  config.inputDefs.push_back(
-      {INPUT_DATA, "layer_0", /* dim= */ 16, /* paraSize= */ 0});
-  config.inputDefs.push_back(
-      {INPUT_DATA, "layer_1", /* dim= */ 4, /* paraSize= */ 0});
-  config.layerConfig.add_inputs();
-  config.layerConfig.add_inputs();
-
-  for (auto useGpu : {false, true}) {
-    testLayerGrad(config, "lstmStep", 100, /* trans= */ false, useGpu);
-  }
-}
-
-void testBatchNormLayer(const string& type, bool trans, bool useGpu) {
-  TestConfig config;
-  const int CHANNELS = 10;
-  const int IMG_SIZE = 16;
-  const int IMG_SIZE_Y = 8;
-  size_t size = CHANNELS * IMG_SIZE * IMG_SIZE_Y;
-  config.layerConfig.set_type(type);
-  config.layerConfig.set_size(size);
-  config.layerConfig.set_active_type("sigmoid");
-  config.biasSize = CHANNELS;
-  config.inputDefs.push_back({INPUT_DATA,
-                              "layer_0",
-                              /* dim= */ size,
-                              /* paraSize= */ CHANNELS});
-
-  config.inputDefs.push_back({INPUT_DATA, "layer_1_running_mean", 1, CHANNELS});
-  config.inputDefs.back().isStatic = true;
-  config.inputDefs.push_back({INPUT_DATA, "layer_2_running_var", 1, CHANNELS});
-  config.inputDefs.back().isStatic = true;
-
-  LayerInputConfig* input = config.layerConfig.add_inputs();
-  config.layerConfig.add_inputs();
-  config.layerConfig.add_inputs();
-
-  ImageConfig* img_conf = input->mutable_image_conf();
-  img_conf->set_channels(CHANNELS);
-  img_conf->set_img_size(IMG_SIZE);
-  img_conf->set_img_size_y(IMG_SIZE_Y);
-
-  testLayerGrad(config,
-                "batch_norm",
-                64,
-                /* trans= */ trans,
-                useGpu,
-                /* useWeight */ true);
-}
-
-TEST(Layer, BatchNormalizationLayer) {
-  testBatchNormLayer("batch_norm", false, false);
-#ifndef PADDLE_ONLY_CPU
-  testBatchNormLayer("batch_norm", false, true);
-  if (hl_get_cudnn_lib_version() >= int(4000)) {
-    testBatchNormLayer("cudnn_batch_norm", false, true);
-  }
-#endif
-}
-
-TEST(Operator, conv) {
-  TestConfig config;
-  const int NUM_FILTERS = 16;
-  const int FILTER_SIZE = 2;
-  const int FILTER_SIZE_Y = 3;
-  const int CHANNELS = 3;
-  const int IMAGE_SIZE = 16;
-  const int IMAGE_SIZE_Y = 8;
-  OperatorConfig& operatorConf = *config.layerConfig.add_operator_confs();
-  operatorConf.set_type("conv");
-  ConvConfig* conv = operatorConf.mutable_conv_conf();
-  operatorConf.set_num_filters(NUM_FILTERS);
-  conv->set_filter_size(FILTER_SIZE);
-  conv->set_filter_size_y(FILTER_SIZE_Y);
-  conv->set_channels(CHANNELS);
-  conv->set_padding(0);
-  conv->set_padding_y(1);
-  conv->set_stride(2);
-  conv->set_stride_y(2);
-  conv->set_groups(1);
-  conv->set_filter_channels(conv->channels() / conv->groups());
-  conv->set_img_size(IMAGE_SIZE);
-  conv->set_img_size_y(IMAGE_SIZE_Y);
-  conv->set_output_x(outputSize(conv->img_size(),
-                                conv->filter_size(),
-                                conv->padding(),
-                                conv->stride(),
-                                /*  caffeMode */ true));
-  conv->set_output_y(outputSize(conv->img_size_y(),
-                                conv->filter_size_y(),
-                                conv->padding_y(),
-                                conv->stride_y(),
-                                /*  caffeMode */ true));
-  config.layerConfig.set_size(conv->output_x() * conv->output_y() *
-                              NUM_FILTERS);
-
-  config.inputDefs.push_back(
-      {INPUT_DATA, "layer_0", IMAGE_SIZE * IMAGE_SIZE_Y * CHANNELS, 0});
-  config.inputDefs.push_back(
-      {INPUT_DATA,
-       "layer_1",
-       FILTER_SIZE * FILTER_SIZE_Y * CHANNELS * NUM_FILTERS,
-       0});
-  config.layerConfig.add_inputs();
-  config.layerConfig.add_inputs();
-
-  testOperatorGrad(config, operatorConf, 100, /*useGpu*/ true, false);
-}
-
-TEST(Layer, FeatureMapExpandLayer) {
-  TestConfig config;
-  config.layerConfig.set_type("featmap_expand");
-  const int CHANNELS = 10;
-  const int INPUT_SIZE = 100;
-  config.layerConfig.set_size(INPUT_SIZE * CHANNELS);
-  config.layerConfig.set_num_filters(CHANNELS);
-  config.inputDefs.push_back({INPUT_SEQUENCE_DATA,
-                              "layer_0",
-                              /* dim= */ INPUT_SIZE,
-                              /* paraSize= */ 0});
-  config.layerConfig.add_inputs();
-  for (auto useGpu : {false, true}) {
-    testLayerGrad(config,
-                  "featmap_expand",
-                  /*batch_size*/ 100,
-                  /* trans= */ false,
-                  useGpu,
-                  /* useWeight */ true);
-  }
-}
-
-TEST(Layer, MultiplexLayer) {
-  TestConfig config;
-  const int LAYER_SIZE = 100;
-  config.layerConfig.set_type("multiplex");
-  config.layerConfig.set_size(LAYER_SIZE);
-
-  config.inputDefs.push_back({INPUT_LABEL, "layer_0", 2, 0});
-  config.inputDefs.push_back(
-      {INPUT_DATA, "layer_1", /* dim= */ LAYER_SIZE, /* paraSize= */ 0});
-  config.inputDefs.push_back(
-      {INPUT_DATA, "layer_2", /* dim= */ LAYER_SIZE, /* paraSize= */ 0});
-  config.layerConfig.add_inputs();
-  config.layerConfig.add_inputs();
-  config.layerConfig.add_inputs();
+  PadConfig* pad = input->mutable_pad_conf();
+  ImageConfig* image = pad->mutable_image_conf();
+
+  image->set_channels(c);
+  image->set_img_size(h);
+  image->set_img_size_y(w);
+  pad->add_pad_c(1);
+  pad->add_pad_c(2);
+  pad->add_pad_h(2);
+  pad->add_pad_h(3);
+  pad->add_pad_w(3);
+  pad->add_pad_w(5);
 
   for (auto useGpu : {false, true}) {
-    testLayerGrad(config, "multiplex", 512, /* trans= */ false, useGpu);
+    testLayerGrad(config, "pad", 10, false, useGpu);
   }
 }
 
diff --git a/proto/ModelConfig.proto b/proto/ModelConfig.proto
index 3a9d339976fff91d79e7459ad5984cf78ea8990a..0456404832c301d8ceb9338d32da0cea9eae5234 100644
--- a/proto/ModelConfig.proto
+++ b/proto/ModelConfig.proto
@@ -255,6 +255,13 @@ message PriorBoxConfig {
   repeated float variance = 4;
 }
 
+message PadConfig {
+  required ImageConfig image_conf = 1;
+  repeated uint32 pad_c = 2;
+  repeated uint32 pad_h = 3;
+  repeated uint32 pad_w = 4;
+}
+
 message LayerInputConfig {
   required string input_layer_name = 1;
   optional string input_parameter_name = 2;
@@ -271,6 +278,7 @@ message LayerInputConfig {
   optional MaxOutConfig maxout_conf = 11;
   optional SppConfig spp_conf = 12;
   optional PriorBoxConfig priorbox_conf = 13;
+  optional PadConfig pad_conf = 14;
 }
 
 message LayerConfig {
diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py
index 674b5ac58b6febd914cb36c75356d8aa70a908b1..6e5922166da1b11601b355770673c7231f7da08f 100644
--- a/python/paddle/trainer/config_parser.py
+++ b/python/paddle/trainer/config_parser.py
@@ -493,6 +493,7 @@ class Input(Cfg):
             block_expand=None,
             maxout=None,
             spp=None,
+            pad=None,
             format=None,
             nnz=None,
             is_static=None,
@@ -844,6 +845,12 @@ class SpatialPyramidPool(Cfg):
         self.add_keys(locals())
 
 
+@config_class
+class Pad(Cfg):
+    def __init__(self, channels, pad_c, pad_h, pad_w):
+        self.add_keys(locals())
+
+
 @config_class
 class Norm(Cfg):
     def __init__(self,
@@ -1842,6 +1849,25 @@ class SpatialPyramidPoolLayer(LayerBase):
             self.set_cnn_layer(name, 1, output_x, spp_conf.image_conf.channels)
 
 
+@config_layer('pad')
+class PadLayer(LayerBase):
+    def __init__(self, name, inputs, **xargs):
+        super(PadLayer, self).__init__(name, 'pad', 0, inputs=inputs, **xargs)
+        pad = self.inputs[0].pad
+        self.config.inputs[0].pad_conf.pad_c.extend(pad.pad_c)
+        self.config.inputs[0].pad_conf.pad_h.extend(pad.pad_h)
+        self.config.inputs[0].pad_conf.pad_w.extend(pad.pad_w)
+
+        input_layer = self.get_input_layer(0)
+        image_conf = self.config.inputs[0].pad_conf.image_conf
+        parse_image(pad, input_layer.name, image_conf)
+        out_ch = pad.channels + pad.pad_c[0] + pad.pad_c[1]
+        out_h = image_conf.img_size_y + pad.pad_h[0] + pad.pad_h[1]
+        out_w = image_conf.img_size + pad.pad_w[0] + pad.pad_w[1]
+        self.set_cnn_layer(name, out_h, out_w, out_ch)
+        self.config.size = out_ch * out_h * out_w
+
+
 @config_layer('batch_norm')
 class BatchNormLayer(LayerBase):
     layer_type = 'batch_norm'
diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py
index 9b6e5774bc82dc05e14a2565fa9cce98764adf04..56c335a0504d04bf20631d432f21df4e1de27456 100644
--- a/python/paddle/trainer_config_helpers/layers.py
+++ b/python/paddle/trainer_config_helpers/layers.py
@@ -170,6 +170,7 @@ class LayerType(object):
     BLOCK_EXPAND = "blockexpand"
     MAXOUT = "maxout"
     SPP_LAYER = "spp"
+    PAD_LAYER = "pad"
 
     PRINT_LAYER = "print"
     PRIORBOX_LAYER = "priorbox"
@@ -3488,9 +3489,6 @@ def conv_projection(input,
                     groups=1,
                     param_attr=None):
     """
-    ConvProjection with a layer as input.
-    It performs element-wise multiplication with weight.
-
     Different from img_conv_layer and conv_op, conv_projection is an Projection,
     which can be used in mixed_layer and conat_layer. It use cudnn to implement
     conv and only support GPU mode.
@@ -3499,7 +3497,7 @@ def conv_projection(input,
 
     .. code-block:: python
 
-       proj = conv_projection(img=input1,
+       proj = conv_projection(input=input1,
                               filter_size=3,
                               num_filters=64,
                               num_channels=64)
@@ -3582,6 +3580,84 @@ def conv_projection(input,
     return proj
 
 
+@wrap_name_default("pad")
+@layer_support()
+def pad_layer(input,
+              pad_c=None,
+              pad_h=None,
+              pad_w=None,
+              name=None,
+              layer_attr=None):
+    """
+    This operation pads zeros to the input data according to pad_c,pad_h
+    and pad_w. pad_c, pad_h, pad_w specifies the which dimension and size
+    of padding. And the input data shape is NCHW.
+
+    For example, pad_c=[2,3] means padding 2 zeros before the
+    input data and 3 zeros after the input data in channel dimension.
+    pad_h means padding zeros in height dimension. pad_w means padding zeros
+    in width dimension.
+
+    .. code-block:: python
+
+       pad = pad_layer(input=ipt,
+                       pad_c=[4,4],
+                       pad_h=[0,0],
+                       pad_w=[2,2])
+
+    :param input: layer's input.
+    :type input: LayerOutput
+    :param pad_c: padding size in channel dimension.
+    :type pad_c: list|None
+    :param pad_h: padding size in height dimension.
+    :type pad_h: list|None
+    :param pad_w: padding size in width dimension.
+    :type pad_w: list|None
+    :param layer_attr: Extra Layer Attribute.
+    :type layer_attr: ExtraLayerAttribute
+    :param name: layer name.
+    :type name: basestring
+    :return: LayerOutput object.
+    :rtype: LayerOutput
+    """
+    if pad_c is not None:
+        assert isinstance(pad_c, collections.Sequence) and len(pad_c) == 2
+    else:
+        pad_c = [0, 0]
+
+    if pad_h is not None:
+        assert isinstance(pad_h, collections.Sequence) and len(pad_h) == 2
+    else:
+        pad_h = [0, 0]
+
+    if pad_w is not None:
+        assert isinstance(pad_w, collections.Sequence) and len(pad_w) == 2
+    else:
+        pad_w = [0, 0]
+
+    assert input.num_filters is not None
+    in_ch = input.num_filters
+    out_ch = in_ch + pad_c[0] + pad_c[1]
+
+    l = Layer(
+        name=name,
+        type=LayerType.PAD_LAYER,
+        inputs=Input(
+            input.name,
+            pad=Pad(
+                channels=in_ch,
+                pad_c=pad_c,
+                pad_h=pad_h,
+                pad_w=pad_w, )),
+        **ExtraLayerAttribute.to_kwargs(layer_attr))
+    return LayerOutput(
+        name,
+        layer_type=LayerType.PAD_LAYER,
+        parents=[input],
+        num_filters=out_ch,
+        size=l.config.size)
+
+
 @wrap_name_default()
 @layer_support()
 def conv_shift_layer(a, b, name=None, layer_attr=None):
diff --git a/python/paddle/trainer_config_helpers/tests/configs/test_pad.py b/python/paddle/trainer_config_helpers/tests/configs/test_pad.py
new file mode 100644
index 0000000000000000000000000000000000000000..bb5f13410dbbbaeea9e28c271d33a15fb3000dcf
--- /dev/null
+++ b/python/paddle/trainer_config_helpers/tests/configs/test_pad.py
@@ -0,0 +1,21 @@
+from paddle.trainer_config_helpers import *
+
+settings(batch_size=1000, learning_rate=1e-5)
+
+data = data_layer(name='data', size=2304, height=48, width=42)
+
+conv = img_conv_layer(
+    input=data,
+    filter_size=3,
+    num_channels=1,
+    num_filters=16,
+    padding=1,
+    act=LinearActivation(),
+    bias_attr=True)
+
+pool = img_pool_layer(
+    input=conv, num_channels=8, pool_size=2, stride=2, pool_type=MaxPooling())
+
+pad = pad_layer(input=pool, pad_c=[2, 3], pad_h=[1, 2], pad_w=[3, 1])
+
+outputs(pad)