diff --git a/paddle/gserver/layers/SubNestedSequenceLayer.cpp b/paddle/gserver/layers/SubNestedSequenceLayer.cpp
index 443396a14d58bae8c102ac518466d7e3dffaf73c..f875fdea45069787547581df0e7b05f1847cffc9 100644
--- a/paddle/gserver/layers/SubNestedSequenceLayer.cpp
+++ b/paddle/gserver/layers/SubNestedSequenceLayer.cpp
@@ -31,16 +31,22 @@ public:
   void backward(const UpdateCallback& callback = nullptr) override;
 
 private:
-  void calSelectedCols(const MatrixPtr scores,
-                       const int* seqStartPos,
-                       const int* subSeqStartPos);
+  void reorganizeSeqInfo(const ICpuGpuVectorPtr seqStartPos,
+                         const ICpuGpuVectorPtr subSeqStartPos);
+  void calSelectedCols(const MatrixPtr selectedIndices,
+                       const std::vector<std::vector<int>> inputSeqInfo);
   void buildOutputSeqInfo();
 
   std::vector<int> outSeqStartInfo_;
   std::vector<int> outSubSeqStartInfo_;
 
-  MatrixPtr scoreOverInputSeq_;
+  // if the second input of this layer is on GPU memory, copy it to CPU memory.
+  MatrixPtr selIdsCpu_;
+  // reorganize sequenceStartPositions and subSequenceStartPositions altogether
+  // into a 2d vector to facilitate the sequence selection process.
+  std::vector<std::vector<int>> inputSeqInfo_;
 
+  // the final seleted row indices in a batch,
   // rowIdx_ and selectedRows_ actually share a same memory.
   IVectorPtr rowIndice_;
   std::vector<int> selectedRows_;
@@ -57,12 +63,47 @@ bool SubNestedSequenceLayer::init(const LayerMap& layerMap,
   return true;
 }
 
-void SubNestedSequenceLayer::calSelectedCols(const MatrixPtr selected_indices,
-                                             const int* seqStartPos,
-                                             const int* subSeqStartPos) {
+void SubNestedSequenceLayer::reorganizeSeqInfo(
+    const ICpuGpuVectorPtr seqStartPos, const ICpuGpuVectorPtr subSeqStartPos) {
+  int* seqStarts = seqStartPos->getMutableData(false);
+  int* subSeqStarts = subSeqStartPos->getMutableData(false);
+
+  int seqNum = seqStartPos->getSize() - 1;
+  inputSeqInfo_.resize(seqNum, std::vector<int>());
+  int seqIdx = 0;
+  for (size_t i = 0; i < subSeqStartPos->getSize(); ++i) {
+    inputSeqInfo_[seqIdx].push_back(subSeqStarts[i]);
+    if (subSeqStarts[i] == seqStarts[seqIdx + 1]) {
+      seqIdx++;
+      if (seqIdx == seqNum) return;
+      inputSeqInfo_[seqIdx].push_back(subSeqStarts[i]);
+    }
+  }
+}
+
+void SubNestedSequenceLayer::calSelectedCols(
+    const MatrixPtr selectedIndices,
+    const std::vector<std::vector<int>> inputSeqInfo) {
   selectedRows_.clear();
   outSubSeqStartInfo_.resize(1, 0);
   outSeqStartInfo_.resize(1, 0);
+
+  size_t seqNum = selectedIndices->getHeight();
+  size_t beamSize = selectedIndices->getWidth();
+  for (size_t i = 0; i < seqNum; ++i) {
+    for (size_t j = 0; j < beamSize; ++j) {
+      if (selectedIndices->getElement(i, j) == -1.) break;
+      int selSubSeqIdx = selectedIndices->getElement(i, j);
+      CHECK_GT(inputSeqInfo_[i].size() - 1, selSubSeqIdx);
+
+      size_t subSeqLen =
+          inputSeqInfo_[i][selSubSeqIdx + 1] - inputSeqInfo_[i][selSubSeqIdx];
+      for (size_t k = 0; k < subSeqLen; ++k)
+        selectedRows_.push_back(inputSeqInfo_[i][selSubSeqIdx] + k);
+      outSubSeqStartInfo_.push_back(outSubSeqStartInfo_.back() + subSeqLen);
+    }
+    outSeqStartInfo_.push_back(outSubSeqStartInfo_.back());
+  }
 }
 
 void SubNestedSequenceLayer::buildOutputSeqInfo() {
@@ -83,17 +124,35 @@ void SubNestedSequenceLayer::forward(PassType passType) {
   Layer::forward(passType);
 
   const Argument& inputSeq = getInput(0);
-  const MatrixPtr selected_indices = getInputValue(1);
   CHECK(inputSeq.hasSubseq()) << "The first input of SubNestSequence layer "
                               << "must be a nested sequence.";
-  CHECK_EQ(inputSeq.getNumSequences(), selected_indices->getHeight());
-
-  calSelectedCols(selected_indices,
-                  inputSeq.sequenceStartPositions->getMutableData(false),
-                  inputSeq.subSequenceStartPositions->getMutableData(false));
+  const MatrixPtr selectedIndices = getInputValue(1);
+  CHECK_EQ(inputSeq.getNumSequences(), selectedIndices->getHeight());
+
+  if (dynamic_cast<GpuMatrix*>(selectedIndices.get())) {
+    /*
+     * Currently, the second input for this layer generated by
+     * kmax_sequence_score_layer whose output is always stored on CPU,
+     * or a data_layer which canbe on GPU.
+     *
+     * If the second input is on GPU, copy it to CPU memory, because this
+     * input always uses very few memory, and operations related to it are
+     * all logic control, not computations.
+     */
+    Matrix::resizeOrCreate(selIdsCpu_,
+                           selectedIndices->getHeight(),
+                           selectedIndices->getWidth(),
+                           false /* trans */,
+                           false /* useGpu */);
+    selIdsCpu_->copyFrom(*selectedIndices);
+  } else {
+    selIdsCpu_ = selectedIndices;
+  }
 
+  reorganizeSeqInfo(inputSeq.sequenceStartPositions,
+                    inputSeq.subSequenceStartPositions);
+  calSelectedCols(selIdsCpu_, inputSeqInfo_);
   resetOutput(selectedRows_.size(), getSize());
-  buildOutputSeqInfo();
 
   if (useGpu_) {
     rowIndice_ = IVector::create(selectedRows_.size(), useGpu_);
@@ -103,6 +162,7 @@ void SubNestedSequenceLayer::forward(PassType passType) {
         IVector::create(selectedRows_.data(), selectedRows_.size(), useGpu_);
   }
 
+  buildOutputSeqInfo();
   getOutputValue()->selectRows(*getInputValue(0), *rowIndice_);
 }
 
diff --git a/paddle/gserver/tests/test_LayerGrad.cpp b/paddle/gserver/tests/test_LayerGrad.cpp
index da546b979e49aef4330feb1e5fef8d86fa018a44..0f312b6ca50bc1e6317251ba785f1c61a224b54e 100644
--- a/paddle/gserver/tests/test_LayerGrad.cpp
+++ b/paddle/gserver/tests/test_LayerGrad.cpp
@@ -32,1887 +32,1872 @@ DECLARE_double(checkgrad_eps);
 DECLARE_bool(thread_local_rand_use_global_seed);
 DECLARE_bool(prev_batch_state);
 
-// 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, slice) {
-//   ProjectionConfig conf;
-//   conf.set_type("slice");
-//   conf.set_input_size(100);
-//   SliceConfig& slice1 = *conf.add_slices();
-//   slice1.set_start(10);
-//   slice1.set_end(20);
-//   SliceConfig& slice2 = *conf.add_slices();
-//   slice2.set_start(50);
-//   slice2.set_end(70);
-//   conf.set_output_size(30);
-//   for (auto useGpu : {false, true}) {
-//     testProjectionGrad(conf,
-//                        INPUT_DATA,
-//                        /* parameterSize */ 0,
-//                        /* batchSize */ 10,
-//                        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, bool isDeconv) {
-//   const int NUM_FILTERS = 18;
-//   const int FILTER_SIZE = 2;
-//   const int FILTER_SIZE_Y = 4;
-//   const int CHANNELS = 3;
-//   const int IMAGE_SIZE = 16;
-//
-//   ProjectionConfig conf;
-//   if (isDeconv) {
-//     conf.set_type("convt");
-//   } else {
-//     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);
-//   if (isDeconv) {
-//     conv->set_filter_channels(NUM_FILTERS / conv->groups());
-//   } else {
-//     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);
-//   conv->set_output_y(output_y);
-//   if (isDeconv) {
-//     conf.set_input_size(output_x * output_y * CHANNELS);
-//     conf.set_output_size(IMAGE_SIZE * IMAGE_SIZE * NUM_FILTERS);
-//   } else {
-//     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) {
-//   /// test ConvProjection
-//   testProjectionConv(1, false);
-//   testProjectionConv(3, false);
-//   /// test ConvTransProjection
-//   testProjectionConv(1, true);
-//   testProjectionConv(3, true);
-// }
-// #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, 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 testDepthwiseConvLayer(const string& type, bool useGpu) {
-//   TestConfig config;
-//   config.biasSize = 32;
-//   config.layerConfig.set_type(type);
-//   config.layerConfig.set_num_filters(32);
-//   config.layerConfig.set_partial_sum(1);
-//   config.layerConfig.set_shared_biases(true);
-//
-//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 2048, 192});
-//   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(16);
-//   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, "depthwise_conv", 100, false, useGpu);
-//   // Use small batch_size and useWeight=true to test biasGrad
-//   testLayerGrad(config, "depthwise_conv", 2, false, useGpu, true, 0.02);
-// }
-//
-// TEST(Layer, depthwiseConvLayer) {
-//   //  'depthwise_conv' is a sepecial case of 'exconv' whose
-//   //  groups size equals to the input channels size.
-//   testDepthwiseConvLayer("exconv", /* useGpu= */ false);
-// #ifndef PADDLE_ONLY_CPU
-//   testDepthwiseConvLayer("exconv", /* useGpu= */ true);
-// #endif
-// }
-//
-// 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, 384});
-//   LayerInputConfig* input = config.layerConfig.add_inputs();
-//   ConvConfig* conv = input->mutable_conv_conf();
-//   conv->set_filter_size(2);
-//   conv->set_filter_size_y(4);
-//   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);
-//   }
-// #ifndef PADDLE_ONLY_CPU
-//   testConvTransLayer("cudnn_convt", /* trans= */ false, /* useGpu= */ true);
-// #endif
-// }
-//
-// 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,
-//                       int stride) {
-//   TestConfig config;
-//   config.layerConfig.set_type(layer_type);
-//   config.layerConfig.set_size(10);
-//   config.layerConfig.set_seq_pool_stride(stride);
-//   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
-//                 << " seq_pool_stride=" << stride;
-//       config.layerConfig.set_average_strategy(strategy);
-//       testDegradeLayerGrad(config, layer_type);
-//     }
-//   } else {
-//     LOG(INFO) << " hasSubseq=" << hasSubseq << " trans_type=" << trans_type
-//               << " seq_pool_stride=" << stride;
-//     testDegradeLayerGrad(config, layer_type);
-//   }
-// }
-//
-// TEST(Layer, MaxLayer) {
-//   testDegradeLayer(false, "max", "non-seq", -1);  // seq max to non-seq
-//   testDegradeLayer(false,
-//                    "max",
-//                    "non-seq",
-//                    5);  // seq max to a shorten seq, stride window = 5
-//   testDegradeLayer(true, "max", "non-seq", -1);  // hasSubseq max to non-seq
-//   testDegradeLayer(true, "max", "seq", -1);      // hasSubseq max to seq
-// }
-//
-// TEST(Layer, SequenceLastInstanceLayer) {
-//   testDegradeLayer(false,
-//                    "seqlastins",
-//                    "non-seq",
-//                    -1);  // seq seqlastins to non-seq
-//   testDegradeLayer(false,
-//                    "seqlastins",
-//                    "non-seq",
-//                    5);  // seq seqlastins to a shorten seq, stride window = 5
-//   testDegradeLayer(true,
-//                    "seqlastins",
-//                    "non-seq",
-//                    -1);  // hasSubseq seqlastins to non-seq
-//   testDegradeLayer(
-//       true, "seqlastins", "seq", -1);  // hasSubseq seqlastins to seq
-// }
-//
-// TEST(Layer, AverageLayer) {
-//   testDegradeLayer(false, "average", "non-seq", -1);  // seq average to
-//   non-seq
-//   testDegradeLayer(false,
-//                    "average",
-//                    "non-seq",
-//                    5);  // seq average to a shorten seq, stride window = 5
-//   testDegradeLayer(
-//       true, "average", "non-seq", -1);           // hasSubseq average to
-//       non-seq
-//   testDegradeLayer(true, "average", "seq", -1);  // 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, RotateLayer) {
-//   TestConfig config;
-//   config.biasSize = 0;
-//   config.layerConfig.set_type("rotate");
-//   const int CHANNEL = 2;
-//   const int HEIGHT = 8;
-//   const int WIDTH = 4;
-//   const int INPUT_SIZE = HEIGHT * WIDTH * CHANNEL;
-//   config.layerConfig.set_size(INPUT_SIZE);
-//   config.layerConfig.set_height(HEIGHT);
-//   config.layerConfig.set_width(WIDTH);
-//   config.inputDefs.push_back({INPUT_DATA, "layer_0", INPUT_SIZE, 0});
-//   config.layerConfig.add_inputs();
-//
-//   for (auto useGpu : {false, true}) {
-//     testLayerGrad(config, "rotate", 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
-// }
-//
-// void testConvOperator(bool isDeconv) {
-//   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 = 9;
-//   OperatorConfig& operatorConf = *config.layerConfig.add_operator_confs();
-//   if (isDeconv) {
-//     operatorConf.set_type("convt");
-//   } else {
-//     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_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));
-//
-//   if (isDeconv) {
-//     conv->set_filter_channels(NUM_FILTERS / conv->groups());
-//     config.inputDefs.push_back({INPUT_DATA,
-//                                 "layer_0",
-//                                 conv->output_x() * conv->output_y() *
-//                                 CHANNELS,
-//                                 0});
-//     config.layerConfig.set_size(IMAGE_SIZE * IMAGE_SIZE_Y * NUM_FILTERS);
-//   } else {
-//     conv->set_filter_channels(conv->channels() / conv->groups());
-//     config.inputDefs.push_back(
-//         {INPUT_DATA, "layer_0", IMAGE_SIZE * IMAGE_SIZE_Y * CHANNELS, 0});
-//     config.layerConfig.set_size(conv->output_x() * conv->output_y() *
-//                                 NUM_FILTERS);
-//   }
-//
-//   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(Operator, conv) {
-//   testConvOperator(/*isDeconv*/ true);
-//   testConvOperator(/*isDeconv*/ 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}) {
-//     for (auto asRowVec : {false, true}) {
-//       config.layerConfig.set_user_arg(asRowVec ? "as_row_vec" :
-//       "as_col_vec");
-//       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.biasSize = 0;
-//   config.layerConfig.set_type("pad");
-//
-//   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();
-//   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, "pad", 10, false, useGpu);
-//   }
-// }
-//
-// TEST(Layer, CrossChannelNormLayer) {
-//   TestConfig config;
-//   config.paramInitialMean = 1.;
-//   config.paramInitialStd = 0.;
-//   config.layerConfig.set_type("norm");
-//   config.layerConfig.set_size(100);
-//   LayerInputConfig* input = config.layerConfig.add_inputs();
-//   NormConfig* norm = input->mutable_norm_conf();
-//   norm->set_norm_type("cross-channel-norm");
-//   norm->set_channels(10);
-//   norm->set_size(100);
-//   norm->set_scale(0);
-//   norm->set_pow(0);
-//   norm->set_blocked(0);
-//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 100, 10});
-//
-//   for (auto useGpu : {false, true}) {
-//     testLayerGrad(config, "cross-channel-norm", 10, false, useGpu, false);
-//   }
-// }
-//
-// TEST(Layer, smooth_l1) {
-//   TestConfig config;
-//   config.layerConfig.set_type("smooth_l1");
-//
-//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 200, 0});
-//   config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 200, 0});
-//   config.layerConfig.add_inputs();
-//   config.layerConfig.add_inputs();
-//
-//   for (auto useGpu : {false, true}) {
-//     testLayerGrad(config, "smooth_l1", 100, false, useGpu, false);
-//   }
-// }
-//
-// TEST(Layer, multibox_loss) {
-//   TestConfig config;
-//   config.layerConfig.set_type("multibox_loss");
-//   config.biasSize = 0;
-//   LayerInputConfig* input = config.layerConfig.add_inputs();
-//   MultiBoxLossConfig* multiboxLoss = input->mutable_multibox_loss_conf();
-//   multiboxLoss->set_num_classes(21);
-//   multiboxLoss->set_input_num(1);
-//   multiboxLoss->set_overlap_threshold(0.5);
-//   multiboxLoss->set_neg_pos_ratio(3);
-//   multiboxLoss->set_neg_overlap(0.5);
-//   multiboxLoss->set_background_id(0);
-//   multiboxLoss->set_height(3);
-//   multiboxLoss->set_width(3);
-//
-//   size_t gtNum = 1;
-//   MatrixPtr labelValue = Matrix::create(gtNum, 6, false, false);
-//   labelValue->randomizeUniform();
-//   labelValue->add(-0.5);
-//   labelValue->sigmoid(*labelValue);
-//   real* labelData = labelValue->getData();
-//   size_t labelWidth = labelValue->getWidth();
-//   for (size_t i = 0; i < gtNum; ++i) {
-//     *(labelData + i * labelWidth) = std::rand() % 20 + 1;
-//     *(labelData + i * labelWidth + 1) = 0.400259;
-//     *(labelData + i * labelWidth + 2) = 0.377857;
-//     *(labelData + i * labelWidth + 3) = 0.525712;
-//     *(labelData + i * labelWidth + 4) = 0.519368;
-//   }
-//   vector<int> seqStartPositions(gtNum + 1, 0);
-//   for (size_t i = 1; i <= gtNum; ++i) {
-//     seqStartPositions[i] = i;
-//   }
-//
-//   // Ensure at lease one matched bbox
-//   MatrixPtr priorValue = Matrix::create(1, 72, false, false);
-//   priorValue->randomizeUniform();
-//   priorValue->add(-0.5);
-//   priorValue->sigmoid(*priorValue);
-//   real* priorData = priorValue->getData();
-//   *(priorData) = 0.424811;
-//   *(priorData + 1) = 0.397059;
-//   *(priorData + 2) = 0.538905;
-//   *(priorData + 3) = 0.447091;
-//   *(priorData + 4) = 0.425720;
-//   *(priorData + 5) = 0.515228;
-//   *(priorData + 6) = 0.519452;
-//   *(priorData + 7) = 0.591065;
-//
-//   config.inputDefs.push_back(
-//       {INPUT_SELF_DEFINE_DATA, "priorbox", priorValue, {}});
-//   config.inputDefs.push_back(
-//       {INPUT_SELF_DEFINE_DATA, "label", labelValue, seqStartPositions});
-//   config.inputDefs.push_back({INPUT_DATA, "locPred", 36, 0});
-//   config.inputDefs.push_back({INPUT_DATA, "confPred", 189, 0});
-//   config.layerConfig.add_inputs();
-//   config.layerConfig.add_inputs();
-//   config.layerConfig.add_inputs();
-//
-//   for (auto useGpu : {false, true}) {
-//     testLayerGrad(config, "multibox_loss", 1, false, useGpu, false);
-//   }
-// }
-//
-// TEST(Layer, TransLayer) {
-//   TestConfig config;
-//   const int height = 128;
-//   const int width = 1028;
-//   config.layerConfig.set_type("trans");
-//   config.layerConfig.set_size(width);
-//
-//   config.inputDefs.push_back(
-//       {INPUT_DATA, "layer_0", /* dim= */ height * width, /* paraSize= */ 0});
-//   config.layerConfig.add_inputs();
-//
-//   for (auto useGpu : {false, true}) {
-//     testLayerGrad(config, "trans", height, /* trans= */ false, useGpu);
-//   }
-// }
-//
-// TEST(Layer, RowConvLayer) {
-//   const int context = 3;
-//   const int size = 512;
-//
-//   TestConfig config;
-//   config.layerConfig.set_type("row_conv");
-//   config.layerConfig.set_size(size);
-//   config.layerConfig.set_active_type("sigmoid");
-//
-//   config.inputDefs.push_back(
-//       {INPUT_SEQUENCE_DATA, "layer_0", size, context * size});
-//   LayerInputConfig* input = config.layerConfig.add_inputs();
-//   RowConvConfig* conv = input->mutable_row_conv_conf();
-//   conv->set_context_length(context);
-//
-//   for (auto useGpu : {false, true}) {
-//     testLayerGrad(config, "row_conv", 100, false, useGpu, false);
-//   }
-// }
-//
-// TEST(Layer, CropLayer) {
-//   TestConfig config;
-//   // config input_0
-//   config.inputDefs.push_back({INPUT_DATA, "layer_0", 1024, 0});
-//   LayerInputConfig* input = config.layerConfig.add_inputs();
-//   ImageConfig* img = input->mutable_image_conf();
-//   img->set_channels(4);
-//   img->set_img_size(16);
-//   config.layerConfig.set_axis(2);
-//   config.layerConfig.add_offset(0);
-//   config.layerConfig.add_offset(0);
-//
-//   // config input_1
-//   config.inputDefs.push_back({INPUT_DATA, "layer_1", 128, 0});
-//   input = config.layerConfig.add_inputs();
-//   img = input->mutable_image_conf();
-//   img->set_channels(2);
-//   img->set_img_size(8);
-//
-//   // config crop layer
-//   config.layerConfig.set_type("crop");
-//   config.layerConfig.set_name("cropLayer");
-//
-//   for (auto useGpu : {false, true}) {
-//     testLayerGrad(config, "crop", 100, false, useGpu, false);
-//   }
-// }
+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, slice) {
+  ProjectionConfig conf;
+  conf.set_type("slice");
+  conf.set_input_size(100);
+  SliceConfig& slice1 = *conf.add_slices();
+  slice1.set_start(10);
+  slice1.set_end(20);
+  SliceConfig& slice2 = *conf.add_slices();
+  slice2.set_start(50);
+  slice2.set_end(70);
+  conf.set_output_size(30);
+  for (auto useGpu : {false, true}) {
+    testProjectionGrad(conf,
+                       INPUT_DATA,
+                       /* parameterSize */ 0,
+                       /* batchSize */ 10,
+                       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, bool isDeconv) {
+  const int NUM_FILTERS = 18;
+  const int FILTER_SIZE = 2;
+  const int FILTER_SIZE_Y = 4;
+  const int CHANNELS = 3;
+  const int IMAGE_SIZE = 16;
+
+  ProjectionConfig conf;
+  if (isDeconv) {
+    conf.set_type("convt");
+  } else {
+    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);
+  if (isDeconv) {
+    conv->set_filter_channels(NUM_FILTERS / conv->groups());
+  } else {
+    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);
+  conv->set_output_y(output_y);
+  if (isDeconv) {
+    conf.set_input_size(output_x * output_y * CHANNELS);
+    conf.set_output_size(IMAGE_SIZE * IMAGE_SIZE * NUM_FILTERS);
+  } else {
+    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) {
+  /// test ConvProjection
+  testProjectionConv(1, false);
+  testProjectionConv(3, false);
+  /// test ConvTransProjection
+  testProjectionConv(1, true);
+  testProjectionConv(3, true);
+}
+#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, 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 testDepthwiseConvLayer(const string& type, bool useGpu) {
+  TestConfig config;
+  config.biasSize = 32;
+  config.layerConfig.set_type(type);
+  config.layerConfig.set_num_filters(32);
+  config.layerConfig.set_partial_sum(1);
+  config.layerConfig.set_shared_biases(true);
+
+  config.inputDefs.push_back({INPUT_DATA, "layer_0", 2048, 192});
+  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(16);
+  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, "depthwise_conv", 100, false, useGpu);
+  // Use small batch_size and useWeight=true to test biasGrad
+  testLayerGrad(config, "depthwise_conv", 2, false, useGpu, true, 0.02);
+}
+
+TEST(Layer, depthwiseConvLayer) {
+  //  'depthwise_conv' is a sepecial case of 'exconv' whose
+  //  groups size equals to the input channels size.
+  testDepthwiseConvLayer("exconv", /* useGpu= */ false);
+#ifndef PADDLE_ONLY_CPU
+  testDepthwiseConvLayer("exconv", /* useGpu= */ true);
+#endif
+}
+
+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, 384});
+  LayerInputConfig* input = config.layerConfig.add_inputs();
+  ConvConfig* conv = input->mutable_conv_conf();
+  conv->set_filter_size(2);
+  conv->set_filter_size_y(4);
+  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);
+  }
+#ifndef PADDLE_ONLY_CPU
+  testConvTransLayer("cudnn_convt", /* trans= */ false, /* useGpu= */ true);
+#endif
+}
+
+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,
+                      int stride) {
+  TestConfig config;
+  config.layerConfig.set_type(layer_type);
+  config.layerConfig.set_size(10);
+  config.layerConfig.set_seq_pool_stride(stride);
+  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
+                << " seq_pool_stride=" << stride;
+      config.layerConfig.set_average_strategy(strategy);
+      testDegradeLayerGrad(config, layer_type);
+    }
+  } else {
+    LOG(INFO) << " hasSubseq=" << hasSubseq << " trans_type=" << trans_type
+              << " seq_pool_stride=" << stride;
+    testDegradeLayerGrad(config, layer_type);
+  }
+}
+
+TEST(Layer, MaxLayer) {
+  testDegradeLayer(false, "max", "non-seq", -1);  // seq max to non-seq
+  testDegradeLayer(false,
+                   "max",
+                   "non-seq",
+                   5);  // seq max to a shorten seq, stride window = 5
+  testDegradeLayer(true, "max", "non-seq", -1);  // hasSubseq max to non-seq
+  testDegradeLayer(true, "max", "seq", -1);      // hasSubseq max to seq
+}
+
+TEST(Layer, SequenceLastInstanceLayer) {
+  testDegradeLayer(false,
+                   "seqlastins",
+                   "non-seq",
+                   -1);  // seq seqlastins to non-seq
+  testDegradeLayer(false,
+                   "seqlastins",
+                   "non-seq",
+                   5);  // seq seqlastins to a shorten seq, stride window = 5
+  testDegradeLayer(true,
+                   "seqlastins",
+                   "non-seq",
+                   -1);  // hasSubseq seqlastins to non-seq
+  testDegradeLayer(
+      true, "seqlastins", "seq", -1);  // hasSubseq seqlastins to seq
+}
+
+TEST(Layer, AverageLayer) {
+  testDegradeLayer(false, "average", "non-seq", -1);  // seq average to non-seq
+  testDegradeLayer(false,
+                   "average",
+                   "non-seq",
+                   5);  // seq average to a shorten seq, stride window = 5
+  testDegradeLayer(
+      true, "average", "non-seq", -1);           // hasSubseq average to non-seq
+  testDegradeLayer(true, "average", "seq", -1);  // 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, RotateLayer) {
+  TestConfig config;
+  config.biasSize = 0;
+  config.layerConfig.set_type("rotate");
+  const int CHANNEL = 2;
+  const int HEIGHT = 8;
+  const int WIDTH = 4;
+  const int INPUT_SIZE = HEIGHT * WIDTH * CHANNEL;
+  config.layerConfig.set_size(INPUT_SIZE);
+  config.layerConfig.set_height(HEIGHT);
+  config.layerConfig.set_width(WIDTH);
+  config.inputDefs.push_back({INPUT_DATA, "layer_0", INPUT_SIZE, 0});
+  config.layerConfig.add_inputs();
+
+  for (auto useGpu : {false, true}) {
+    testLayerGrad(config, "rotate", 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
+}
+
+void testConvOperator(bool isDeconv) {
+  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 = 9;
+  OperatorConfig& operatorConf = *config.layerConfig.add_operator_confs();
+  if (isDeconv) {
+    operatorConf.set_type("convt");
+  } else {
+    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_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));
+
+  if (isDeconv) {
+    conv->set_filter_channels(NUM_FILTERS / conv->groups());
+    config.inputDefs.push_back({INPUT_DATA,
+                                "layer_0",
+                                conv->output_x() * conv->output_y() * CHANNELS,
+                                0});
+    config.layerConfig.set_size(IMAGE_SIZE * IMAGE_SIZE_Y * NUM_FILTERS);
+  } else {
+    conv->set_filter_channels(conv->channels() / conv->groups());
+    config.inputDefs.push_back(
+        {INPUT_DATA, "layer_0", IMAGE_SIZE * IMAGE_SIZE_Y * CHANNELS, 0});
+    config.layerConfig.set_size(conv->output_x() * conv->output_y() *
+                                NUM_FILTERS);
+  }
+
+  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(Operator, conv) {
+  testConvOperator(/*isDeconv*/ true);
+  testConvOperator(/*isDeconv*/ 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}) {
+    for (auto asRowVec : {false, true}) {
+      config.layerConfig.set_user_arg(asRowVec ? "as_row_vec" : "as_col_vec");
+      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.biasSize = 0;
+  config.layerConfig.set_type("pad");
+
+  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();
+  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, "pad", 10, false, useGpu);
+  }
+}
+
+TEST(Layer, CrossChannelNormLayer) {
+  TestConfig config;
+  config.paramInitialMean = 1.;
+  config.paramInitialStd = 0.;
+  config.layerConfig.set_type("norm");
+  config.layerConfig.set_size(100);
+  LayerInputConfig* input = config.layerConfig.add_inputs();
+  NormConfig* norm = input->mutable_norm_conf();
+  norm->set_norm_type("cross-channel-norm");
+  norm->set_channels(10);
+  norm->set_size(100);
+  norm->set_scale(0);
+  norm->set_pow(0);
+  norm->set_blocked(0);
+  config.inputDefs.push_back({INPUT_DATA, "layer_0", 100, 10});
+
+  for (auto useGpu : {false, true}) {
+    testLayerGrad(config, "cross-channel-norm", 10, false, useGpu, false);
+  }
+}
+
+TEST(Layer, smooth_l1) {
+  TestConfig config;
+  config.layerConfig.set_type("smooth_l1");
+
+  config.inputDefs.push_back({INPUT_DATA, "layer_0", 200, 0});
+  config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 200, 0});
+  config.layerConfig.add_inputs();
+  config.layerConfig.add_inputs();
+
+  for (auto useGpu : {false, true}) {
+    testLayerGrad(config, "smooth_l1", 100, false, useGpu, false);
+  }
+}
+
+TEST(Layer, multibox_loss) {
+  TestConfig config;
+  config.layerConfig.set_type("multibox_loss");
+  config.biasSize = 0;
+  LayerInputConfig* input = config.layerConfig.add_inputs();
+  MultiBoxLossConfig* multiboxLoss = input->mutable_multibox_loss_conf();
+  multiboxLoss->set_num_classes(21);
+  multiboxLoss->set_input_num(1);
+  multiboxLoss->set_overlap_threshold(0.5);
+  multiboxLoss->set_neg_pos_ratio(3);
+  multiboxLoss->set_neg_overlap(0.5);
+  multiboxLoss->set_background_id(0);
+  multiboxLoss->set_height(3);
+  multiboxLoss->set_width(3);
+
+  size_t gtNum = 1;
+  MatrixPtr labelValue = Matrix::create(gtNum, 6, false, false);
+  labelValue->randomizeUniform();
+  labelValue->add(-0.5);
+  labelValue->sigmoid(*labelValue);
+  real* labelData = labelValue->getData();
+  size_t labelWidth = labelValue->getWidth();
+  for (size_t i = 0; i < gtNum; ++i) {
+    *(labelData + i * labelWidth) = std::rand() % 20 + 1;
+    *(labelData + i * labelWidth + 1) = 0.400259;
+    *(labelData + i * labelWidth + 2) = 0.377857;
+    *(labelData + i * labelWidth + 3) = 0.525712;
+    *(labelData + i * labelWidth + 4) = 0.519368;
+  }
+  vector<int> seqStartPositions(gtNum + 1, 0);
+  for (size_t i = 1; i <= gtNum; ++i) {
+    seqStartPositions[i] = i;
+  }
+
+  // Ensure at lease one matched bbox
+  MatrixPtr priorValue = Matrix::create(1, 72, false, false);
+  priorValue->randomizeUniform();
+  priorValue->add(-0.5);
+  priorValue->sigmoid(*priorValue);
+  real* priorData = priorValue->getData();
+  *(priorData) = 0.424811;
+  *(priorData + 1) = 0.397059;
+  *(priorData + 2) = 0.538905;
+  *(priorData + 3) = 0.447091;
+  *(priorData + 4) = 0.425720;
+  *(priorData + 5) = 0.515228;
+  *(priorData + 6) = 0.519452;
+  *(priorData + 7) = 0.591065;
+
+  config.inputDefs.push_back(
+      {INPUT_SELF_DEFINE_DATA, "priorbox", priorValue, {}});
+  config.inputDefs.push_back(
+      {INPUT_SELF_DEFINE_DATA, "label", labelValue, seqStartPositions});
+  config.inputDefs.push_back({INPUT_DATA, "locPred", 36, 0});
+  config.inputDefs.push_back({INPUT_DATA, "confPred", 189, 0});
+  config.layerConfig.add_inputs();
+  config.layerConfig.add_inputs();
+  config.layerConfig.add_inputs();
+
+  for (auto useGpu : {false, true}) {
+    testLayerGrad(config, "multibox_loss", 1, false, useGpu, false);
+  }
+}
+
+TEST(Layer, TransLayer) {
+  TestConfig config;
+  const int height = 128;
+  const int width = 1028;
+  config.layerConfig.set_type("trans");
+  config.layerConfig.set_size(width);
+
+  config.inputDefs.push_back(
+      {INPUT_DATA, "layer_0", /* dim= */ height * width, /* paraSize= */ 0});
+  config.layerConfig.add_inputs();
+
+  for (auto useGpu : {false, true}) {
+    testLayerGrad(config, "trans", height, /* trans= */ false, useGpu);
+  }
+}
+
+TEST(Layer, RowConvLayer) {
+  const int context = 3;
+  const int size = 512;
+
+  TestConfig config;
+  config.layerConfig.set_type("row_conv");
+  config.layerConfig.set_size(size);
+  config.layerConfig.set_active_type("sigmoid");
+
+  config.inputDefs.push_back(
+      {INPUT_SEQUENCE_DATA, "layer_0", size, context * size});
+  LayerInputConfig* input = config.layerConfig.add_inputs();
+  RowConvConfig* conv = input->mutable_row_conv_conf();
+  conv->set_context_length(context);
+
+  for (auto useGpu : {false, true}) {
+    testLayerGrad(config, "row_conv", 100, false, useGpu, false);
+  }
+}
+
+TEST(Layer, CropLayer) {
+  TestConfig config;
+  // config input_0
+  config.inputDefs.push_back({INPUT_DATA, "layer_0", 1024, 0});
+  LayerInputConfig* input = config.layerConfig.add_inputs();
+  ImageConfig* img = input->mutable_image_conf();
+  img->set_channels(4);
+  img->set_img_size(16);
+  config.layerConfig.set_axis(2);
+  config.layerConfig.add_offset(0);
+  config.layerConfig.add_offset(0);
+
+  // config input_1
+  config.inputDefs.push_back({INPUT_DATA, "layer_1", 128, 0});
+  input = config.layerConfig.add_inputs();
+  img = input->mutable_image_conf();
+  img->set_channels(2);
+  img->set_img_size(8);
+
+  // config crop layer
+  config.layerConfig.set_type("crop");
+  config.layerConfig.set_name("cropLayer");
+
+  for (auto useGpu : {false, true}) {
+    testLayerGrad(config, "crop", 100, false, useGpu, false);
+  }
+}
 
 vector<real> randSampling(real range, int n) {
   CHECK_GE(range, n);
@@ -1929,18 +1914,20 @@ vector<real> randSampling(real range, int n) {
 TEST(Layer, SubNestedSequenceLayer) {
   // layer size is not crutial for this layer,
   // so use a small layer size in unittest
-  const int layerSize = 8;
-  const int maxSeqNum = 5;
-  const int maxSeqLen = 5;
-  const int beamSize = 3;
+  const int layerSize = 4;
+
+  const int maxSeqNum = 50;
+  const int maxSeqLen = 50;
+  const int maxBeamSize = 32;
+
+  srand((size_t)(time(NULL)));
+  int beamSize = 1 + (rand() % maxBeamSize);
 
   TestConfig config;
   config.layerConfig.set_type("sub_nested_seq");
   config.layerConfig.set_name("sub_nested_seq_layer");
   config.layerConfig.set_size(layerSize);
 
-  // srand((size_t)(time(NULL)));
-  srand(1);
   int seqNum = 1 + (rand() % maxSeqNum);
 
   // sequence information for the first input, it is a nested sequence
@@ -1969,6 +1956,7 @@ TEST(Layer, SubNestedSequenceLayer) {
 
   MatrixPtr seqInputPtr =
       Matrix::create(seqStartPos.back(), layerSize, false, false);
+  seqInputPtr->randomizeUniform();
   config.inputDefs.push_back({INPUT_SELF_DEFINE_DATA,
                               "nested_seq_input",
                               seqInputPtr,
@@ -1989,35 +1977,35 @@ TEST(Layer, SubNestedSequenceLayer) {
   }
 }
 
-// TEST(Layer, ClipLayer) {
-//   const size_t batchSize = 128;
-//   const size_t size = 512;
-//   TestConfig config;
-//   config.layerConfig.set_type("clip");
-//   config.inputDefs.push_back({INPUT_DATA, "input", size, 0});
-//   LayerInputConfig* input = config.layerConfig.add_inputs();
-//   ClipConfig* layerConf = input->mutable_clip_conf();
-//   double p1 = std::rand() / (double)RAND_MAX;
-//   double p2 = std::rand() / (double)RAND_MAX;
-//   layerConf->set_min(std::min(p1, p2));
-//   layerConf->set_max(std::max(p1, p2));
-//   for (auto useGpu : {false, true}) {
-//     testLayerGrad(config, "clip", batchSize, false, useGpu, false);
-//   }
-// }
-//
-// TEST(Layer, RowL2NormLayer) {
-//   const size_t batchSize = 128;
-//   const size_t size = 512;
-//   TestConfig config;
-//   config.layerConfig.set_type("row_l2_norm");
-//   config.layerConfig.set_size(size);
-//   config.inputDefs.push_back({INPUT_DATA, "input", size, 0});
-//   config.layerConfig.add_inputs();
-//   for (auto useGpu : {false, true}) {
-//     testLayerGrad(config, "row_l2_norm", batchSize, false, useGpu, false);
-//   }
-// }
+TEST(Layer, ClipLayer) {
+  const size_t batchSize = 128;
+  const size_t size = 512;
+  TestConfig config;
+  config.layerConfig.set_type("clip");
+  config.inputDefs.push_back({INPUT_DATA, "input", size, 0});
+  LayerInputConfig* input = config.layerConfig.add_inputs();
+  ClipConfig* layerConf = input->mutable_clip_conf();
+  double p1 = std::rand() / (double)RAND_MAX;
+  double p2 = std::rand() / (double)RAND_MAX;
+  layerConf->set_min(std::min(p1, p2));
+  layerConf->set_max(std::max(p1, p2));
+  for (auto useGpu : {false, true}) {
+    testLayerGrad(config, "clip", batchSize, false, useGpu, false);
+  }
+}
+
+TEST(Layer, RowL2NormLayer) {
+  const size_t batchSize = 128;
+  const size_t size = 512;
+  TestConfig config;
+  config.layerConfig.set_type("row_l2_norm");
+  config.layerConfig.set_size(size);
+  config.inputDefs.push_back({INPUT_DATA, "input", size, 0});
+  config.layerConfig.add_inputs();
+  for (auto useGpu : {false, true}) {
+    testLayerGrad(config, "row_l2_norm", batchSize, false, useGpu, false);
+  }
+}
 
 int main(int argc, char** argv) {
   testing::InitGoogleTest(&argc, argv);
diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py
index ebbe95a0c72b9d4e137df1ca3e66add3b4d8b4ed..2bed2b5f458ba04d7b98b6317c760cea424246ff 100755
--- a/python/paddle/trainer_config_helpers/layers.py
+++ b/python/paddle/trainer_config_helpers/layers.py
@@ -6097,9 +6097,11 @@ def sub_nested_seq_layer(input, selected_indices, name=None):
     The sub_nested_seq_layer accepts two inputs: the first one is a nested
     sequence; the second one is a set of selceted indices in the nested sequence.
 
+    Then sub_nest_seq_layer trims the first nested sequence input according to
+    the selected indices to form a new output.
+
+    This layer is useful in beam training.
 
-    Then sub_nest_seq_layer selects trims the first input according to the
-    selected indices to give a new output. This layer is used in beam training.
 
     The example usage is: