test_LayerGrad.cpp 61.2 KB
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */

#include <gtest/gtest.h>
#include <string>
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#include <vector>
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#include "ModelConfig.pb.h"
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#include "paddle/gserver/layers/DataLayer.h"
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#include "paddle/math/MathUtils.h"
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#include "paddle/trainer/Trainer.h"
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#include "LayerGradUtil.h"
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#include "paddle/testing/TestUtil.h"
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using namespace paddle;  // NOLINT
using namespace std;     // NOLINT

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DECLARE_bool(use_gpu);
DECLARE_int32(gpu_id);
DECLARE_double(checkgrad_eps);
DECLARE_bool(thread_local_rand_use_global_seed);
DECLARE_bool(prev_batch_state);
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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}) {
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      for (auto batchSize : {1, 2, 5, 20, 50}) {
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        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(
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                conf,
                INPUT_SEQUENCE_DATA,
                trainablePadding ? conf.input_size() * pad : 0,
                batchSize,
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                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}) {
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    testProjectionGrad(conf,
                       INPUT_DATA,
                       /* parameterSize */ 1000,
                       /* batchSize */ 100,
                       useGpu);
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  }
}

TEST(Projection, fc) {
  ProjectionConfig conf;
  conf.set_type("fc");
  conf.set_input_size(10);
  conf.set_output_size(20);
  for (auto useGpu : {false, true}) {
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    testProjectionGrad(conf,
                       INPUT_DATA,
                       /* parameterSize */ 200,
                       /* batchSize */ 100,
                       useGpu);
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  }
}

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}) {
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    testProjectionGrad(conf,
                       INPUT_DATA,
                       /* parameterSize */ 20,
                       /* batchSize */ 100,
                       useGpu);
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  }
}

TEST(Projection, table) {
  ProjectionConfig conf;
  conf.set_type("table");
  conf.set_input_size(10);
  conf.set_output_size(20);
  for (auto useGpu : {false, true}) {
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    testProjectionGrad(conf,
                       INPUT_LABEL,
                       /* parameterSize */ 200,
                       /* batchSize */ 100,
                       useGpu);
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  }
}

TEST(Projection, identity) {
  ProjectionConfig conf;
  conf.set_type("identity");
  conf.set_input_size(10);
  conf.set_output_size(10);
  for (auto useGpu : {false, true}) {
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    testProjectionGrad(conf,
                       INPUT_DATA,
                       /* parameterSize */ 0,
                       /* batchSize */ 100,
                       useGpu);
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  }
}

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TEST(Projection, scaling) {
  ProjectionConfig conf;
  conf.set_type("scaling");
  conf.set_input_size(10);
  conf.set_output_size(10);
  for (auto useGpu : {false}) {
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    testProjectionGrad(conf,
                       INPUT_DATA,
                       /* parameterSize */ 1,
                       /* batchSize */ 100,
                       useGpu);
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  }
}

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void testProjectionConv(size_t groups, bool isDeconv) {
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  const int NUM_FILTERS = 18;
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  const int FILTER_SIZE = 2;
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  const int FILTER_SIZE_Y = 4;
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  const int CHANNELS = 3;
  const int IMAGE_SIZE = 16;

  ProjectionConfig conf;
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  if (isDeconv) {
    conf.set_type("convt");
  } else {
    conf.set_type("conv");
  }
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  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);
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  conv->set_groups(groups);
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  if (isDeconv) {
    conv->set_filter_channels(NUM_FILTERS / conv->groups());
  } else {
    conv->set_filter_channels(conv->channels() / conv->groups());
  }
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  conv->set_img_size(IMAGE_SIZE);
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  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);
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  conv->set_output_x(output_x);
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  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);
  }
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  testProjectionGrad(conf,
                     INPUT_DATA,
                     /* parameterSize */ NUM_FILTERS * CHANNELS * FILTER_SIZE *
                         FILTER_SIZE_Y / groups,
                     /* batchSize */ 100,
                     true,
                     false,
                     NUM_FILTERS,
                     true);
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}
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#ifndef PADDLE_ONLY_CPU
TEST(Projection, conv) {
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  /// test ConvProjection
  testProjectionConv(1, false);
  testProjectionConv(3, false);
  /// test ConvTransProjection
  testProjectionConv(1, true);
  testProjectionConv(3, true);
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}
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#endif

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TEST(Layer, BilinearInterpLayer) {
  TestConfig config;
  config.layerConfig.set_type("bilinear_interp");
  config.biasSize = 0;
  config.inputDefs.push_back({INPUT_DATA, "layer_0", 4096, 0});

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  LayerInputConfig* input = config.layerConfig.add_inputs();
  BilinearInterpConfig* bilinear = input->mutable_bilinear_interp_conf();
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  ImageConfig* image = bilinear->mutable_image_conf();
  image->set_img_size(32);
  image->set_img_size_y(32);
  image->set_channels(4);
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  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);
    }
  }
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}

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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}) {
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    testLayerGrad(config,
                  "ctc",
                  100,
                  /* trans */ false, /* useGpu */
                  useGpu);
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  }
}

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);
  }
}

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void testDepthwiseConvLayer(const string& type, bool useGpu) {
  TestConfig config;
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  config.biasSize = 32;
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  config.layerConfig.set_type(type);
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  config.layerConfig.set_num_filters(32);
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  config.layerConfig.set_partial_sum(1);
  config.layerConfig.set_shared_biases(true);

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  config.inputDefs.push_back({INPUT_DATA, "layer_0", 2048, 192});
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  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) {
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  //  'depthwise_conv' is a sepecial case of 'exconv' whose
  //  groups size equals to the input channels size.
  testDepthwiseConvLayer("exconv", /* useGpu= */ false);
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#ifndef PADDLE_ONLY_CPU
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  testDepthwiseConvLayer("exconv", /* useGpu= */ true);
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#endif
}

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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);

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  config.inputDefs.push_back({INPUT_DATA, "layer_0", 384, 288});
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  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);
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  conv->set_img_size_y(8);
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  conv->set_output_x(outputSize(conv->img_size(),
                                conv->filter_size(),
                                conv->padding(),
                                conv->stride(),
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                                /* caffeMode */ true));
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  conv->set_output_y(outputSize(conv->img_size_y(),
                                conv->filter_size_y(),
                                conv->padding_y(),
                                conv->stride_y(),
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                                /* caffeMode */ true));
  config.layerConfig.set_size(conv->output_x() * conv->output_y() *
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                              config.layerConfig.num_filters());

  testLayerGrad(config, "conv", 100, trans, useGpu);
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  // Use small batch_size and useWeight=true to test biasGrad
  testLayerGrad(config, "conv", 2, trans, useGpu, true, 0.02);
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}

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
}

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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);

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  config.inputDefs.push_back({INPUT_DATA, "layer_0", 1024, 384});
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  LayerInputConfig* input = config.layerConfig.add_inputs();
  ConvConfig* conv = input->mutable_conv_conf();
  conv->set_filter_size(2);
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  conv->set_filter_size_y(4);
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  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);
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  conv->set_output_x(outputSize(conv->img_size(),
                                conv->filter_size(),
                                conv->padding(),
                                conv->stride(),
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                                /* caffeMode */ true));
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  config.layerConfig.set_size(conv->img_size() * conv->img_size() *
                              config.layerConfig.num_filters());

  testLayerGrad(config, "convTrans", 100, trans, useGpu);
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  // Use small batch_size and useWeight=true to test biasGrad
  testLayerGrad(config, "convTrans", 2, trans, useGpu, true, 0.02);
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}

TEST(Layer, convTransLayer) {
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  for (auto useGpu : {false, true}) {
    testConvTransLayer("exconvt", /* trans= */ false, /* useGpu= */ useGpu);
  }
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#ifndef PADDLE_ONLY_CPU
  testConvTransLayer("cudnn_convt", /* trans= */ false, /* useGpu= */ true);
#endif
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}

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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);
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  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));
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  config.layerConfig.set_size(blockExpand->block_x() * blockExpand->block_y() *
                              blockExpand->channels());

  for (auto useGpu : {false, true}) {
    testLayerGrad(config, "blockexpand", 100, false, useGpu);
  }
}

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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();
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  ImageConfig* image = maxout->mutable_image_conf();
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  image->set_img_size(32);
  image->set_img_size_y(32);
  image->set_channels(4);
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  maxout->set_groups(2);

  for (auto useGpu : {false, true}) {
    testLayerGrad(config, "maxout", 10, false, useGpu);
  }
}
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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}) {
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    testLayerGrad(config,
                  "fc",
                  100,
                  /* trans */ false,
                  useGpu,
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                  /* 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();

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  testLayerGrad(config,
                "selective_fc",
                100,
                /* trans= */ false,
                /* useGup= */ false,
                false);
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#ifndef PADDLE_ONLY_CPU
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  testLayerGrad(config,
                "selective_fc",
                100,
                /* trans= */ false,
                /* useGup= */ true,
                false);
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#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
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    testLayerGrad(config,
                  "data_norm",
                  200,
                  /* trans */ false,
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                  /* 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
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  testLayerGrad(config,
                "hsigmoid",
                100,
                /* trans */ false, /* useGpu */
                false);
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}

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}) {
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    testLayerGrad(
        config, "multi-class-cross-entropy", 100, /* trans */ false, useGpu);
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  }
}

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TEST(Layer, multi_binary_label_sparse_mat) {
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  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();

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  for (auto useGpu : {false, true}) {
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    testLayerGrad(config,
                  "multi_binary_label_cross_entropy",
                  100,
                  /* trans */ false,
                  useGpu);
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  }
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}

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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}) {
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    testLayerGrad(config,
                  "multi_binary_label_cross_entropy",
                  100,
                  /* trans */ false,
                  useGpu);
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  }
}

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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
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  testLayerGrad(config,
                "multi_class_cross_entropy_with_selfnorm",
                100,
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                /* 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}) {
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    testLayerGrad(config,
                  "soft_binary_class_cross_entropy",
                  100,
                  /* trans */ false,
                  useGpu);
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  }
}

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
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  testLayerGrad(config,
                "square_error",
                100,
                /* trans */ false,
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                /* 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
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  testLayerGrad(config,
                "square_error",
                100,
                /* trans */ false,
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                /* 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,
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       "layer_0",
       10,
       0});
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  config.inputDefs.push_back(
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      {hasSubseq ? INPUT_HASSUB_SEQUENCE_DATA : INPUT_SEQUENCE_DATA,
       "layer_1",
       10,
       0});
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  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
}

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void testDegradeLayer(bool hasSubseq,
                      string layer_type,
                      string trans_type,
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                      int stride) {
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  TestConfig config;
  config.layerConfig.set_type(layer_type);
  config.layerConfig.set_size(10);
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  config.layerConfig.set_seq_pool_stride(stride);
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  config.biasSize = 0;

  config.inputDefs.push_back(
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      {hasSubseq ? INPUT_HASSUB_SEQUENCE_DATA : INPUT_SEQUENCE_DATA,
       "layer_0",
       10,
       0});
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  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
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                << " average_strategy=" << strategy
                << " seq_pool_stride=" << stride;
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      config.layerConfig.set_average_strategy(strategy);
      testDegradeLayerGrad(config, layer_type);
    }
  } else {
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    LOG(INFO) << " hasSubseq=" << hasSubseq << " trans_type=" << trans_type
              << " seq_pool_stride=" << stride;
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    testDegradeLayerGrad(config, layer_type);
  }
}

TEST(Layer, MaxLayer) {
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  testDegradeLayer(false, "max", "non-seq", -1);  // seq max to non-seq
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  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
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}

TEST(Layer, SequenceLastInstanceLayer) {
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  testDegradeLayer(false,
                   "seqlastins",
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                   "non-seq",
                   -1);  // seq seqlastins to non-seq
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  testDegradeLayer(false,
                   "seqlastins",
                   "non-seq",
                   5);  // seq seqlastins to a shorten seq, stride window = 5
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  testDegradeLayer(true,
                   "seqlastins",
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                   "non-seq",
                   -1);  // hasSubseq seqlastins to non-seq
  testDegradeLayer(
      true, "seqlastins", "seq", -1);  // hasSubseq seqlastins to seq
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}

TEST(Layer, AverageLayer) {
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  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
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}

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");

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  config.inputDefs.push_back({INPUT_DATA, "layer_0", 1568, 0});
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  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);
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  norm->set_img_size_y(7);
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  norm->set_output_x(norm->img_size());
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  norm->set_output_y(norm->img_size_y());
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  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()));
  }

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  config.layerConfig.set_size(norm->output_x() * norm->output_y() *
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                              norm->channels());
  config.biasSize = 0;

  testLayerGrad(config, "norm", 100, trans, useGpu);
}

TEST(Layer, NormLayer) {
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  testNormLayer("cmrnorm-projection",
                /* trans= */ false, /* useGpu= */
                true);
  testNormLayer("cmrnorm-projection",
                /* trans= */ false, /* useGpu= */
                false);
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}

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void setPoolConfig(TestConfig* config,
                   PoolConfig* pool,
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                   const string& poolType) {
  (*config).biasSize = 0;
  (*config).layerConfig.set_type("pool");
  (*config).layerConfig.set_num_filters(16);

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  int kw = 3, kh = 3;
  int pw = 0, ph = 0;
  int sw = 2, sh = 2;
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  pool->set_pool_type(poolType);
  pool->set_channels(16);
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  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);

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  int ow = outputSize(pool->img_size(), kw, pw, sw, /* caffeMode */ false);
  int oh = outputSize(pool->img_size_y(), kh, ph, sh, /* caffeMode */ false);
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  pool->set_output_x(ow);
  pool->set_output_y(oh);
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}

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);
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  pool->set_img_size_y(14);
  setPoolConfig(&config, pool, poolType);
  config.layerConfig.set_size(pool->output_x() * pool->output_y() *
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                              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);
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  setPoolConfig(&config, pool, poolType);
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  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
}

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void testSppLayer(const string& poolType,
                  const int pyramidHeight,
                  bool trans,
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                  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);
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  ImageConfig* imageConfig = sppConfig->mutable_image_conf();
  imageConfig->set_channels(16);
  imageConfig->set_img_size(10);
  imageConfig->set_img_size_y(20);
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  int outputSize = (std::pow(4, sppConfig->pyramid_height()) - 1) / (4 - 1);
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  config.layerConfig.set_size(outputSize * imageConfig->channels());
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  testLayerGrad(config, "spp", 100, trans, useGpu);
}

TEST(Layer, SpatialPyramidPoolLayer) {
  for (auto useGpu : {false, true}) {
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    for (auto pyramidHeight : {1, 2, 3}) {
      testSppLayer("avg-projection", pyramidHeight, false, useGpu);
      testSppLayer("max-projection", pyramidHeight, false, useGpu);
    }
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  }
}

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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);
  }
}

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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);
  }
}

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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);
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  config.layerConfig.set_active_type("tanh");
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  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);
  }
}

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TEST(Layer, RotateLayer) {
  TestConfig config;
  config.biasSize = 0;
  config.layerConfig.set_type("rotate");
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  const int CHANNEL = 2;
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  const int HEIGHT = 8;
  const int WIDTH = 4;
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  const int INPUT_SIZE = HEIGHT * WIDTH * CHANNEL;
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  config.layerConfig.set_size(INPUT_SIZE);
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  config.layerConfig.set_height(HEIGHT);
  config.layerConfig.set_width(WIDTH);
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  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);
  }
}

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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] = {
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          isIdLabel ? INPUT_LABEL : INPUT_SPARSE_NON_VALUE_DATA,
          "label",
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          /* 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
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        testLayerGrad(config,
                      "nce",
                      100,
                      /* trans= */ false,
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                      /* 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;
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  const int IMG_SIZE_Y = 8;
  size_t size = CHANNELS * IMG_SIZE * IMG_SIZE_Y;
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  config.layerConfig.set_type(type);
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  config.layerConfig.set_size(size);
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  config.layerConfig.set_active_type("sigmoid");
  config.biasSize = CHANNELS;
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  config.inputDefs.push_back({INPUT_DATA,
                              "layer_0",
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                              /* dim= */ size,
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                              /* 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);
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  img_conf->set_img_size_y(IMG_SIZE_Y);
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  testLayerGrad(config,
                "batch_norm",
                64,
                /* trans= */ trans,
                useGpu,
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                /* 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
}

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void testConvOperator(bool isDeconv) {
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  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;
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  const int IMAGE_SIZE_Y = 9;
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  OperatorConfig& operatorConf = *config.layerConfig.add_operator_confs();
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  if (isDeconv) {
    operatorConf.set_type("convt");
  } else {
    operatorConf.set_type("conv");
  }
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  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);
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  conv->set_img_size_y(IMAGE_SIZE_Y);
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  conv->set_output_x(outputSize(conv->img_size(),
                                conv->filter_size(),
                                conv->padding(),
                                conv->stride(),
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                                /*  caffeMode */ true));
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  conv->set_output_y(outputSize(conv->img_size_y(),
                                conv->filter_size_y(),
                                conv->padding_y(),
                                conv->stride_y(),
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                                /*  caffeMode */ true));
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  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);
  }

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  config.inputDefs.push_back(
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      {INPUT_DATA,
       "layer_1",
       FILTER_SIZE * FILTER_SIZE_Y * CHANNELS * NUM_FILTERS,
       0});
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  config.layerConfig.add_inputs();
  config.layerConfig.add_inputs();

  testOperatorGrad(config, operatorConf, 100, /*useGpu*/ true, false);
}

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TEST(Operator, conv) {
  testConvOperator(/*isDeconv*/ true);
  testConvOperator(/*isDeconv*/ false);
}

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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);
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  config.inputDefs.push_back({INPUT_SEQUENCE_DATA,
                              "layer_0",
                              /* dim= */ INPUT_SIZE,
                              /* paraSize= */ 0});
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  config.layerConfig.add_inputs();
  for (auto useGpu : {false, true}) {
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    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);
    }
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  }
}

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);
  }
}

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TEST(Layer, PadLayer) {
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  TestConfig config;
  config.biasSize = 0;
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  config.layerConfig.set_type("pad");
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  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});
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  LayerInputConfig* input = config.layerConfig.add_inputs();
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  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);
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  for (auto useGpu : {false, true}) {
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    testLayerGrad(config, "pad", 10, false, useGpu);
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  }
}

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TEST(Layer, CrossChannelNormLayer) {
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  TestConfig config;
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  config.paramInitialMean = 1.;
  config.paramInitialStd = 0.;
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  config.layerConfig.set_type("norm");
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  config.layerConfig.set_size(100);
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  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);
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  config.inputDefs.push_back({INPUT_DATA, "layer_0", 100, 10});

  for (auto useGpu : {false, true}) {
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    testLayerGrad(config, "cross-channel-norm", 10, false, useGpu, false);
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  }
}

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TEST(Layer, smooth_l1) {
  TestConfig config;
  config.layerConfig.set_type("smooth_l1");

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  config.inputDefs.push_back({INPUT_DATA, "layer_0", 200, 0});
  config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 200, 0});
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  config.layerConfig.add_inputs();
  config.layerConfig.add_inputs();

  for (auto useGpu : {false, true}) {
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    testLayerGrad(config, "smooth_l1", 100, false, useGpu, false);
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  }
}

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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);
  }
}

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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);
  }
}

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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);
  }
}

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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);
  }
}

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TEST(Layer, SubNestedSequenceLayer) {
  const int layerSize = 128;

  TestConfig config;
  config.layerConfig.set_type("sub_nested_seq");
  config.layerConfig.set_top_k(2);
  config.layerConfig.set_name("sub_nested_seq_layer");
  config.layerConfig.set_size(layerSize);

  // Generate the first input
  srand((size_t)(time(NULL)));
  const int batchSize = 128;
  const int maxSeqLen = 100;
  const int maxSubSeqNum = 50;
  // sequenceStartPositioins info for the first input.
  vector<int> seqStartPos1(batchSize + 1, 0);
  // subSequenceStartPositioins info for the first input.
  vector<int> subSeqStartPos;
  subSeqStartPos.push_back(0);

  // sequenceStartPositioins info for the second input.
  vector<int> seqStartPos2(batchSize + 1, 0);

  size_t curPos = 0;
  for (int i = 1; i < batchSize + 1; ++i) {
    int seqNum = uniformRandom(maxSubSeqNum);
    seqStartPos2[i] = seqStartPos2[i - 1] + seqNum;
    for (int j = 0; j < seqNum; ++j) {
      int seqLen = uniformRandom(maxSeqLen);
      subSeqStartPos.push_back(curPos + seqLen);
      curPos += seqLen;
    }
    seqStartPos1[i] = curPos;
  }

  MatrixPtr dataInputPtr1 = Matrix::create(curPos, layerSize, false, false);
  dataInputPtr1->randomizeUniform();
  config.inputDefs.push_back({INPUT_SELF_DEFINE_DATA,
                              "layer_0",
                              dataInputPtr1,
                              seqStartPos1,
                              subSeqStartPos});
  config.layerConfig.add_inputs();

  // Generate the second input
  MatrixPtr dataInputPtr2 =
      Matrix::create(seqStartPos2[batchSize], 1, false, false);
  dataInputPtr2->randomizeUniform();
  config.inputDefs.push_back(
      {INPUT_SELF_DEFINE_DATA, "layer_1", dataInputPtr2, seqStartPos2});
  config.layerConfig.add_inputs();

  for (auto useGpu : {false, true}) {
    testLayerGrad(config,
                  "sub_nested_seq",
                  /* batchSize */ 100,
                  /* trans */ false,
                  /* useGpu*/ useGpu,
                  /* useWeight */ false);
  }
}

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int main(int argc, char** argv) {
  testing::InitGoogleTest(&argc, argv);
  initMain(argc, argv);
  FLAGS_thread_local_rand_use_global_seed = true;
  srand(1);
  return RUN_ALL_TESTS();
}