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#include "../src/model/layer/cudnn_dropout.h"
#ifdef USE_CUDNN
// cudnn dropout is added in cudnn 5
#if CUDNN_MAJOR >= 5

#include "gtest/gtest.h"

bool inline GetBitValue(const char* x, int pos) {
  const unsigned char BitMask[] = {1, 2, 4, 8, 16, 32, 64, 128};
  int idx = pos / 8;
  int offset = pos % 8;
  return x[idx] & BitMask[offset];
}

using singa::CudnnDropout;
using singa::Shape;
TEST(CudnnDropout, Setup) {
  CudnnDropout drop;
  // EXPECT_EQ("CudnnDropout", drop.layer_type());

  singa::LayerConf conf;
  singa::DropoutConf* dropconf = conf.mutable_dropout_conf();
  dropconf->set_dropout_ratio(0.8);

  drop.Setup(Shape{1}, conf);
  EXPECT_EQ(0.8f, drop.dropout_ratio());
}

TEST(CudnnDropout, Forward) {
  const float x[] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f};
  size_t n = sizeof(x) / sizeof(float);
  auto cuda = std::make_shared<singa::CudaGPU>();
  singa::Tensor in(singa::Shape{n}, cuda);
  in.CopyDataFromHostPtr(x, n);

  float pdrop = 0.5;
  CudnnDropout drop;
  singa::LayerConf conf;
  singa::DropoutConf* dropconf = conf.mutable_dropout_conf();
  dropconf->set_dropout_ratio(pdrop);
  drop.Setup(Shape{1}, conf);

  singa::Tensor out1 = drop.Forward(singa::kTrain, in);

  singa::Tensor mask(drop.mask().shape(), drop.mask().data_type());
  mask.CopyData(drop.mask());
  const char* mptr = mask.data<char>();
  for (size_t i = 0; i < n; i++)
    EXPECT_FLOAT_EQ(0, GetBitValue(mptr, i) * (GetBitValue(mptr, i) - 1));

  out1.ToHost();
  const float* outptr1 = out1.data<float>();
  EXPECT_EQ(n, out1.Size());
  float scale = 1.0f / (1.0f - pdrop);
  // the output value should be 0 or the same as the input
  EXPECT_EQ(0.f, outptr1[0] * (outptr1[0] - scale * x[0]));
  EXPECT_EQ(0.f, outptr1[1] * (outptr1[1] - scale * x[1]));
  EXPECT_EQ(0.f, outptr1[7] * (outptr1[7] - scale * x[7]));

  singa::Tensor out2 = drop.Forward(singa::kEval, in);
  out2.ToHost();
  EXPECT_EQ(n, out2.Size());
  const float* outptr2 = out2.data<float>();
  // the output value should be the same as the input
  EXPECT_EQ(x[0], outptr2[0]);
  EXPECT_EQ(x[1], outptr2[1]);
  EXPECT_EQ(x[7], outptr2[7]);
}

TEST(CudnnDropout, Backward) {
  const float x[] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f};
  size_t n = sizeof(x) / sizeof(float);
  auto cuda = std::make_shared<singa::CudaGPU>();
  singa::Tensor in(singa::Shape{n}, cuda);
  in.CopyDataFromHostPtr(x, n);

  float pdrop = 0.5;
  float scale = 1.0f / (1.0f - pdrop);

  CudnnDropout drop;
  singa::LayerConf conf;
  singa::DropoutConf* dropconf = conf.mutable_dropout_conf();
  dropconf->set_dropout_ratio(pdrop);
  drop.Setup(Shape{1}, conf);
  singa::Tensor out1 = drop.Forward(singa::kTrain, in);

  const float dy[] = {4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 1.0f, 2.0f, 3.0f};
  singa::Tensor grad(singa::Shape{n}, cuda);
  grad.CopyDataFromHostPtr(dy, n);

  const auto ret = drop.Backward(singa::kTrain, grad);
  singa::Tensor in_grad = ret.first;
  in_grad.ToHost();
  const float* dx = in_grad.data<float>();

  singa::Tensor mask(drop.mask().shape(), drop.mask().data_type());
  mask.CopyData(drop.mask());
  const char* mptr = mask.data<char>();

  EXPECT_FLOAT_EQ(dx[0], dy[0] * GetBitValue(mptr, 0) * scale);
  EXPECT_FLOAT_EQ(dx[1], dy[1] * GetBitValue(mptr, 1) * scale);
  EXPECT_FLOAT_EQ(dx[7], dy[7] * GetBitValue(mptr, 7) * scale);
}
#endif  // CUDNN_MAJOR>=5
#endif  // USE_CUDNN