/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

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

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

#include "paddle/platform/cudnn_helper.h"
#include "glog/logging.h"
#include "gtest/gtest.h"

TEST(CudnnHelper, ScopedTensorDescriptor) {
  using paddle::platform::ScopedTensorDescriptor;
  using paddle::platform::DataLayout;

  ScopedTensorDescriptor tensor_desc;
  std::vector<int> shape = {2, 4, 6, 6};
  auto desc = tensor_desc.descriptor<float>(DataLayout::kNCHW, shape);

  cudnnDataType_t type;
  int nd;
  std::vector<int> dims(4);
  std::vector<int> strides(4);
  paddle::platform::dynload::cudnnGetTensorNdDescriptor(
      desc, 4, &type, &nd, dims.data(), strides.data());

  EXPECT_EQ(nd, 4);
  for (size_t i = 0; i < dims.size(); ++i) {
    EXPECT_EQ(dims[i], shape[i]);
  }
  EXPECT_EQ(strides[3], 1);
  EXPECT_EQ(strides[2], 6);
  EXPECT_EQ(strides[1], 36);
  EXPECT_EQ(strides[0], 144);
}

TEST(CudnnHelper, ScopedFilterDescriptor) {
  using paddle::platform::ScopedFilterDescriptor;
  using paddle::platform::DataLayout;

  ScopedFilterDescriptor filter_desc;
  std::vector<int> shape = {2, 3, 3};
  auto desc = filter_desc.descriptor<float>(DataLayout::kNCHW, shape);

  cudnnDataType_t type;
  int nd;
  cudnnTensorFormat_t format;
  std::vector<int> kernel(3);
  paddle::platform::dynload::cudnnGetFilterNdDescriptor(desc, 3, &type, &format,
                                                        &nd, kernel.data());

  EXPECT_EQ(GetCudnnTensorFormat(DataLayout::kNCHW), format);
  EXPECT_EQ(nd, 3);
  for (size_t i = 0; i < shape.size(); ++i) {
    EXPECT_EQ(kernel[i], shape[i]);
  }
}

TEST(CudnnHelper, ScopedConvolutionDescriptor) {
  using paddle::platform::ScopedConvolutionDescriptor;

  ScopedConvolutionDescriptor conv_desc;
  std::vector<int> src_pads = {2, 2, 2};
  std::vector<int> src_strides = {1, 1, 1};
  std::vector<int> src_dilations = {1, 1, 1};
  auto desc = conv_desc.descriptor<float>(src_pads, src_strides, src_dilations);

  cudnnDataType_t type;
  cudnnConvolutionMode_t mode;
  int nd;
  std::vector<int> pads(3);
  std::vector<int> strides(3);
  std::vector<int> dilations(3);
  paddle::platform::dynload::cudnnGetConvolutionNdDescriptor(
      desc, 3, &nd, pads.data(), strides.data(), dilations.data(), &mode,
      &type);

  EXPECT_EQ(nd, 3);
  for (size_t i = 0; i < src_pads.size(); ++i) {
    EXPECT_EQ(pads[i], src_pads[i]);
    EXPECT_EQ(strides[i], src_strides[i]);
    EXPECT_EQ(dilations[i], src_dilations[i]);
  }
  EXPECT_EQ(mode, CUDNN_CROSS_CORRELATION);
}

TEST(CudnnHelper, ScopedPoolingDescriptor) {
  using paddle::platform::ScopedPoolingDescriptor;
  using paddle::platform::PoolingMode;

  ScopedPoolingDescriptor pool_desc;
  std::vector<int> src_kernel = {2, 2, 5};
  std::vector<int> src_pads = {1, 1, 2};
  std::vector<int> src_strides = {2, 2, 3};
  auto desc = pool_desc.descriptor(PoolingMode::kMaximum, src_kernel, src_pads,
                                   src_strides);

  cudnnPoolingMode_t mode;
  cudnnNanPropagation_t nan_t = CUDNN_PROPAGATE_NAN;
  int nd;
  std::vector<int> kernel(3);
  std::vector<int> pads(3);
  std::vector<int> strides(3);
  paddle::platform::dynload::cudnnGetPoolingNdDescriptor(
      desc, 3, &mode, &nan_t, &nd, kernel.data(), pads.data(), strides.data());

  EXPECT_EQ(nd, 3);
  for (size_t i = 0; i < src_pads.size(); ++i) {
    EXPECT_EQ(kernel[i], src_kernel[i]);
    EXPECT_EQ(pads[i], src_pads[i]);
    EXPECT_EQ(strides[i], src_strides[i]);
  }
  EXPECT_EQ(mode, CUDNN_POOLING_MAX);
}