/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.

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 <memory>

#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/common/place.h"
#include "paddle/phi/kernels/copy_kernel.h"
#include "paddle/phi/kernels/sparse/convolution_grad_kernel.h"
#include "paddle/phi/kernels/sparse/convolution_kernel.h"

#include "paddle/fluid/memory/allocation/allocator_facade.h"
#include "paddle/phi/api/lib/utils/allocator.h"
#include "paddle/phi/core/kernel_registry.h"

namespace phi {
namespace tests {

std::vector<int> flatten(const std::vector<std::vector<int>>& in) {
  std::vector<int> out;
  if (in.size() == 0) return out;
  const int cols = in[0].size();
  out.resize(in.size() * cols);
  for (uint64_t i = 0; i < in.size(); i++) {
    memcpy(&out[i * cols], in[i].data(), cols * sizeof(int));
  }
  return out;
}

template <typename T1, typename T2>
std::vector<T2> cast(const std::vector<T1>& in) {
  std::vector<T2> out(in.size());
  for (uint64_t i = 0; i < in.size(); i++) {
    out[i] = static_cast<T2>(in[i]);
  }
  return out;
}

template <typename T>
void TestConv3dBase(const std::vector<int>& indices,
                    const std::vector<T>& features,
                    const DDim& x_dims,
                    const std::vector<T>& kernel,
                    const DDim& kernel_dims,
                    const std::vector<int>& correct_out_indices,
                    const std::vector<T>& correct_out_features,
                    const DDim& correct_out_dims,
                    const int non_zero_num,
                    const std::vector<int>& paddings,
                    const std::vector<int>& strides,
                    const std::vector<int>& dilations,
                    const float diff = 1e-3,
                    const bool backward = false,
                    const std::vector<T> features_grad = {},
                    const std::vector<T> kernel_grad = {},
                    const bool subm = false) {
  phi::CPUContext dev_ctx_cpu;
  dev_ctx_cpu.SetAllocator(
      paddle::memory::allocation::AllocatorFacade::Instance()
          .GetAllocator(paddle::platform::CPUPlace())
          .get());
  dev_ctx_cpu.Init();

  const int in_channels = kernel_dims[3];
  const int out_channels = kernel_dims[4];

  DenseTensor indices_tensor = phi::Empty(
      dev_ctx_cpu,
      DenseTensorMeta(DataType::INT32, {4, non_zero_num}, DataLayout::NCHW));
  memcpy(
      indices_tensor.data<int>(), indices.data(), indices.size() * sizeof(int));
  DenseTensor features_tensor = phi::Empty(
      dev_ctx_cpu,
      DenseTensorMeta(paddle::experimental::CppTypeToDataType<T>::Type(),
                      {non_zero_num, in_channels},
                      DataLayout::NHWC));
  memcpy(
      features_tensor.data<T>(), features.data(), features.size() * sizeof(T));

  SparseCooTensor x_tensor(indices_tensor, features_tensor, x_dims);

  DenseTensor kernel_tensor = phi::Empty(
      dev_ctx_cpu,
      DenseTensorMeta(paddle::experimental::CppTypeToDataType<T>::Type(),
                      kernel_dims,
                      DataLayout::NHWC));
  memcpy(kernel_tensor.data<T>(), kernel.data(), kernel.size() * sizeof(T));

  auto f_verify = [&](const T* real_data, const std::vector<T>& correct_data) {
    for (uint64_t i = 0; i < correct_data.size(); i++) {
      float tmp = std::fabs(static_cast<float>(correct_data[i] - real_data[i]));
      ASSERT_LT(tmp, diff);
    }
  };

  if (!std::is_same<T, phi::dtype::float16>::value) {
    DenseTensor rulebook = phi::Empty(
        dev_ctx_cpu, DenseTensorMeta(DataType::INT32, {1}, DataLayout::NCHW));
    SparseCooTensor out = sparse::Conv3d<T>(dev_ctx_cpu,
                                            x_tensor,
                                            kernel_tensor,
                                            paddings,
                                            dilations,
                                            strides,
                                            1,
                                            subm,
                                            &rulebook);

    ASSERT_EQ(correct_out_dims.size(), out.dims().size());
    for (int i = 0; i < correct_out_dims.size(); i++) {
      ASSERT_EQ(correct_out_dims[i], out.dims()[i]);
    }
    ASSERT_EQ((int64_t)correct_out_features.size() / out_channels, out.nnz());

    int cmp_indices = memcmp(correct_out_indices.data(),
                             out.non_zero_indices().data<int>(),
                             correct_out_indices.size() * sizeof(int));
    ASSERT_EQ(cmp_indices, 0);

    f_verify(out.non_zero_elements().data<T>(), correct_out_features);

    if (backward) {
      std::vector<DenseTensor> grads = sparse::Conv3dGrad<T>(dev_ctx_cpu,
                                                             x_tensor,
                                                             rulebook,
                                                             kernel_tensor,
                                                             out,
                                                             paddings,
                                                             dilations,
                                                             strides,
                                                             1,
                                                             subm);
      f_verify(grads[0].data<T>(), features_grad);
      f_verify(grads[1].data<T>(), kernel_grad);
    }
  }

// test gpu
#if defined(PADDLE_WITH_CUDA)
  phi::GPUContext dev_ctx_gpu;
  dev_ctx_gpu.PartialInitWithoutAllocator();
  dev_ctx_gpu.SetAllocator(
      paddle::memory::allocation::AllocatorFacade::Instance()
          .GetAllocator(dev_ctx_gpu.GetPlace(), dev_ctx_gpu.stream())
          .get());
  dev_ctx_gpu.SetHostAllocator(
      paddle::memory::allocation::AllocatorFacade::Instance()
          .GetAllocator(phi::CPUPlace())
          .get());
  dev_ctx_gpu.PartialInitWithAllocator();

  DenseTensor d_indices_tensor = phi::Empty(
      dev_ctx_gpu,
      DenseTensorMeta(DataType::INT32, {4, non_zero_num}, DataLayout::NCHW));
  phi::Copy(
      dev_ctx_gpu, indices_tensor, phi::GPUPlace(), true, &d_indices_tensor);

  DenseTensor d_features_tensor = phi::Empty(
      dev_ctx_gpu,
      DenseTensorMeta(paddle::experimental::CppTypeToDataType<T>::Type(),
                      {non_zero_num, in_channels},
                      DataLayout::NHWC));
  phi::Copy(
      dev_ctx_gpu, features_tensor, phi::GPUPlace(), true, &d_features_tensor);

  SparseCooTensor d_x_tensor(d_indices_tensor, d_features_tensor, x_dims);

  DenseTensor d_kernel_tensor = phi::Empty(
      dev_ctx_gpu,
      DenseTensorMeta(paddle::experimental::CppTypeToDataType<T>::Type(),
                      kernel_dims,
                      DataLayout::NHWC));
  phi::Copy(
      dev_ctx_gpu, kernel_tensor, phi::GPUPlace(), true, &d_kernel_tensor);

  DenseTensor d_rulebook = phi::Empty(
      dev_ctx_gpu, DenseTensorMeta(DataType::INT32, {1}, DataLayout::NCHW));
  SparseCooTensor d_out = sparse::Conv3d<T>(dev_ctx_gpu,
                                            d_x_tensor,
                                            d_kernel_tensor,
                                            paddings,
                                            dilations,
                                            strides,
                                            1,
                                            subm,
                                            &d_rulebook);

  ASSERT_EQ(correct_out_dims.size(), d_out.dims().size());
  ASSERT_EQ((int64_t)correct_out_features.size() / out_channels, d_out.nnz());
  for (int i = 0; i < correct_out_dims.size(); i++) {
    ASSERT_EQ(correct_out_dims[i], d_out.dims()[i]);
  }

  DenseTensor h_indices_tensor = phi::Empty(
      dev_ctx_cpu,
      DenseTensorMeta(DataType::INT32, {4, d_out.nnz()}, DataLayout::NCHW));
  phi::Copy(dev_ctx_gpu,
            d_out.non_zero_indices(),
            phi::CPUPlace(),
            true,
            &h_indices_tensor);

  int cmp_indices2 = memcmp(correct_out_indices.data(),
                            h_indices_tensor.data<int>(),
                            correct_out_indices.size() * sizeof(int));
  ASSERT_EQ(cmp_indices2, 0);

  DenseTensor h_features_tensor = phi::Empty(
      dev_ctx_cpu,
      DenseTensorMeta(paddle::experimental::CppTypeToDataType<T>::Type(),
                      {d_out.nnz()},
                      d_out.layout()));

  phi::Copy(dev_ctx_gpu,
            d_out.non_zero_elements(),
            phi::CPUPlace(),
            true,
            &h_features_tensor);
  f_verify(h_features_tensor.data<T>(), correct_out_features);

  if (backward) {
    std::vector<DenseTensor> grads = sparse::Conv3dGrad<T>(dev_ctx_gpu,
                                                           d_x_tensor,
                                                           d_rulebook,
                                                           d_kernel_tensor,
                                                           d_out,
                                                           paddings,
                                                           dilations,
                                                           strides,
                                                           1,
                                                           subm);
    DenseTensor h_features_grad = phi::Empty(
        dev_ctx_cpu,
        DenseTensorMeta(grads[0].dtype(), grads[0].dims(), grads[0].layout()));
    phi::Copy(dev_ctx_gpu, grads[0], phi::CPUPlace(), true, &h_features_grad);
    f_verify(h_features_grad.data<T>(), features_grad);

    DenseTensor h_kernel_grad = phi::Empty(
        dev_ctx_cpu,
        DenseTensorMeta(grads[1].dtype(), grads[1].dims(), grads[1].layout()));
    phi::Copy(dev_ctx_gpu, grads[1], phi::CPUPlace(), true, &h_kernel_grad);
    f_verify(h_kernel_grad.data<T>(), kernel_grad);
  }
#endif
}

void TestConv3d(const std::vector<int>& indices,
                const std::vector<float>& features,
                const DDim& x_dims,
                const std::vector<float>& kernel,
                const DDim& kernel_dims,
                const std::vector<int>& correct_out_indices,
                const std::vector<float>& correct_out_features,
                const DDim& correct_out_dims,
                const int non_zero_num,
                const std::vector<int>& paddings,
                const std::vector<int>& strides,
                const std::vector<int>& dilations,
                const float diff = 1e-3,
                const bool backward = false,
                const std::vector<float> features_grad = {},
                const std::vector<float> kernel_grad = {},
                const bool subm = false) {
  // test float
  TestConv3dBase<float>(indices,
                        features,
                        x_dims,
                        kernel,
                        kernel_dims,
                        correct_out_indices,
                        correct_out_features,
                        correct_out_dims,
                        non_zero_num,
                        paddings,
                        strides,
                        dilations,
                        diff,
                        backward,
                        features_grad,
                        kernel_grad,
                        subm);
  // test double
  TestConv3dBase<double>(indices,
                         cast<float, double>(features),
                         x_dims,
                         cast<float, double>(kernel),
                         kernel_dims,
                         correct_out_indices,
                         cast<float, double>(correct_out_features),
                         correct_out_dims,
                         non_zero_num,
                         paddings,
                         strides,
                         dilations,
                         diff,
                         backward,
                         cast<float, double>(features_grad),
                         cast<float, double>(kernel_grad),
                         subm);
}

TEST(DEV_API, sparse_conv3d) {
  const int in_channels = 1;
  const int out_channels = 1;
  DDim x_dims = {1, 4, 4, 4, in_channels};
  DDim kernel_dims = {3, 3, 3, in_channels, out_channels};
  DDim out_dims = {1, 2, 2, 2, out_channels};
  std::vector<int> paddings = {0, 0, 0};
  std::vector<int> strides = {1, 1, 1};
  std::vector<int> dilations = {1, 1, 1};

  const int non_zero_num = 4;
  std::vector<std::vector<int>> indices = {
      {0, 0, 0, 0}, {0, 2, 0, 2}, {3, 2, 2, 3}, {3, 2, 3, 2}};
  std::vector<int> indices_flatten = flatten(indices);

  std::vector<float> features = {-0.2883, 0.0287, 0.2864, -0.0992};
  // 3*3*3=27
  std::vector<float> kernel = {
      0.4721, 0.2292, 0.9751, 0.8616, 0.5784, 0.9178, 0.8727, 0.1659, 0.4455,

      0.0189, 0.4646, 0.4472, 0.1991, 0.8968, 0.3717, 0.0051, 0.6963, 0.2690,

      0.7473, 0.5403, 0.5391, 0.0796, 0.4734, 0.9097, 0.1712, 0.6237, 0.8837};

  std::vector<std::vector<int>> out_indices = {{0, 0, 0, 0, 0, 0, 0, 0},
                                               {0, 0, 0, 0, 1, 1, 1, 1},
                                               {0, 0, 1, 1, 0, 0, 1, 1},
                                               {0, 1, 0, 1, 0, 1, 0, 1}};
  std::vector<int> out_indices_flatten = flatten(out_indices);

  std::vector<float> out_features = {
      0.0254, 0.1455, -0.0615, 0.0862, 0.0077, 0.0200, -0.0160, -0.0433};

  TestConv3d(indices_flatten,
             features,
             x_dims,
             kernel,
             kernel_dims,
             out_indices_flatten,
             out_features,
             out_dims,
             non_zero_num,
             paddings,
             strides,
             dilations);
}

TEST(DEV_API, sparse_conv3d_batch) {
  const int in_channels = 1;
  const int out_channels = 1;
  DDim x_dims = {2, 4, 4, 4, in_channels};
  DDim kernel_dims = {3, 3, 3, in_channels, out_channels};
  DDim out_dims = {2, 2, 2, 2, out_channels};
  std::vector<int> paddings = {0, 0, 0};
  std::vector<int> strides = {1, 1, 1};
  std::vector<int> dilations = {1, 1, 1};

  const int non_zero_num = 8;
  std::vector<std::vector<int>> indices = {{0, 0, 0, 0, 1, 1, 1, 1},
                                           {0, 2, 0, 2, 0, 2, 0, 2},
                                           {3, 2, 2, 3, 3, 2, 2, 3},
                                           {3, 2, 3, 2, 3, 2, 3, 2}};
  std::vector<int> indices_flatten = flatten(indices);

  std::vector<float> features = {
      -0.2883, 0.0287, 0.2864, -0.0992, -0.2883, 0.0287, 0.2864, -0.0992};
  // 3*3*3=27
  std::vector<float> kernel = {
      0.4721, 0.2292, 0.9751, 0.8616, 0.5784, 0.9178, 0.8727, 0.1659, 0.4455,

      0.0189, 0.4646, 0.4472, 0.1991, 0.8968, 0.3717, 0.0051, 0.6963, 0.2690,

      0.7473, 0.5403, 0.5391, 0.0796, 0.4734, 0.9097, 0.1712, 0.6237, 0.8837};

  std::vector<std::vector<int>> out_indices = {
      {0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1},
      {0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1},
      {0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1},
      {0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1}};
  std::vector<int> out_indices_flatten = flatten(out_indices);

  std::vector<float> out_features = {0.0254,
                                     0.1455,
                                     -0.0615,
                                     0.0862,
                                     0.0077,
                                     0.0200,
                                     -0.0160,
                                     -0.0433,
                                     0.0254,
                                     0.1455,
                                     -0.0615,
                                     0.0862,
                                     0.0077,
                                     0.0200,
                                     -0.0160,
                                     -0.0433};

  TestConv3d(indices_flatten,
             features,
             x_dims,
             kernel,
             kernel_dims,
             out_indices_flatten,
             out_features,
             out_dims,
             non_zero_num,
             paddings,
             strides,
             dilations);
}

TEST(DEV_API, sparse_conv3d_stride) {
  const int in_channels = 1;
  const int out_channels = 1;
  DDim x_dims = {1, 4, 4, 4, in_channels};
  DDim kernel_dims = {3, 3, 3, in_channels, out_channels};
  DDim out_dims = {1, 1, 1, 1, out_channels};
  std::vector<int> paddings = {0, 0, 0};
  std::vector<int> strides = {2, 2, 2};
  std::vector<int> dilations = {1, 1, 1};

  const int non_zero_num = 3;
  std::vector<std::vector<int>> indices = {
      {0, 0, 0}, {0, 2, 0}, {3, 2, 2}, {3, 2, 3}};
  std::vector<int> indices_flatten = flatten(indices);

  std::vector<float> features = {-0.28833008, 0.02873230, 0.28637695};
  // 3*3*3=27
  std::vector<float> kernel = {
      0.45043945, 0.47216797, 0.22924805, 0.97509766, 0.86181641, 0.57861328,
      0.91796875, 0.87255859, 0.16589355, 0.44555664, 0.01889038, 0.46459961,
      0.44726562, 0.19909668, 0.89697266, 0.37158203, 0.00513077, 0.69628906,
      0.26904297, 0.74707031, 0.54003906, 0.5390625,  0.07958984, 0.47338867,
      0.90966797, 0.17126465, 0.62353516};

  std::vector<std::vector<int>> out_indices = {{0, 0, 0, 0}};
  std::vector<int> out_indices_flatten = flatten(out_indices);

  std::vector<float> out_features = {0.01791};

  TestConv3d(indices_flatten,
             features,
             x_dims,
             kernel,
             kernel_dims,
             out_indices_flatten,
             out_features,
             out_dims,
             non_zero_num,
             paddings,
             strides,
             dilations);
}

TEST(DEV_API, sparse_conv3d_dilation) {
  const int in_channels = 1;
  const int out_channels = 1;
  DDim x_dims = {1, 6, 6, 6, in_channels};
  DDim kernel_dims = {3, 3, 3, in_channels, out_channels};
  DDim out_dims = {1, 2, 2, 2, out_channels};
  std::vector<int> paddings = {0, 0, 0};
  std::vector<int> strides = {1, 1, 1};
  std::vector<int> dilations = {2, 2, 2};

  const int non_zero_num = 3;
  std::vector<std::vector<int>> indices = {
      {0, 0, 0}, {2, 3, 3}, {2, 3, 3}, {5, 2, 0}};
  std::vector<int> indices_flatten = flatten(indices);

  std::vector<float> features = {-0.78710938, -0.64746094, 0.98828125};
  // 3*3*3=27
  std::vector<float> kernel = {
      0.20617676, 0.99365234, 0.16760254, 0.30639648, 0.41479492, 0.75732422,
      0.65625,    0.48535156, 0.72167969, 0.56005859, 0.5,        0.3581543,
      0.20324707, 0.88769531, 0.81298828, 0.58398438, 0.30810547, 0.12634277,
      0.70507812, 0.38720703, 0.34814453, 0.02690125, 0.80273438, 0.90625,
      0.2277832,  0.4362793,  0.44482422};

  std::vector<std::vector<int>> out_indices = {{0, 0, 0, 1, 0, 1, 1, 0}};
  std::vector<int> out_indices_flatten = flatten(out_indices);

  std::vector<float> out_features = {-0.64014, -0.37402};

  TestConv3d(indices_flatten,
             features,
             x_dims,
             kernel,
             kernel_dims,
             out_indices_flatten,
             out_features,
             out_dims,
             non_zero_num,
             paddings,
             strides,
             dilations);
}

TEST(DEV_API, sparse_conv3d_padding) {
  const int in_channels = 1;
  const int out_channels = 1;
  DDim x_dims = {1, 3, 3, 3, in_channels};
  DDim kernel_dims = {3, 3, 3, in_channels, out_channels};
  DDim out_dims = {1, 3, 3, 3, out_channels};
  std::vector<int> paddings = {1, 1, 1};
  std::vector<int> strides = {1, 1, 1};
  std::vector<int> dilations = {1, 1, 1};

  const int non_zero_num = 1;
  std::vector<std::vector<int>> indices = {{0, 1, 0, 0}};
  std::vector<int> indices_flatten = flatten(indices);

  std::vector<float> features = {-0.79394531};
  // 3*3*3=27
  std::vector<float> kernel = {
      0.34375,    0.22485352, 0.65820312, 0.75048828, 0.21411133, 0.17370605,
      0.85546875, 0.53076172, 0.28833008, 0.71044922, 0.00659943, 0.45922852,
      0.19372559, 0.64599609, 0.78808594, 0.49316406, 0.62646484, 0.40649414,
      0.62744141, 0.5703125,  0.23144531, 0.50048828, 0.31835938, 0.90869141,
      0.38208008, 0.60449219, 0.09075928};

  std::vector<int> out_indices_flatten = {
      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2,
      0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1};

  std::vector<float> out_features = {-0.25269,
                                     -0.39746,
                                     -0.45288,
                                     -0.49805,
                                     -0.5127,
                                     -0.15381,
                                     -0.00524,
                                     -0.56396,
                                     -0.17004,
                                     -0.5957,
                                     -0.17847,
                                     -0.27295};

  TestConv3d(indices_flatten,
             features,
             x_dims,
             kernel,
             kernel_dims,
             out_indices_flatten,
             out_features,
             out_dims,
             non_zero_num,
             paddings,
             strides,
             dilations);
}

TEST(DEV_API, sparse_conv2d) {
  const int in_channels = 1;
  const int out_channels = 1;
  DDim x_dims = {1, 1, 5, 5, in_channels};
  DDim kernel_dims = {1, 3, 3, in_channels, out_channels};
  DDim out_dims = {1, 1, 3, 3, out_channels};
  std::vector<int> paddings = {0, 0, 0};
  std::vector<int> strides = {1, 1, 1};
  std::vector<int> dilations = {1, 1, 1};

  const int non_zero_num = 3;
  std::vector<int> indices_flatten = {0, 0, 0, 0, 0, 0, 0, 4, 0, 3, 2, 4};

  std::vector<float> features = {-0.79394531, -0.3125, -0.55029297};
  // 3*3*3=27
  std::vector<float> kernel = {0.65820312,
                               0.75048828,
                               0.21411133,
                               0.17370605,
                               0.85546875,
                               0.53076172,
                               0.28833008,
                               0.71044922,
                               0.00659943};

  std::vector<int> out_indices_flatten = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                                          0, 0, 2, 2, 2, 1, 2, 0, 1, 2};

  std::vector<float> out_features = {
      -0.17004, -0.71338, -0.00206, -0.22205, -0.09009};

  TestConv3d(indices_flatten,
             features,
             x_dims,
             kernel,
             kernel_dims,
             out_indices_flatten,
             out_features,
             out_dims,
             non_zero_num,
             paddings,
             strides,
             dilations);
}

TEST(DEV_API, sparse_conv3d_backward) {
  const int in_channels = 1;
  const int out_channels = 1;
  DDim x_dims = {1, 4, 4, 4, in_channels};
  DDim kernel_dims = {3, 3, 3, in_channels, out_channels};
  DDim out_dims = {1, 2, 2, 2, out_channels};
  std::vector<int> paddings = {0, 0, 0};
  std::vector<int> strides = {1, 1, 1};
  std::vector<int> dilations = {1, 1, 1};

  const int non_zero_num = 2;
  std::vector<int> indices_flatten = {0, 0, 0, 2, 3, 2, 3, 2};

  std::vector<float> features = {-0.28833008, 0.0287323};
  // 3*3*3=27
  std::vector<float> kernel = {
      0.64306641, 0.45043945, 0.47216797, 0.22924805, 0.97509766, 0.86181641,
      0.57861328, 0.91796875, 0.87255859, 0.16589355, 0.44555664, 0.01889038,
      0.46459961, 0.44726562, 0.19909668, 0.89697266, 0.37158203, 0.00513077,
      0.69628906, 0.26904297, 0.74707031, 0.54003906, 0.5390625,  0.07958984,
      0.47338867, 0.90966797, 0.17126465};

  std::vector<int> out_indices_flatten = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                                          0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0,
                                          1, 1, 0, 1, 0, 1, 0, 1, 0, 1};

  std::vector<float> out_features = {4.9200e-03,
                                     2.6140e-02,
                                     2.2900e-03,
                                     -2.3596e-01,
                                     1.5000e-04,
                                     1.0670e-02,
                                     5.7200e-03,
                                     1.2850e-02};

  std::vector<float> features_grad = {-0.20593, -0.09149};
  std::vector<float> kernel_grad = {
      0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,  0.000e+00,
      0.000e+00, 0.000e+00, 6.805e-02, 0.000e+00, 0.000e+00,  0.000e+00,
      0.000e+00, 3.700e-04, 1.600e-04, 0.000e+00, 3.100e-04,  0.000e+00,
      0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, -6.780e-03, 7.000e-05,
      0.000e+00, 7.500e-04, 1.400e-04};

  TestConv3d(indices_flatten,
             features,
             x_dims,
             kernel,
             kernel_dims,
             out_indices_flatten,
             out_features,
             out_dims,
             non_zero_num,
             paddings,
             strides,
             dilations,
             1e-3,
             true,
             features_grad,
             kernel_grad);
}

TEST(DEV_API, sparse_conv2d_subm) {
  const int in_channels = 1;
  const int out_channels = 1;
  DDim x_dims = {1, 1, 4, 5, in_channels};
  DDim kernel_dims = {1, 3, 3, in_channels, out_channels};
  DDim out_dims = {1, 1, 4, 5, out_channels};
  std::vector<int> paddings = {0, 1, 1};
  std::vector<int> strides = {1, 1, 1};
  std::vector<int> dilations = {1, 1, 1};

  const int non_zero_num = 4;
  std::vector<int> indices_flatten = {
      0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 3, 3, 2, 2, 3};

  std::vector<float> features = {0.8854, 0.6505, -0.1999, 0.3583};
  // 3*3*3=27
  std::vector<float> kernel = {
      0.9364, 0.9460, 0.6564, 0.7999, 0.2013, 0.3812, 0.5474, 0.1016, 0.3368};

  std::vector<int> out_indices_flatten = {
      0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 3, 3, 2, 2, 3};

  std::vector<float> out_features = {0.1782, 0.2313, 0.7117, 0.5214};

  std::vector<float> features_grad = {0.0359, 1.2080, 0.5838, 0.4541};
  std::vector<float> kernel_grad = {
      0.3391, 0.4630, 0.0000, -0.1042, 0.3528, 0.2550, 0.0000, -0.0462, 0.0829};

  TestConv3d(indices_flatten,
             features,
             x_dims,
             kernel,
             kernel_dims,
             out_indices_flatten,
             out_features,
             out_dims,
             non_zero_num,
             paddings,
             strides,
             dilations,
             1e-3,
             true,
             features_grad,
             kernel_grad,
             true);
}

TEST(DEV_API, sparse_conv3d_subm) {
  const int in_channels = 1;
  const int out_channels = 1;
  DDim x_dims = {1, 4, 4, 5, in_channels};
  DDim kernel_dims = {3, 3, 3, in_channels, out_channels};
  DDim out_dims = {1, 4, 4, 5, out_channels};
  std::vector<int> paddings = {1, 1, 1};
  std::vector<int> strides = {1, 1, 1};
  std::vector<int> dilations = {1, 1, 1};

  const int non_zero_num = 3;
  std::vector<int> indices_flatten = {0, 0, 0, 1, 3, 3, 2, 0, 2, 0, 3, 1};

  std::vector<float> features = {-0.9578, 0.1572, 0.1036};
  // 3*3*3=27
  std::vector<float> kernel = {
      0.1367, 0.4534, 0.2138, 0.8264, 0.7534, 0.3270, 0.2880, 0.1562, 0.7770,
      0.6902, 0.1981, 0.1369, 0.6582, 0.7582, 0.5640, 0.8894, 0.7350, 0.1845,
      0.6892, 0.3654, 0.6076, 0.0326, 0.8412, 0.5289, 0.9824, 0.8235, 0.9802};

  std::vector<int> out_indices_flatten = {0, 0, 0, 1, 3, 3, 2, 0, 2, 0, 3, 1};

  std::vector<float> out_features = {-0.7262, 0.1192, 0.0785};

  std::vector<float> features_grad = {-0.5506, 0.0904, 0.0595};
  std::vector<float> kernel_grad = {
      0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
      0.0000, 0.0000, 0.0000, 0.0000, 0.7224, 0.0000, 0.0000, 0.0000, 0.0000,
      0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000};

  TestConv3d(indices_flatten,
             features,
             x_dims,
             kernel,
             kernel_dims,
             out_indices_flatten,
             out_features,
             out_dims,
             non_zero_num,
             paddings,
             strides,
             dilations,
             1e-3,
             true,
             features_grad,
             kernel_grad,
             true);
}

}  // namespace tests
}  // namespace phi