// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
// SPDX-License-Identifier: MPL-2.0

#define EIGEN_TEST_NO_LONGDOUBLE
#define EIGEN_TEST_NO_COMPLEX

#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
#define EIGEN_USE_GPU

#include "main.h"
#include <contrib/Eigen/Tensor>

using Eigen::Tensor;

void test_gpu_conversion() {
  Eigen::GpuStreamDevice stream;
  Eigen::GpuDevice gpu_device(&stream);
  int num_elem = 101;

  Tensor<float, 1> floats(num_elem);
  floats.setRandom();

  float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float));
  Eigen::half* d_half = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half));
  float* d_conv = (float*)gpu_device.allocate(num_elem * sizeof(float));

  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float(d_float, num_elem);
  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_half(d_half, num_elem);
  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_conv(d_conv, num_elem);

  gpu_device.memcpyHostToDevice(d_float, floats.data(), num_elem * sizeof(float));

  gpu_half.device(gpu_device) = gpu_float.cast<Eigen::half>();
  gpu_conv.device(gpu_device) = gpu_half.cast<float>();

  Tensor<float, 1> initial(num_elem);
  Tensor<float, 1> final(num_elem);
  gpu_device.memcpyDeviceToHost(initial.data(), d_float, num_elem * sizeof(float));
  gpu_device.memcpyDeviceToHost(final.data(), d_conv, num_elem * sizeof(float));
  gpu_device.synchronize();

  for (int i = 0; i < num_elem; ++i) {
    VERIFY_IS_APPROX(initial(i), final(i));
  }

  gpu_device.deallocate(d_float);
  gpu_device.deallocate(d_half);
  gpu_device.deallocate(d_conv);
}

void test_fallback_conversion() {
  int num_elem = 101;
  Tensor<float, 1> floats(num_elem);
  floats.setRandom();

  Eigen::Tensor<Eigen::half, 1> halfs = floats.cast<Eigen::half>();
  Eigen::Tensor<float, 1> conv = halfs.cast<float>();

  for (int i = 0; i < num_elem; ++i) {
    VERIFY_IS_APPROX(floats(i), conv(i));
  }
}

EIGEN_DECLARE_TEST(tensor_cast_float16_gpu) {
  CALL_SUBTEST(test_gpu_conversion());
  CALL_SUBTEST(test_fallback_conversion());
}