// 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;

template <typename>
void test_gpu_numext() {
  Eigen::GpuStreamDevice stream;
  Eigen::GpuDevice gpu_device(&stream);
  int num_elem = 101;

  float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float));
  bool* d_res_half = (bool*)gpu_device.allocate(num_elem * sizeof(bool));
  bool* d_res_float = (bool*)gpu_device.allocate(num_elem * sizeof(bool));

  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float(d_float, num_elem);
  Eigen::TensorMap<Eigen::Tensor<bool, 1>, Eigen::Aligned> gpu_res_half(d_res_half, num_elem);
  Eigen::TensorMap<Eigen::Tensor<bool, 1>, Eigen::Aligned> gpu_res_float(d_res_float, num_elem);

  gpu_float.device(gpu_device) = gpu_float.random() - gpu_float.constant(0.5f);
  gpu_res_float.device(gpu_device) = gpu_float.unaryExpr(Eigen::internal::scalar_isnan_op<float>());
  gpu_res_half.device(gpu_device) =
      gpu_float.template cast<Eigen::half>().unaryExpr(Eigen::internal::scalar_isnan_op<Eigen::half>());

  Tensor<bool, 1> half_prec(num_elem);
  Tensor<bool, 1> full_prec(num_elem);
  gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, num_elem * sizeof(bool));
  gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem * sizeof(bool));
  gpu_device.synchronize();

  for (int i = 0; i < num_elem; ++i) {
    std::cout << "Checking numext " << i << std::endl;
    VERIFY_IS_EQUAL(full_prec(i), half_prec(i));
  }

  gpu_device.deallocate(d_float);
  gpu_device.deallocate(d_res_half);
  gpu_device.deallocate(d_res_float);
}

template <typename>
void test_gpu_unary() {
  Eigen::GpuStreamDevice stream;
  Eigen::GpuDevice gpu_device(&stream);
  int num_elem = 101;

  float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float));
  float* d_res_half = (float*)gpu_device.allocate(num_elem * sizeof(float));
  float* d_res_float = (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<float, 1>, Eigen::Aligned> gpu_res_half(d_res_half, num_elem);
  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_float(d_res_float, num_elem);

  gpu_float.device(gpu_device) = gpu_float.random() - gpu_float.constant(0.5f);
  gpu_res_float.device(gpu_device) = gpu_float.abs();
  gpu_res_half.device(gpu_device) = gpu_float.template cast<Eigen::half>().abs().template cast<float>();

  Tensor<float, 1> half_prec(num_elem);
  Tensor<float, 1> full_prec(num_elem);
  gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, num_elem * sizeof(float));
  gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem * sizeof(float));
  gpu_device.synchronize();

  for (int i = 0; i < num_elem; ++i) {
    std::cout << "Checking unary " << i << std::endl;
    VERIFY_IS_APPROX(full_prec(i), half_prec(i));
  }

  gpu_device.deallocate(d_float);
  gpu_device.deallocate(d_res_half);
  gpu_device.deallocate(d_res_float);
}

template <typename>
void test_gpu_elementwise() {
  Eigen::GpuStreamDevice stream;
  Eigen::GpuDevice gpu_device(&stream);
  int num_elem = 101;

  float* d_float1 = (float*)gpu_device.allocate(num_elem * sizeof(float));
  float* d_float2 = (float*)gpu_device.allocate(num_elem * sizeof(float));
  float* d_res_half = (float*)gpu_device.allocate(num_elem * sizeof(float));
  float* d_res_float = (float*)gpu_device.allocate(num_elem * sizeof(float));

  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float1(d_float1, num_elem);
  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float2(d_float2, num_elem);
  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_half(d_res_half, num_elem);
  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_float(d_res_float, num_elem);

  gpu_float1.device(gpu_device) = gpu_float1.random();
  gpu_float2.device(gpu_device) = gpu_float2.random();
  gpu_res_float.device(gpu_device) = (gpu_float1 + gpu_float2) * gpu_float1;
  gpu_res_half.device(gpu_device) =
      ((gpu_float1.template cast<Eigen::half>() + gpu_float2.template cast<Eigen::half>()) *
       gpu_float1.template cast<Eigen::half>())
          .template cast<float>();

  Tensor<float, 1> half_prec(num_elem);
  Tensor<float, 1> full_prec(num_elem);
  gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, num_elem * sizeof(float));
  gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem * sizeof(float));
  gpu_device.synchronize();

  for (int i = 0; i < num_elem; ++i) {
    std::cout << "Checking elemwise " << i << ": full prec = " << full_prec(i) << " vs half prec = " << half_prec(i)
              << std::endl;
    VERIFY_IS_APPROX(static_cast<Eigen::half>(full_prec(i)), static_cast<Eigen::half>(half_prec(i)));
  }

  gpu_device.deallocate(d_float1);
  gpu_device.deallocate(d_float2);
  gpu_device.deallocate(d_res_half);
  gpu_device.deallocate(d_res_float);
}

template <typename>
void test_gpu_trancendental() {
  Eigen::GpuStreamDevice stream;
  Eigen::GpuDevice gpu_device(&stream);
  int num_elem = 101;

  float* d_float1 = (float*)gpu_device.allocate(num_elem * sizeof(float));
  float* d_float2 = (float*)gpu_device.allocate(num_elem * sizeof(float));
  float* d_float3 = (float*)gpu_device.allocate(num_elem * sizeof(float));
  Eigen::half* d_res1_half = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half));
  Eigen::half* d_res1_float = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half));
  Eigen::half* d_res2_half = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half));
  Eigen::half* d_res2_float = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half));
  Eigen::half* d_res3_half = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half));
  Eigen::half* d_res3_float = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half));

  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float1(d_float1, num_elem);
  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float2(d_float2, num_elem);
  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float3(d_float3, num_elem);
  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res1_half(d_res1_half, num_elem);
  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res1_float(d_res1_float, num_elem);
  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res2_half(d_res2_half, num_elem);
  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res2_float(d_res2_float, num_elem);
  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res3_half(d_res3_half, num_elem);
  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res3_float(d_res3_float, num_elem);
  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res4_half(d_res3_half, num_elem);
  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res4_float(d_res3_float, num_elem);

  gpu_float1.device(gpu_device) = gpu_float1.random() - gpu_float1.constant(0.5f);
  gpu_float2.device(gpu_device) = gpu_float2.random() + gpu_float1.constant(0.5f);
  gpu_float3.device(gpu_device) = gpu_float3.random();
  gpu_res1_float.device(gpu_device) = gpu_float1.exp().template cast<Eigen::half>();
  gpu_res2_float.device(gpu_device) = gpu_float2.log().template cast<Eigen::half>();
  gpu_res3_float.device(gpu_device) = gpu_float3.log1p().template cast<Eigen::half>();
  gpu_res4_float.device(gpu_device) = gpu_float3.expm1().template cast<Eigen::half>();

  gpu_res1_half.device(gpu_device) = gpu_float1.template cast<Eigen::half>();
  gpu_res1_half.device(gpu_device) = gpu_res1_half.exp();

  gpu_res2_half.device(gpu_device) = gpu_float2.template cast<Eigen::half>();
  gpu_res2_half.device(gpu_device) = gpu_res2_half.log();

  gpu_res3_half.device(gpu_device) = gpu_float3.template cast<Eigen::half>();
  gpu_res3_half.device(gpu_device) = gpu_res3_half.log1p();

  gpu_res3_half.device(gpu_device) = gpu_float3.template cast<Eigen::half>();
  gpu_res3_half.device(gpu_device) = gpu_res3_half.expm1();

  Tensor<float, 1> input1(num_elem);
  Tensor<Eigen::half, 1> half_prec1(num_elem);
  Tensor<Eigen::half, 1> full_prec1(num_elem);
  Tensor<float, 1> input2(num_elem);
  Tensor<Eigen::half, 1> half_prec2(num_elem);
  Tensor<Eigen::half, 1> full_prec2(num_elem);
  Tensor<float, 1> input3(num_elem);
  Tensor<Eigen::half, 1> half_prec3(num_elem);
  Tensor<Eigen::half, 1> full_prec3(num_elem);
  gpu_device.memcpyDeviceToHost(input1.data(), d_float1, num_elem * sizeof(float));
  gpu_device.memcpyDeviceToHost(input2.data(), d_float2, num_elem * sizeof(float));
  gpu_device.memcpyDeviceToHost(input3.data(), d_float3, num_elem * sizeof(float));
  gpu_device.memcpyDeviceToHost(half_prec1.data(), d_res1_half, num_elem * sizeof(Eigen::half));
  gpu_device.memcpyDeviceToHost(full_prec1.data(), d_res1_float, num_elem * sizeof(Eigen::half));
  gpu_device.memcpyDeviceToHost(half_prec2.data(), d_res2_half, num_elem * sizeof(Eigen::half));
  gpu_device.memcpyDeviceToHost(full_prec2.data(), d_res2_float, num_elem * sizeof(Eigen::half));
  gpu_device.memcpyDeviceToHost(half_prec3.data(), d_res3_half, num_elem * sizeof(Eigen::half));
  gpu_device.memcpyDeviceToHost(full_prec3.data(), d_res3_float, num_elem * sizeof(Eigen::half));
  gpu_device.synchronize();

  for (int i = 0; i < num_elem; ++i) {
    std::cout << "Checking elemwise exp " << i << " input = " << input1(i) << " full = " << full_prec1(i)
              << " half = " << half_prec1(i) << std::endl;
    VERIFY_IS_APPROX(full_prec1(i), half_prec1(i));
  }
  for (int i = 0; i < num_elem; ++i) {
    std::cout << "Checking elemwise log " << i << " input = " << input2(i) << " full = " << full_prec2(i)
              << " half = " << half_prec2(i) << std::endl;
    if (std::abs(input2(i) - 1.f) < 0.05f)  // log lacks accuracy nearby 1
      VERIFY_IS_APPROX(full_prec2(i) + Eigen::half(0.1f), half_prec2(i) + Eigen::half(0.1f));
    else
      VERIFY_IS_APPROX(full_prec2(i), half_prec2(i));
  }
  for (int i = 0; i < num_elem; ++i) {
    std::cout << "Checking elemwise plog1 " << i << " input = " << input3(i) << " full = " << full_prec3(i)
              << " half = " << half_prec3(i) << std::endl;
    VERIFY_IS_APPROX(full_prec3(i), half_prec3(i));
  }
  gpu_device.deallocate(d_float1);
  gpu_device.deallocate(d_float2);
  gpu_device.deallocate(d_float3);
  gpu_device.deallocate(d_res1_half);
  gpu_device.deallocate(d_res1_float);
  gpu_device.deallocate(d_res2_half);
  gpu_device.deallocate(d_res2_float);
  gpu_device.deallocate(d_res3_float);
  gpu_device.deallocate(d_res3_half);
}

template <typename>
void test_gpu_contractions() {
  Eigen::GpuStreamDevice stream;
  Eigen::GpuDevice gpu_device(&stream);
  int rows = 23;
  int cols = 23;
  int num_elem = rows * cols;

  float* d_float1 = (float*)gpu_device.allocate(num_elem * sizeof(float));
  float* d_float2 = (float*)gpu_device.allocate(num_elem * sizeof(float));
  Eigen::half* d_res_half = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half));
  Eigen::half* d_res_float = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half));

  Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float1(d_float1, rows, cols);
  Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float2(d_float2, rows, cols);
  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 2>, Eigen::Aligned> gpu_res_half(d_res_half, rows, cols);
  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 2>, Eigen::Aligned> gpu_res_float(d_res_float, rows, cols);

  gpu_float1.device(gpu_device) = gpu_float1.random() - gpu_float1.constant(0.5f);
  gpu_float2.device(gpu_device) = gpu_float2.random() - gpu_float2.constant(0.5f);

  typedef Tensor<float, 2>::DimensionPair DimPair;
  Eigen::array<DimPair, 1> dims{DimPair(1, 0)};
  gpu_res_float.device(gpu_device) = gpu_float1.contract(gpu_float2, dims).template cast<Eigen::half>();
  gpu_res_half.device(gpu_device) =
      gpu_float1.template cast<Eigen::half>().contract(gpu_float2.template cast<Eigen::half>(), dims);

  Tensor<Eigen::half, 2> half_prec(rows, cols);
  Tensor<Eigen::half, 2> full_prec(rows, cols);
  gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, num_elem * sizeof(Eigen::half));
  gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem * sizeof(Eigen::half));
  gpu_device.synchronize();

  for (int i = 0; i < rows; ++i) {
    for (int j = 0; j < cols; ++j) {
      std::cout << "Checking contract " << i << " " << j << full_prec(i, j) << " " << half_prec(i, j) << std::endl;
      // 16 * eps_half of absolute slack covers the typical accumulated rounding error of the 23-term half dot
      // product before falling back to the relative check.
      if (numext::abs(full_prec(i, j) - half_prec(i, j)) > Eigen::half(16.f) * NumTraits<Eigen::half>::epsilon()) {
        VERIFY_IS_APPROX(full_prec(i, j), half_prec(i, j));
      }
    }
  }

  gpu_device.deallocate(d_float1);
  gpu_device.deallocate(d_float2);
  gpu_device.deallocate(d_res_half);
  gpu_device.deallocate(d_res_float);
}

template <typename>
void test_gpu_reductions(int size1, int size2, int redux) {
  std::cout << "Reducing " << size1 << " by " << size2 << " tensor along dim " << redux << std::endl;

  Eigen::GpuStreamDevice stream;
  Eigen::GpuDevice gpu_device(&stream);
  int num_elem = size1 * size2;
  int result_size = (redux == 1 ? size1 : size2);

  float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float));
  Eigen::half* d_res_half = (Eigen::half*)gpu_device.allocate(result_size * sizeof(Eigen::half));
  Eigen::half* d_res_float = (Eigen::half*)gpu_device.allocate(result_size * sizeof(Eigen::half));

  Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float(d_float, size1, size2);
  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res_half(d_res_half, result_size);
  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res_float(d_res_float, result_size);

  gpu_float.device(gpu_device) = gpu_float.random() * 2.0f;

  Eigen::array<int, 1> redux_dim = {redux};
  gpu_res_float.device(gpu_device) = gpu_float.sum(redux_dim).template cast<Eigen::half>();
  gpu_res_half.device(gpu_device) = gpu_float.template cast<Eigen::half>().sum(redux_dim);

  Tensor<Eigen::half, 1> half_prec(result_size);
  Tensor<Eigen::half, 1> full_prec(result_size);
  gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, result_size * sizeof(Eigen::half));
  gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, result_size * sizeof(Eigen::half));
  gpu_device.synchronize();

  for (int i = 0; i < result_size; ++i) {
    std::cout << "EXPECTED " << full_prec(i) << " GOT " << half_prec(i) << std::endl;
    VERIFY_IS_APPROX(full_prec(i), half_prec(i));
  }

  gpu_device.deallocate(d_float);
  gpu_device.deallocate(d_res_half);
  gpu_device.deallocate(d_res_float);
}

template <typename>
void test_gpu_reductions() {
  test_gpu_reductions<void>(13, 13, 0);
  test_gpu_reductions<void>(13, 13, 1);

  test_gpu_reductions<void>(35, 36, 0);
  test_gpu_reductions<void>(35, 36, 1);

  test_gpu_reductions<void>(36, 35, 0);
  test_gpu_reductions<void>(36, 35, 1);
}

template <typename>
void test_gpu_full_reductions() {
  Eigen::GpuStreamDevice stream;
  Eigen::GpuDevice gpu_device(&stream);
  int size = 13;
  int num_elem = size * size;

  float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float));
  Eigen::half* d_res_half = (Eigen::half*)gpu_device.allocate(1 * sizeof(Eigen::half));
  Eigen::half* d_res_float = (Eigen::half*)gpu_device.allocate(1 * sizeof(Eigen::half));

  Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float(d_float, size, size);
  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 0>, Eigen::Aligned> gpu_res_half(d_res_half);
  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 0>, Eigen::Aligned> gpu_res_float(d_res_float);

  gpu_float.device(gpu_device) = gpu_float.random();

  gpu_res_float.device(gpu_device) = gpu_float.sum().template cast<Eigen::half>();
  gpu_res_half.device(gpu_device) = gpu_float.template cast<Eigen::half>().sum();

  Tensor<Eigen::half, 0> half_prec;
  Tensor<Eigen::half, 0> full_prec;
  gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, sizeof(Eigen::half));
  gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, sizeof(Eigen::half));
  gpu_device.synchronize();

  VERIFY_IS_APPROX(full_prec(), half_prec());

  gpu_res_float.device(gpu_device) = gpu_float.maximum().template cast<Eigen::half>();
  gpu_res_half.device(gpu_device) = gpu_float.template cast<Eigen::half>().maximum();
  gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, sizeof(Eigen::half));
  gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, sizeof(Eigen::half));
  gpu_device.synchronize();

  VERIFY_IS_APPROX(full_prec(), half_prec());

  gpu_device.deallocate(d_float);
  gpu_device.deallocate(d_res_half);
  gpu_device.deallocate(d_res_float);
}

template <typename>
void test_gpu_forced_evals() {
  Eigen::GpuStreamDevice stream;
  Eigen::GpuDevice gpu_device(&stream);
  int num_elem = 101;

  float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float));
  float* d_res_half1 = (float*)gpu_device.allocate(num_elem * sizeof(float));
  float* d_res_half2 = (float*)gpu_device.allocate(num_elem * sizeof(float));
  float* d_res_float = (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<float, 1>, Eigen::Aligned> gpu_res_half1(d_res_half1, num_elem);
  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Unaligned> gpu_res_half2(d_res_half2, num_elem);
  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_float(d_res_float, num_elem);

  Eigen::array<int, 1> no_bcast;
  no_bcast[0] = 1;

  gpu_float.device(gpu_device) = gpu_float.random() - gpu_float.constant(0.5f);
  gpu_res_float.device(gpu_device) = gpu_float.abs();
  gpu_res_half1.device(gpu_device) = gpu_float.template cast<Eigen::half>().abs().eval().template cast<float>();
  gpu_res_half2.device(gpu_device) =
      gpu_float.template cast<Eigen::half>().abs().broadcast(no_bcast).eval().template cast<float>();

  Tensor<float, 1> half_prec1(num_elem);
  Tensor<float, 1> half_prec2(num_elem);
  Tensor<float, 1> full_prec(num_elem);
  gpu_device.memcpyDeviceToHost(half_prec1.data(), d_res_half1, num_elem * sizeof(float));
  gpu_device.memcpyDeviceToHost(half_prec2.data(), d_res_half2, num_elem * sizeof(float));
  gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem * sizeof(float));
  gpu_device.synchronize();

  for (int i = 0; i < num_elem; ++i) {
    std::cout << "Checking forced eval " << i << full_prec(i) << " vs " << half_prec1(i) << " vs " << half_prec2(i)
              << std::endl;
    VERIFY_IS_APPROX(full_prec(i), half_prec1(i));
    VERIFY_IS_APPROX(full_prec(i), half_prec2(i));
  }

  gpu_device.deallocate(d_float);
  gpu_device.deallocate(d_res_half1);
  gpu_device.deallocate(d_res_half2);
  gpu_device.deallocate(d_res_float);
}

EIGEN_DECLARE_TEST(tensor_of_float16_gpu) {
  CALL_SUBTEST_1(test_gpu_numext<void>());

  CALL_SUBTEST_1(test_gpu_unary<void>());
  CALL_SUBTEST_1(test_gpu_elementwise<void>());
  CALL_SUBTEST_1(test_gpu_trancendental<void>());
  CALL_SUBTEST_2(test_gpu_contractions<void>());
  CALL_SUBTEST_3(test_gpu_reductions<void>());
  CALL_SUBTEST_4(test_gpu_full_reductions<void>());
  CALL_SUBTEST_5(test_gpu_forced_evals<void>());
}