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// 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/.
#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 <unsupported/Eigen/CXX11/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(cxx11_tensor_cast_float16_gpu)
{
CALL_SUBTEST(test_gpu_conversion());
CALL_SUBTEST(test_fallback_conversion());
}
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