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eigen/unsupported/test/cxx11_tensor_cuda.cpp

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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2014 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/.
// TODO(mdevin): Free the cuda memory.
#define EIGEN_TEST_NO_LONGDOUBLE
#define EIGEN_TEST_NO_COMPLEX
#define EIGEN_TEST_FUNC cxx11_tensor_cuda
#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
#define EIGEN_USE_GPU
#include "main.h"
#include <unsupported/Eigen/CXX11/Tensor>
using Eigen::Tensor;
void test_cuda_elementwise_small() {
Tensor<float, 1> in1(Eigen::array<int, 1>(2));
Tensor<float, 1> in2(Eigen::array<int, 1>(2));
Tensor<float, 1> out(Eigen::array<int, 1>(2));
in1.setRandom();
in2.setRandom();
std::size_t in1_bytes = in1.size() * sizeof(float);
std::size_t in2_bytes = in2.size() * sizeof(float);
std::size_t out_bytes = out.size() * sizeof(float);
float* d_in1;
float* d_in2;
float* d_out;
cudaMalloc((void**)(&d_in1), in1_bytes);
cudaMalloc((void**)(&d_in2), in2_bytes);
cudaMalloc((void**)(&d_out), out_bytes);
cudaMemcpy(d_in1, in1.data(), in1_bytes, cudaMemcpyHostToDevice);
cudaMemcpy(d_in2, in2.data(), in2_bytes, cudaMemcpyHostToDevice);
cudaStream_t stream;
assert(cudaStreamCreate(&stream) == cudaSuccess);
Eigen::GpuDevice gpu_device(&stream);
Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_in1(
d_in1, Eigen::array<int, 1>(2));
Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_in2(
d_in2, Eigen::array<int, 1>(2));
Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_out(
d_out, Eigen::array<int, 1>(2));
gpu_out.device(gpu_device) = gpu_in1 + gpu_in2;
assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost,
gpu_device.stream()) == cudaSuccess);
assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess);
for (int i = 0; i < 2; ++i) {
VERIFY_IS_APPROX(
out(Eigen::array<int, 1>(i)),
in1(Eigen::array<int, 1>(i)) + in2(Eigen::array<int, 1>(i)));
}
}
void test_cuda_elementwise()
{
Tensor<float, 3> in1(Eigen::array<int, 3>(72,53,97));
Tensor<float, 3> in2(Eigen::array<int, 3>(72,53,97));
Tensor<float, 3> in3(Eigen::array<int, 3>(72,53,97));
Tensor<float, 3> out(Eigen::array<int, 3>(72,53,97));
in1.setRandom();
in2.setRandom();
in3.setRandom();
std::size_t in1_bytes = in1.size() * sizeof(float);
std::size_t in2_bytes = in2.size() * sizeof(float);
std::size_t in3_bytes = in3.size() * sizeof(float);
std::size_t out_bytes = out.size() * sizeof(float);
float* d_in1;
float* d_in2;
float* d_in3;
float* d_out;
cudaMalloc((void**)(&d_in1), in1_bytes);
cudaMalloc((void**)(&d_in2), in2_bytes);
cudaMalloc((void**)(&d_in3), in3_bytes);
cudaMalloc((void**)(&d_out), out_bytes);
cudaMemcpy(d_in1, in1.data(), in1_bytes, cudaMemcpyHostToDevice);
cudaMemcpy(d_in2, in2.data(), in2_bytes, cudaMemcpyHostToDevice);
cudaMemcpy(d_in3, in3.data(), in3_bytes, cudaMemcpyHostToDevice);
cudaStream_t stream;
assert(cudaStreamCreate(&stream) == cudaSuccess);
Eigen::GpuDevice gpu_device(&stream);
Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_in1(d_in1, Eigen::array<int, 3>(72,53,97));
Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_in2(d_in2, Eigen::array<int, 3>(72,53,97));
Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_in3(d_in3, Eigen::array<int, 3>(72,53,97));
Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_out(d_out, Eigen::array<int, 3>(72,53,97));
gpu_out.device(gpu_device) = gpu_in1 + gpu_in2 * gpu_in3;
assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess);
assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess);
for (int i = 0; i < 72; ++i) {
for (int j = 0; j < 53; ++j) {
for (int k = 0; k < 97; ++k) {
VERIFY_IS_APPROX(out(Eigen::array<int, 3>(i,j,k)), in1(Eigen::array<int, 3>(i,j,k)) + in2(Eigen::array<int, 3>(i,j,k)) * in3(Eigen::array<int, 3>(i,j,k)));
}
}
}
}
void test_cuda_reduction()
{
Tensor<float, 4> in1(Eigen::array<int, 4>(72,53,97,113));
Tensor<float, 2> out(Eigen::array<int, 2>(72,97));
in1.setRandom();
std::size_t in1_bytes = in1.size() * sizeof(float);
std::size_t out_bytes = out.size() * sizeof(float);
float* d_in1;
float* d_out;
cudaMalloc((void**)(&d_in1), in1_bytes);
cudaMalloc((void**)(&d_out), out_bytes);
cudaMemcpy(d_in1, in1.data(), in1_bytes, cudaMemcpyHostToDevice);
cudaStream_t stream;
assert(cudaStreamCreate(&stream) == cudaSuccess);
Eigen::GpuDevice gpu_device(&stream);
Eigen::TensorMap<Eigen::Tensor<float, 4> > gpu_in1(d_in1, Eigen::array<int, 4>(72,53,97,113));
Eigen::TensorMap<Eigen::Tensor<float, 2> > gpu_out(d_out, Eigen::array<int, 2>(72,97));
array<int, 2> reduction_axis;
reduction_axis[0] = 1;
reduction_axis[1] = 3;
gpu_out.device(gpu_device) = gpu_in1.maximum(reduction_axis);
assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess);
assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess);
for (int i = 0; i < 72; ++i) {
for (int j = 0; j < 97; ++j) {
float expected = 0;
for (int k = 0; k < 53; ++k) {
for (int l = 0; l < 113; ++l) {
expected =
std::max<float>(expected, in1(Eigen::array<int, 4>(i, k, j, l)));
}
}
VERIFY_IS_APPROX(out(Eigen::array<int, 2>(i,j)), expected);
}
}
}
template<int DataLayout>
static void test_cuda_contraction()
{
// with these dimensions, the output has 300 * 140 elements, which is
// more than 30 * 1024, which is the number of threads in blocks on
// a 15 SM GK110 GPU
Tensor<float, 4, DataLayout> t_left(Eigen::array<int, 4>(6, 50, 3, 31));
Tensor<float, 5, DataLayout> t_right(Eigen::array<int, 5>(3, 31, 7, 20, 1));
Tensor<float, 5, DataLayout> t_result(Eigen::array<int, 5>(6, 50, 7, 20, 1));
t_left.setRandom();
t_right.setRandom();
std::size_t t_left_bytes = t_left.size() * sizeof(float);
std::size_t t_right_bytes = t_right.size() * sizeof(float);
std::size_t t_result_bytes = t_result.size() * sizeof(float);
float* d_t_left;
float* d_t_right;
float* d_t_result;
cudaMalloc((void**)(&d_t_left), t_left_bytes);
cudaMalloc((void**)(&d_t_right), t_right_bytes);
cudaMalloc((void**)(&d_t_result), t_result_bytes);
cudaMemcpy(d_t_left, t_left.data(), t_left_bytes, cudaMemcpyHostToDevice);
cudaMemcpy(d_t_right, t_right.data(), t_right_bytes, cudaMemcpyHostToDevice);
cudaStream_t stream;
assert(cudaStreamCreate(&stream) == cudaSuccess);
Eigen::GpuDevice gpu_device(&stream);
Eigen::TensorMap<Eigen::Tensor<float, 4, DataLayout> >
gpu_t_left(d_t_left, Eigen::array<int, 4>(6, 50, 3, 31));
Eigen::TensorMap<Eigen::Tensor<float, 5, DataLayout> >
gpu_t_right(d_t_right, Eigen::array<int, 5>(3, 31, 7, 20, 1));
Eigen::TensorMap<Eigen::Tensor<float, 5, DataLayout> >
gpu_t_result(d_t_result, Eigen::array<int, 5>(6, 50, 7, 20, 1));
typedef Eigen::Map<Eigen::Matrix<float, Dynamic, Dynamic, DataLayout> > MapXf;
MapXf m_left(t_left.data(), 300, 93);
MapXf m_right(t_right.data(), 93, 140);
Eigen::Matrix<float, Dynamic, Dynamic, DataLayout> m_result(300, 140);
typedef Tensor<float, 1>::DimensionPair DimPair;
Eigen::array<DimPair, 2> dims;
dims[0] = DimPair(2, 0);
dims[1] = DimPair(3, 1);
m_result = m_left * m_right;
gpu_t_result.device(gpu_device) = gpu_t_left.contract(gpu_t_right, dims);
cudaMemcpy(t_result.data(), d_t_result, t_result_bytes, cudaMemcpyDeviceToHost);
for (size_t i = 0; i < t_result.dimensions().TotalSize(); i++) {
if (fabs(t_result.data()[i] - m_result.data()[i]) >= 1e-4) {
cout << "mismatch detected at index " << i << ": " << t_result.data()[i] << " vs " << m_result.data()[i] << endl;
assert(false);
}
}
}
static void test_cuda_convolution_1d()
{
Tensor<float, 4> input(Eigen::array<int, 4>(74,37,11,137));
Tensor<float, 1> kernel(Eigen::array<int, 1>(4));
Tensor<float, 4> out(Eigen::array<int, 4>(74,34,11,137));
input = input.constant(10.0f) + input.random();
kernel = kernel.constant(7.0f) + kernel.random();
std::size_t input_bytes = input.size() * sizeof(float);
std::size_t kernel_bytes = kernel.size() * sizeof(float);
std::size_t out_bytes = out.size() * sizeof(float);
float* d_input;
float* d_kernel;
float* d_out;
cudaMalloc((void**)(&d_input), input_bytes);
cudaMalloc((void**)(&d_kernel), kernel_bytes);
cudaMalloc((void**)(&d_out), out_bytes);
cudaMemcpy(d_input, input.data(), input_bytes, cudaMemcpyHostToDevice);
cudaMemcpy(d_kernel, kernel.data(), kernel_bytes, cudaMemcpyHostToDevice);
cudaStream_t stream;
assert(cudaStreamCreate(&stream) == cudaSuccess);
Eigen::GpuDevice gpu_device(&stream);
Eigen::TensorMap<Eigen::Tensor<float, 4> > gpu_input(d_input, Eigen::array<int, 4>(74,37,11,137));
Eigen::TensorMap<Eigen::Tensor<float, 1> > gpu_kernel(d_kernel, Eigen::array<int, 1>(4));
Eigen::TensorMap<Eigen::Tensor<float, 4> > gpu_out(d_out, Eigen::array<int, 4>(74,34,11,137));
Eigen::array<int, 1> dims(1);
gpu_out.device(gpu_device) = gpu_input.convolve(gpu_kernel, dims);
assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess);
assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess);
for (int i = 0; i < 74; ++i) {
for (int j = 0; j < 34; ++j) {
for (int k = 0; k < 11; ++k) {
for (int l = 0; l < 137; ++l) {
const float result = out(Eigen::array<int, 4>(i,j,k,l));
const float expected = input(Eigen::array<int, 4>(i,j+0,k,l)) * kernel(Eigen::array<int, 1>(0)) +
input(Eigen::array<int, 4>(i,j+1,k,l)) * kernel(Eigen::array<int, 1>(1)) +
input(Eigen::array<int, 4>(i,j+2,k,l)) * kernel(Eigen::array<int, 1>(2)) +
input(Eigen::array<int, 4>(i,j+3,k,l)) * kernel(Eigen::array<int, 1>(3));
VERIFY_IS_APPROX(result, expected);
}
}
}
}
}
static void test_cuda_convolution_2d()
{
Tensor<float, 4> input(Eigen::array<int, 4>(74,37,11,137));
Tensor<float, 2> kernel(Eigen::array<int, 2>(3,4));
Tensor<float, 4> out(Eigen::array<int, 4>(74,35,8,137));
input = input.constant(10.0f) + input.random();
kernel = kernel.constant(7.0f) + kernel.random();
std::size_t input_bytes = input.size() * sizeof(float);
std::size_t kernel_bytes = kernel.size() * sizeof(float);
std::size_t out_bytes = out.size() * sizeof(float);
float* d_input;
float* d_kernel;
float* d_out;
cudaMalloc((void**)(&d_input), input_bytes);
cudaMalloc((void**)(&d_kernel), kernel_bytes);
cudaMalloc((void**)(&d_out), out_bytes);
cudaMemcpy(d_input, input.data(), input_bytes, cudaMemcpyHostToDevice);
cudaMemcpy(d_kernel, kernel.data(), kernel_bytes, cudaMemcpyHostToDevice);
cudaStream_t stream;
assert(cudaStreamCreate(&stream) == cudaSuccess);
Eigen::GpuDevice gpu_device(&stream);
Eigen::TensorMap<Eigen::Tensor<float, 4> > gpu_input(d_input, Eigen::array<int, 4>(74,37,11,137));
Eigen::TensorMap<Eigen::Tensor<float, 2> > gpu_kernel(d_kernel, Eigen::array<int, 2>(3,4));
Eigen::TensorMap<Eigen::Tensor<float, 4> > gpu_out(d_out, Eigen::array<int, 4>(74,35,8,137));
Eigen::array<int, 2> dims(1,2);
gpu_out.device(gpu_device) = gpu_input.convolve(gpu_kernel, dims);
assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess);
assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess);
for (int i = 0; i < 74; ++i) {
for (int j = 0; j < 35; ++j) {
for (int k = 0; k < 8; ++k) {
for (int l = 0; l < 137; ++l) {
const float result = out(Eigen::array<int, 4>(i,j,k,l));
const float expected = input(Eigen::array<int, 4>(i,j+0,k+0,l)) * kernel(Eigen::array<int, 2>(0,0)) +
input(Eigen::array<int, 4>(i,j+1,k+0,l)) * kernel(Eigen::array<int, 2>(1,0)) +
input(Eigen::array<int, 4>(i,j+2,k+0,l)) * kernel(Eigen::array<int, 2>(2,0)) +
input(Eigen::array<int, 4>(i,j+0,k+1,l)) * kernel(Eigen::array<int, 2>(0,1)) +
input(Eigen::array<int, 4>(i,j+1,k+1,l)) * kernel(Eigen::array<int, 2>(1,1)) +
input(Eigen::array<int, 4>(i,j+2,k+1,l)) * kernel(Eigen::array<int, 2>(2,1)) +
input(Eigen::array<int, 4>(i,j+0,k+2,l)) * kernel(Eigen::array<int, 2>(0,2)) +
input(Eigen::array<int, 4>(i,j+1,k+2,l)) * kernel(Eigen::array<int, 2>(1,2)) +
input(Eigen::array<int, 4>(i,j+2,k+2,l)) * kernel(Eigen::array<int, 2>(2,2)) +
input(Eigen::array<int, 4>(i,j+0,k+3,l)) * kernel(Eigen::array<int, 2>(0,3)) +
input(Eigen::array<int, 4>(i,j+1,k+3,l)) * kernel(Eigen::array<int, 2>(1,3)) +
input(Eigen::array<int, 4>(i,j+2,k+3,l)) * kernel(Eigen::array<int, 2>(2,3));
VERIFY_IS_APPROX(result, expected);
}
}
}
}
}
static void test_cuda_convolution_3d()
{
Tensor<float, 5> input(Eigen::array<int, 5>(74,37,11,137,17));
Tensor<float, 3> kernel(Eigen::array<int, 3>(3,4,2));
Tensor<float, 5> out(Eigen::array<int, 5>(74,35,8,136,17));
input = input.constant(10.0f) + input.random();
kernel = kernel.constant(7.0f) + kernel.random();
std::size_t input_bytes = input.size() * sizeof(float);
std::size_t kernel_bytes = kernel.size() * sizeof(float);
std::size_t out_bytes = out.size() * sizeof(float);
float* d_input;
float* d_kernel;
float* d_out;
cudaMalloc((void**)(&d_input), input_bytes);
cudaMalloc((void**)(&d_kernel), kernel_bytes);
cudaMalloc((void**)(&d_out), out_bytes);
cudaMemcpy(d_input, input.data(), input_bytes, cudaMemcpyHostToDevice);
cudaMemcpy(d_kernel, kernel.data(), kernel_bytes, cudaMemcpyHostToDevice);
cudaStream_t stream;
assert(cudaStreamCreate(&stream) == cudaSuccess);
Eigen::GpuDevice gpu_device(&stream);
Eigen::TensorMap<Eigen::Tensor<float, 5> > gpu_input(d_input, Eigen::array<int, 5>(74,37,11,137,17));
Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_kernel(d_kernel, Eigen::array<int, 3>(3,4,2));
Eigen::TensorMap<Eigen::Tensor<float, 5> > gpu_out(d_out, Eigen::array<int, 5>(74,35,8,136,17));
Eigen::array<int, 3> dims(1,2,3);
gpu_out.device(gpu_device) = gpu_input.convolve(gpu_kernel, dims);
assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess);
assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess);
for (int i = 0; i < 74; ++i) {
for (int j = 0; j < 35; ++j) {
for (int k = 0; k < 8; ++k) {
for (int l = 0; l < 136; ++l) {
for (int m = 0; m < 17; ++m) {
const float result = out(Eigen::array<int, 5>(i,j,k,l,m));
const float expected = input(Eigen::array<int, 5>(i,j+0,k+0,l+0,m)) * kernel(Eigen::array<int, 3>(0,0,0)) +
input(Eigen::array<int, 5>(i,j+1,k+0,l+0,m)) * kernel(Eigen::array<int, 3>(1,0,0)) +
input(Eigen::array<int, 5>(i,j+2,k+0,l+0,m)) * kernel(Eigen::array<int, 3>(2,0,0)) +
input(Eigen::array<int, 5>(i,j+0,k+1,l+0,m)) * kernel(Eigen::array<int, 3>(0,1,0)) +
input(Eigen::array<int, 5>(i,j+1,k+1,l+0,m)) * kernel(Eigen::array<int, 3>(1,1,0)) +
input(Eigen::array<int, 5>(i,j+2,k+1,l+0,m)) * kernel(Eigen::array<int, 3>(2,1,0)) +
input(Eigen::array<int, 5>(i,j+0,k+2,l+0,m)) * kernel(Eigen::array<int, 3>(0,2,0)) +
input(Eigen::array<int, 5>(i,j+1,k+2,l+0,m)) * kernel(Eigen::array<int, 3>(1,2,0)) +
input(Eigen::array<int, 5>(i,j+2,k+2,l+0,m)) * kernel(Eigen::array<int, 3>(2,2,0)) +
input(Eigen::array<int, 5>(i,j+0,k+3,l+0,m)) * kernel(Eigen::array<int, 3>(0,3,0)) +
input(Eigen::array<int, 5>(i,j+1,k+3,l+0,m)) * kernel(Eigen::array<int, 3>(1,3,0)) +
input(Eigen::array<int, 5>(i,j+2,k+3,l+0,m)) * kernel(Eigen::array<int, 3>(2,3,0)) +
input(Eigen::array<int, 5>(i,j+0,k+0,l+1,m)) * kernel(Eigen::array<int, 3>(0,0,1)) +
input(Eigen::array<int, 5>(i,j+1,k+0,l+1,m)) * kernel(Eigen::array<int, 3>(1,0,1)) +
input(Eigen::array<int, 5>(i,j+2,k+0,l+1,m)) * kernel(Eigen::array<int, 3>(2,0,1)) +
input(Eigen::array<int, 5>(i,j+0,k+1,l+1,m)) * kernel(Eigen::array<int, 3>(0,1,1)) +
input(Eigen::array<int, 5>(i,j+1,k+1,l+1,m)) * kernel(Eigen::array<int, 3>(1,1,1)) +
input(Eigen::array<int, 5>(i,j+2,k+1,l+1,m)) * kernel(Eigen::array<int, 3>(2,1,1)) +
input(Eigen::array<int, 5>(i,j+0,k+2,l+1,m)) * kernel(Eigen::array<int, 3>(0,2,1)) +
input(Eigen::array<int, 5>(i,j+1,k+2,l+1,m)) * kernel(Eigen::array<int, 3>(1,2,1)) +
input(Eigen::array<int, 5>(i,j+2,k+2,l+1,m)) * kernel(Eigen::array<int, 3>(2,2,1)) +
input(Eigen::array<int, 5>(i,j+0,k+3,l+1,m)) * kernel(Eigen::array<int, 3>(0,3,1)) +
input(Eigen::array<int, 5>(i,j+1,k+3,l+1,m)) * kernel(Eigen::array<int, 3>(1,3,1)) +
input(Eigen::array<int, 5>(i,j+2,k+3,l+1,m)) * kernel(Eigen::array<int, 3>(2,3,1));
VERIFY_IS_APPROX(result, expected);
}
}
}
}
}
}
static float* CudaCopyFloat(float* data, int size) {
const int nbytes = size * sizeof(float);
float* result = NULL;
if (cudaMalloc((void**)(&result), nbytes) != cudaSuccess) {
return NULL;
} else {
if (data != NULL) {
cudaMemcpy(result, data, nbytes, cudaMemcpyHostToDevice);
}
return result;
}
}
static void test_cuda_constant_broadcast()
{
cudaStream_t stream;
assert(cudaStreamCreate(&stream) == cudaSuccess);
Eigen::GpuDevice gpu_device(&stream);
Tensor<float, 1> t1(10);
for (int i = 0; i < 10; ++i) {
t1(i) = 10.0f * i;
}
float* t1_cuda = CudaCopyFloat(t1.data(), t1.size());
Eigen::TensorMap<Eigen::Tensor<float, 1> > t1_gpu(t1_cuda, 10);
Tensor<float, 1> t2(1);
t2 = t2.constant(20.0f);
float* t2_cuda = CudaCopyFloat(t2.data(), t2.size());
Eigen::TensorMap<Eigen::TensorFixedSize<float, Sizes<1> > > t2_gpu(t2_cuda, 1);
float* t3_cuda = CudaCopyFloat(NULL, 10);
Eigen::TensorMap<Eigen::Tensor<float, 1> > t3_gpu(t3_cuda, 10);
t3_gpu.device(gpu_device) =
t1_gpu + t2_gpu.broadcast(Eigen::array<int, 1>(10));
Eigen::Tensor<float, 1> t3(10);
cudaMemcpy(t3.data(), t3_gpu.data(), 10 * sizeof(float),
cudaMemcpyDeviceToHost);
for (int i = 0; i < 10; ++i) {
VERIFY_IS_APPROX(t3(i), t1(i) + t2(0));
}
}
void test_cuda_cast()
{
Tensor<double, 3> in(Eigen::array<int, 3>(72,53,97));
Tensor<float, 3> out(Eigen::array<int, 3>(72,53,97));
in.setRandom();
std::size_t in_bytes = in.size() * sizeof(double);
std::size_t out_bytes = out.size() * sizeof(float);
double* d_in;
float* d_out;
cudaMalloc((void**)(&d_in), in_bytes);
cudaMalloc((void**)(&d_out), out_bytes);
cudaMemcpy(d_in, in.data(), in_bytes, cudaMemcpyHostToDevice);
cudaStream_t stream;
assert(cudaStreamCreate(&stream) == cudaSuccess);
Eigen::GpuDevice gpu_device(&stream);
Eigen::TensorMap<Eigen::Tensor<double, 3> > gpu_in(d_in, Eigen::array<int, 3>(72,53,97));
Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_out(d_out, Eigen::array<int, 3>(72,53,97));
gpu_out.device(gpu_device) = gpu_in.template cast<float>();
assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess);
assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess);
for (int i = 0; i < 72; ++i) {
for (int j = 0; j < 53; ++j) {
for (int k = 0; k < 97; ++k) {
VERIFY_IS_APPROX(out(Eigen::array<int, 3>(i,j,k)), static_cast<float>(in(Eigen::array<int, 3>(i,j,k))));
}
}
}
}
void test_cxx11_tensor_cuda()
{
CALL_SUBTEST(test_cuda_elementwise_small());
CALL_SUBTEST(test_cuda_elementwise());
CALL_SUBTEST(test_cuda_reduction());
CALL_SUBTEST(test_cuda_contraction<ColMajor>());
CALL_SUBTEST(test_cuda_contraction<RowMajor>());
CALL_SUBTEST(test_cuda_convolution_1d());
CALL_SUBTEST(test_cuda_convolution_2d());
CALL_SUBTEST(test_cuda_convolution_3d());
CALL_SUBTEST(test_cuda_constant_broadcast());
CALL_SUBTEST(test_cuda_cast());
}
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