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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2016
// Mehdi Goli Codeplay Software Ltd.
// Ralph Potter Codeplay Software Ltd.
// Luke Iwanski Codeplay Software Ltd.
// Contact: <eigen@codeplay.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_TEST_FUNC cxx11_tensor_contract_sycl
#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
#define EIGEN_USE_SYCL
#include <iostream>
#include <chrono>
#include <ctime>
#include "main.h"
#include <unsupported/Eigen/CXX11/Tensor>
using Eigen::array;
using Eigen::SyclDevice;
using Eigen::Tensor;
using Eigen::TensorMap;
typedef Tensor<float, 1>::DimensionPair DimPair;
template<int DataLayout, typename Device>
void test_sycl_contraction(const Device& sycl_device, int m_size, int k_size, int n_size)
{
// std::cout << "Testing for (" << m_size << "," << k_size << "," << n_size << ")" << std::endl;
// 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, 2, DataLayout> t_left(m_size, k_size);
Tensor<float, 2, DataLayout> t_right(k_size, n_size);
Tensor<float, 2, DataLayout> t_result(m_size, n_size);
Tensor<float, 2, DataLayout> t_result_gpu(m_size, n_size);
// Eigen::array<DimPair, 1> dims(DimPair(1, 0));
Eigen::array<DimPair, 1> dims = {{DimPair(1, 0)}};
Eigen::array<int, 2> left_dims = {{m_size, k_size}};
Eigen::array<int, 2> right_dims = {{k_size, n_size}};
Eigen::array<int, 2> result_dims = {{m_size, n_size}};
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 = static_cast<float*>(sycl_device.allocate(t_left_bytes));
float * d_t_right = static_cast<float*>(sycl_device.allocate(t_right_bytes));
float * d_t_result = static_cast<float*>(sycl_device.allocate(t_result_bytes));
Eigen::TensorMap<Eigen::Tensor<float, 2, DataLayout> > gpu_t_left(d_t_left, left_dims);
Eigen::TensorMap<Eigen::Tensor<float, 2, DataLayout> > gpu_t_right(d_t_right, right_dims);
Eigen::TensorMap<Eigen::Tensor<float, 2, DataLayout> > gpu_t_result(d_t_result, result_dims);
sycl_device.memcpyHostToDevice(d_t_left, t_left.data(),t_left_bytes);
sycl_device.memcpyHostToDevice(d_t_right, t_right.data(),t_right_bytes);
gpu_t_result.device(sycl_device) = gpu_t_left.contract(gpu_t_right, dims);
t_result = t_left.contract(t_right, dims);
sycl_device.memcpyDeviceToHost(t_result_gpu.data(), d_t_result, t_result_bytes);
for (DenseIndex i = 0; i < t_result.size(); i++) {
if (static_cast<float>(fabs(t_result(i) - t_result_gpu(i))) < 1e-4f) {
continue;
}
if (Eigen::internal::isApprox(t_result(i), t_result_gpu(i), 1e-4f)) {
continue;
}
std::cout << "mismatch detected at index " << i << ": " << t_result(i)
<< " vs " << t_result_gpu(i) << std::endl;
assert(false);
}
sycl_device.deallocate(d_t_left);
sycl_device.deallocate(d_t_right);
sycl_device.deallocate(d_t_result);
}
template<int DataLayout, typename Device>
void test_scalar(const Device& sycl_device, int m_size, int k_size, int n_size)
{
//std::cout << "Testing for (" << m_size << "," << k_size << "," << n_size << ")" << std::endl;
// 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, 2, DataLayout> t_left(m_size, k_size);
Tensor<float, 2, DataLayout> t_right(k_size, n_size);
Tensor<float, 0, DataLayout> t_result;
Tensor<float, 0, DataLayout> t_result_gpu;
Eigen::array<DimPair, 2> dims = {{DimPair(0, 0), DimPair(1, 1)}};
Eigen::array<int, 2> left_dims = {{m_size, k_size}};
Eigen::array<int, 2> right_dims = {{k_size, n_size}};
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 = sizeof(float);
float * d_t_left = static_cast<float*>(sycl_device.allocate(t_left_bytes));
float * d_t_right = static_cast<float*>(sycl_device.allocate(t_right_bytes));
float * d_t_result = static_cast<float*>(sycl_device.allocate(t_result_bytes));
Eigen::TensorMap<Eigen::Tensor<float, 2, DataLayout> > gpu_t_left(d_t_left, left_dims);
Eigen::TensorMap<Eigen::Tensor<float, 2, DataLayout> > gpu_t_right(d_t_right, right_dims);
Eigen::TensorMap<Eigen::Tensor<float, 0, DataLayout> > gpu_t_result(d_t_result);
sycl_device.memcpyHostToDevice(d_t_left, t_left.data(),t_left_bytes);
sycl_device.memcpyHostToDevice(d_t_right, t_right.data(),t_right_bytes);
gpu_t_result.device(sycl_device) = gpu_t_left.contract(gpu_t_right, dims);
t_result = t_left.contract(t_right, dims);
sycl_device.memcpyDeviceToHost(t_result_gpu.data(), d_t_result, t_result_bytes);
if (static_cast<float>(fabs(t_result() - t_result_gpu())) > 1e-4f &&
!Eigen::internal::isApprox(t_result(), t_result_gpu(), 1e-4f)) {
std::cout << "mismatch detected: " << t_result()
<< " vs " << t_result_gpu() << std::endl;
assert(false);
}
sycl_device.deallocate(d_t_left);
sycl_device.deallocate(d_t_right);
sycl_device.deallocate(d_t_result);
}
template<int DataLayout, typename Device>
void test_sycl_contraction_m(const Device& sycl_device) {
for (int k = 32; k < 256; k++) {
test_sycl_contraction<DataLayout>(sycl_device, k, 128, 128);
}
}
template<int DataLayout, typename Device>
void test_sycl_contraction_k(const Device& sycl_device) {
for (int k = 32; k < 256; k++) {
test_sycl_contraction<DataLayout>(sycl_device, 128, k, 128);
}
}
template<int DataLayout, typename Device>
void test_sycl_contraction_n(const Device& sycl_device) {
for (int k = 32; k < 256; k++) {
test_sycl_contraction<DataLayout>(sycl_device, 128, 128, k);
}
}
template<int DataLayout, typename Device>
void test_sycl_contraction_sizes(const Device& sycl_device) {
int m_sizes[] = { 31, 39, 63, 64, 65,
127, 129, 255, 257 , 511,
512, 513, 1023, 1024, 1025};
int n_sizes[] = { 31, 39, 63, 64, 65,
127, 129, 255, 257, 511,
512, 513, 1023, 1024, 1025};
int k_sizes[] = { 31, 39, 63, 64, 65,
95, 96, 127, 129, 255,
257, 511, 512, 513, 1023,
1024, 1025};
for (int i = 0; i < 15; i++) {
for (int j = 0; j < 15; j++) {
for (int k = 0; k < 17; k++) {
test_sycl_contraction<DataLayout>(sycl_device, m_sizes[i], n_sizes[j], k_sizes[k]);
}
}
}
}
template <typename Dev_selector> void tensorContractionPerDevice(Dev_selector& s){
QueueInterface queueInterface(s);
auto sycl_device=Eigen::SyclDevice(&queueInterface);
test_sycl_contraction<ColMajor>(sycl_device, 32, 32, 32);
test_sycl_contraction<RowMajor>(sycl_device, 32, 32, 32);
test_scalar<ColMajor>(sycl_device, 32, 32, 32);
test_scalar<RowMajor>(sycl_device, 32, 32, 32);
std::chrono::time_point<std::chrono::system_clock> start, end;
start = std::chrono::system_clock::now();
test_sycl_contraction<ColMajor>(sycl_device, 128, 128, 128);
test_sycl_contraction<RowMajor>(sycl_device, 128, 128, 128);
test_scalar<ColMajor>(sycl_device, 128, 128, 128);
test_scalar<RowMajor>(sycl_device, 128, 128, 128);
test_sycl_contraction_m<ColMajor>(sycl_device);
test_sycl_contraction_m<RowMajor>(sycl_device);
test_sycl_contraction_n<ColMajor>(sycl_device);
test_sycl_contraction_n<RowMajor>(sycl_device);
test_sycl_contraction_k<ColMajor>(sycl_device);
test_sycl_contraction_k<RowMajor>(sycl_device);
test_sycl_contraction_sizes<ColMajor>(sycl_device);
test_sycl_contraction_sizes<RowMajor>(sycl_device);
end = std::chrono::system_clock::now();
std::chrono::duration<double> elapsed_seconds = end-start;
std::time_t end_time = std::chrono::system_clock::to_time_t(end);
std::cout << "finished computation at " << std::ctime(&end_time)
<< "elapsed time: " << elapsed_seconds.count() << "s\n";
}
void test_cxx11_tensor_contract_sycl() {
for (const auto& device :Eigen::get_sycl_supported_devices()) {
CALL_SUBTEST(tensorContractionPerDevice(device));
}
}
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