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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_DEFAULT_DENSE_INDEX_TYPE int64_t
#define EIGEN_USE_SYCL
#include <algorithm>
#include <chrono>
#include <ctime>
#include <iostream>
#include "main.h"
#include <unsupported/Eigen/CXX11/Tensor>
using Eigen::array;
using Eigen::SyclDevice;
using Eigen::Tensor;
using Eigen::TensorMap;
template <int DataLayout, typename DataType, typename IndexType, typename Device>
void static test_sycl_contraction(const Device &sycl_device, IndexType m_size, IndexType k_size, IndexType n_size) {
typedef typename Tensor<DataType, 1, DataLayout, IndexType>::DimensionPair DimPair;
static const DataType error_threshold = DataType(1e-4);
// 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<DataType, 2, DataLayout, IndexType> t_left(m_size, k_size);
Tensor<DataType, 2, DataLayout, IndexType> t_right(k_size, n_size);
Tensor<DataType, 2, DataLayout, IndexType> t_result(m_size, n_size);
Tensor<DataType, 2, DataLayout, IndexType> t_result_gpu(m_size, n_size);
Eigen::array<DimPair, 1> dims = {{DimPair(1, 0)}};
Eigen::array<IndexType, 2> left_dims = {{m_size, k_size}};
Eigen::array<IndexType, 2> right_dims = {{k_size, n_size}};
Eigen::array<IndexType, 2> result_dims = {{m_size, n_size}};
t_left.setRandom();
t_right.setRandom();
std::size_t t_left_bytes = t_left.size() * sizeof(DataType);
std::size_t t_right_bytes = t_right.size() * sizeof(DataType);
std::size_t t_result_bytes = t_result.size() * sizeof(DataType);
DataType *d_t_left = static_cast<DataType *>(sycl_device.allocate(t_left_bytes));
DataType *d_t_right = static_cast<DataType *>(sycl_device.allocate(t_right_bytes));
DataType *d_t_result = static_cast<DataType *>(sycl_device.allocate(t_result_bytes));
Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_t_left(d_t_left, left_dims);
Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_t_right(d_t_right, right_dims);
Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> 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);
sycl_device.memcpyDeviceToHost(t_result_gpu.data(), d_t_result, t_result_bytes);
t_result = t_left.contract(t_right, dims);
for (IndexType i = 0; i < t_result.size(); i++) {
if (static_cast<DataType>(std::fabs(static_cast<DataType>(t_result(i) - t_result_gpu(i)))) < error_threshold) {
continue;
}
if (Eigen::internal::isApprox(t_result(i), t_result_gpu(i), error_threshold)) {
continue;
}
std::cout << "M : " << m_size << ", N : " << n_size << ", K : " << k_size << ", mismatch detected at IndexType "
<< i << ": " << t_result(i) << " vs " << t_result_gpu(i) << std::endl;
VERIFY_IS_APPROX(t_result_gpu(i), t_result(i));
}
sycl_device.deallocate(d_t_left);
sycl_device.deallocate(d_t_right);
sycl_device.deallocate(d_t_result);
}
template <int DataLayout, typename DataType, typename IndexType, typename Device>
void test_sycl_contraction_m(const Device &sycl_device) {
for (IndexType k = 32; k < 256; k++) {
test_sycl_contraction<DataLayout, DataType, IndexType>(sycl_device, k, 128, 128);
}
}
template <int DataLayout, typename DataType, typename IndexType, typename Device>
void test_sycl_contraction_k(const Device &sycl_device) {
for (IndexType k = 32; k < 256; k++) {
test_sycl_contraction<DataLayout, DataType, IndexType>(sycl_device, 128, k, 128);
}
}
template <int DataLayout, typename DataType, typename IndexType, typename Device>
void test_sycl_contraction_n(const Device &sycl_device) {
for (IndexType k = 32; k < 256; k++) {
test_sycl_contraction<DataLayout, DataType, IndexType>(sycl_device, 128, 128, k);
}
}
template <int DataLayout, typename DataType, typename IndexType, typename Device>
void test_sycl_contraction_sizes(const Device &sycl_device) {
IndexType m_sizes[] = {31, 39, 63, 64, 65, 127, 129, 255, 257, 511, 512, 513, 1023, 1024, 1025};
IndexType n_sizes[] = {31, 39, 63, 64, 65, 127, 129, 255, 257, 511, 512, 513, 1023, 1024, 1025};
IndexType k_sizes[] = {31, 39, 63, 64, 65, 95, 96, 127, 129, 255, 257, 511, 512, 513, 1023, 1024, 1025};
for (IndexType i = 0; i < 15; i++) {
for (IndexType j = 0; j < 15; j++) {
for (IndexType k = 0; k < 17; k++) {
test_sycl_contraction<DataLayout, DataType, IndexType>(sycl_device, m_sizes[i], n_sizes[j], k_sizes[k]);
}
}
}
}
template <int DataLayout, typename DataType, typename IndexType, typename Device>
void static test_no_out_of_bounds(const Device &sycl_device, IndexType m_size, IndexType k_size, IndexType n_size) {
typedef typename Tensor<DataType, 1, DataLayout, IndexType>::DimensionPair DimPair;
static const DataType error_threshold = DataType(1e-4);
Tensor<DataType, 2, DataLayout, IndexType> t_left(m_size, k_size);
Tensor<DataType, 2, DataLayout, IndexType> t_right(k_size, n_size);
Tensor<DataType, 2, DataLayout, IndexType> t_result(m_size, n_size);
Eigen::array<DimPair, 1> dims = {{DimPair(1, 0)}};
Eigen::array<IndexType, 2> left_dims = {{m_size, k_size}};
Eigen::array<IndexType, 2> right_dims = {{k_size, n_size}};
Eigen::array<IndexType, 2> result_dims = {{m_size, n_size}};
t_left.setRandom();
t_right.setRandom();
// Allocate buffers twice as big to check for invalid read and write
auto padded_left_size = 2 * t_left.size();
auto padded_right_size = 2 * t_right.size();
auto padded_result_size = 2 * t_result.size();
std::size_t t_left_bytes = padded_left_size * sizeof(DataType);
std::size_t t_right_bytes = padded_right_size * sizeof(DataType);
std::size_t t_result_bytes = padded_result_size * sizeof(DataType);
DataType *d_t_left = static_cast<DataType *>(sycl_device.allocate(t_left_bytes));
DataType *d_t_right = static_cast<DataType *>(sycl_device.allocate(t_right_bytes));
DataType *d_t_result = static_cast<DataType *>(sycl_device.allocate(t_result_bytes));
// TensorMaps are still of the same size than the Tensors
Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_t_left(d_t_left, left_dims);
Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_t_right(d_t_right, right_dims);
Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_t_result(d_t_result, result_dims);
// Write nan after the actual buffer to propagate nans everywhere in case of
// invalid reads
DataType nan = std::numeric_limits<DataType>::quiet_NaN();
auto host_left_data = new DataType[padded_left_size];
std::copy_n(t_left.data(), t_left.size(), host_left_data);
std::fill_n(host_left_data + t_left.size(), t_left.size(), nan);
auto host_right_data = new DataType[padded_right_size];
std::copy_n(t_right.data(), t_right.size(), host_right_data);
std::fill_n(host_right_data + t_right.size(), t_right.size(), nan);
auto host_result_data = new DataType[padded_result_size];
std::fill_n(host_result_data, padded_result_size, nan);
sycl_device.memcpyHostToDevice(d_t_left, host_left_data, t_left_bytes);
sycl_device.memcpyHostToDevice(d_t_right, host_right_data, t_right_bytes);
sycl_device.memcpyHostToDevice(d_t_result, host_result_data, t_result_bytes);
gpu_t_result.device(sycl_device) = gpu_t_left.contract(gpu_t_right, dims);
sycl_device.memcpyDeviceToHost(host_result_data, d_t_result, t_result_bytes);
t_result = t_left.contract(t_right, dims);
for (IndexType i = 0; i < t_result.size(); i++) {
if (static_cast<DataType>(std::fabs(static_cast<DataType>(t_result(i) - host_result_data[i]))) < error_threshold) {
continue;
}
if (Eigen::internal::isApprox(t_result(i), host_result_data[i], error_threshold)) {
continue;
}
if (std::isnan(host_result_data[i])) {
std::cout << "M : " << m_size << ", N : " << n_size << ", K : " << k_size
<< ", invalid read detected at IndexType " << i << ": " << t_result(i) << " vs " << host_result_data[i]
<< std::endl;
} else {
std::cout << "M : " << m_size << ", N : " << n_size << ", K : " << k_size << ", mismatch detected at IndexType "
<< i << ": " << t_result(i) << " vs " << host_result_data[i] << std::endl;
}
VERIFY_IS_APPROX(host_result_data[i], t_result(i));
}
// Make sure that the rest of the result is still nans
for (IndexType i = t_result.size(); i < padded_result_size; i++) {
if (std::isnan(host_result_data[i])) {
continue;
}
std::cout << "M : " << m_size << ", N : " << n_size << ", K : " << k_size
<< ", invalid write detected at IndexType " << i << ": " << host_result_data[i] << std::endl;
VERIFY_IS_APPROX(host_result_data[i], t_result(i));
}
sycl_device.deallocate(d_t_left);
sycl_device.deallocate(d_t_right);
sycl_device.deallocate(d_t_result);
delete[] host_left_data;
delete[] host_right_data;
delete[] host_result_data;
}
template <int DataLayout, typename DataType, typename IndexType, typename Device>
void test_scalar(const Device &sycl_device, IndexType m_size, IndexType k_size, IndexType 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
typedef typename Tensor<DataType, 1, DataLayout, IndexType>::DimensionPair DimPair;
static const DataType error_threshold = DataType(1e-4);
Tensor<DataType, 2, DataLayout, IndexType> t_left(m_size, k_size);
Tensor<DataType, 2, DataLayout, IndexType> t_right(k_size, n_size);
Tensor<DataType, 0, DataLayout, IndexType> t_result;
Tensor<DataType, 0, DataLayout, IndexType> t_result_gpu;
Eigen::array<DimPair, 2> dims = {{DimPair(0, 0), DimPair(1, 1)}};
Eigen::array<IndexType, 2> left_dims = {{m_size, k_size}};
Eigen::array<IndexType, 2> right_dims = {{k_size, n_size}};
t_left.setRandom();
t_right.setRandom();
std::size_t t_left_bytes = t_left.size() * sizeof(DataType);
std::size_t t_right_bytes = t_right.size() * sizeof(DataType);
std::size_t t_result_bytes = sizeof(DataType);
DataType *d_t_left = static_cast<DataType *>(sycl_device.allocate(t_left_bytes));
DataType *d_t_right = static_cast<DataType *>(sycl_device.allocate(t_right_bytes));
DataType *d_t_result = static_cast<DataType *>(sycl_device.allocate(t_result_bytes));
Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_t_left(d_t_left, left_dims);
Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_t_right(d_t_right, right_dims);
Eigen::TensorMap<Eigen::Tensor<DataType, 0, DataLayout, IndexType>> 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);
sycl_device.memcpyDeviceToHost(t_result_gpu.data(), d_t_result, t_result_bytes);
t_result = t_left.contract(t_right, dims);
if (static_cast<DataType>(std::fabs(static_cast<DataType>(t_result() - t_result_gpu()))) > error_threshold &&
!Eigen::internal::isApprox(t_result(), t_result_gpu(), error_threshold)) {
std::cout << "K: " << k_size << ", N: " << n_size << ", M: " << m_size << " : mismatch detected: " << t_result()
<< " vs " << t_result_gpu() << std::endl;
VERIFY_IS_APPROX(t_result_gpu(), t_result());
}
sycl_device.deallocate(d_t_left);
sycl_device.deallocate(d_t_right);
sycl_device.deallocate(d_t_result);
}
template <int DataLayout, typename DataType, typename IndexType, typename Device>
void contraction_batch(const Device &sycl_device, IndexType m_size, IndexType k_size, IndexType n_size,
IndexType m_batch, IndexType start, IndexType limit) {
typedef typename Tensor<DataType, 1, DataLayout, IndexType>::DimensionPair DimPair;
static const DataType error_threshold = DataType(1e-4);
typedef Eigen::array<IndexType, 3> TensorDim;
typedef Eigen::Tensor<DataType, 3, DataLayout, IndexType> TensorType;
TensorDim left_dims = {{m_batch, k_size, m_size}};
TensorDim right_dims = {{m_batch, n_size, k_size}};
TensorDim res_dims = {{m_batch, m_size, n_size}};
Eigen::array<DimPair, 1> contract_pairs = {{DimPair(0, 1)}};
TensorType t_left(left_dims);
TensorType t_right(right_dims);
TensorType t_result_gpu(res_dims);
TensorType t_result(res_dims);
t_left.setRandom();
t_right.setRandom();
std::size_t t_left_bytes = t_left.size() * sizeof(DataType);
std::size_t t_right_bytes = t_right.size() * sizeof(DataType);
std::size_t t_result_bytes = t_result.size() * sizeof(DataType);
DataType *d_t_left = static_cast<DataType *>(sycl_device.allocate(t_left_bytes));
DataType *d_t_right = static_cast<DataType *>(sycl_device.allocate(t_right_bytes));
DataType *d_t_result = static_cast<DataType *>(sycl_device.allocate(t_result_bytes));
Eigen::TensorMap<TensorType> gpu_t_left(d_t_left, left_dims);
Eigen::TensorMap<TensorType> gpu_t_right(d_t_right, right_dims);
Eigen::TensorMap<TensorType> gpu_t_result(d_t_result, res_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);
for (int i = start; i < limit; ++i) {
auto x = gpu_t_left.template chip<0>(i);
auto y = gpu_t_right.template chip<0>(i);
auto z = gpu_t_result.template chip<0>(i);
z.device(sycl_device) = x.contract(y, contract_pairs);
}
sycl_device.memcpyDeviceToHost(t_result_gpu.data(), d_t_result, t_result_bytes);
for (int i = start; i < limit; ++i) {
auto x = t_left.template chip<0>(i);
auto y = t_right.template chip<0>(i);
auto z = t_result.template chip<0>(i);
z = x.contract(y, contract_pairs);
}
for (IndexType i = 0; i < t_result.size(); i++) {
if (static_cast<DataType>(std::fabs(static_cast<DataType>(t_result(i) - t_result_gpu(i)))) < error_threshold) {
continue;
}
if (Eigen::internal::isApprox(t_result(i), t_result_gpu(i), error_threshold)) {
continue;
}
std::cout << "mismatch detected at IndexType " << i << ": " << t_result(i) << " vs " << t_result_gpu(i)
<< std::endl;
VERIFY_IS_APPROX(t_result_gpu(i), t_result(i));
}
sycl_device.deallocate(d_t_left);
sycl_device.deallocate(d_t_right);
sycl_device.deallocate(d_t_result);
}
template <int DataLayout, typename DataType, typename IndexType, typename Device>
void contraction_rhs_transposed(const Device &sycl_device, IndexType m_size, IndexType k_size, IndexType n_size) {
typedef typename Tensor<DataType, 1, DataLayout, IndexType>::DimensionPair DimPair;
static const DataType error_threshold = DataType(1e-4);
Eigen::array<IndexType, 2> left_dims = {{m_size, k_size}};
Eigen::array<IndexType, 2> right_dims = {{n_size, k_size}};
Eigen::array<IndexType, 2> res_dims = {{m_size, n_size}};
Eigen::array<DimPair, 1> dims = {{DimPair(1, 1)}};
Tensor<DataType, 2, DataLayout, IndexType> t_left(left_dims);
Tensor<DataType, 2, DataLayout, IndexType> t_right(right_dims);
Tensor<DataType, 2, DataLayout, IndexType> t_result_gpu(res_dims);
Tensor<DataType, 2, DataLayout, IndexType> t_result(res_dims);
t_left.setRandom();
t_right.setRandom();
std::size_t t_left_bytes = t_left.size() * sizeof(DataType);
std::size_t t_right_bytes = t_right.size() * sizeof(DataType);
std::size_t t_result_bytes = t_result.size() * sizeof(DataType);
DataType *d_t_left = static_cast<DataType *>(sycl_device.allocate(t_left_bytes));
DataType *d_t_right = static_cast<DataType *>(sycl_device.allocate(t_right_bytes));
DataType *d_t_result = static_cast<DataType *>(sycl_device.allocate(t_result_bytes));
Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_t_left(d_t_left, left_dims);
Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_t_right(d_t_right, right_dims);
Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_t_result(d_t_result, res_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);
sycl_device.memcpyDeviceToHost(t_result_gpu.data(), d_t_result, t_result_bytes);
t_result = t_left.contract(t_right, dims);
for (IndexType j = 0; j < m_size; j++) {
for (IndexType i = 0; i < n_size; i++) {
if (static_cast<DataType>(std::fabs(static_cast<DataType>(t_result(j, i) - t_result_gpu(j, i)))) <
error_threshold) {
continue;
}
if (Eigen::internal::isApprox(t_result(j, i), t_result_gpu(j, i), error_threshold)) {
continue;
}
std::cout << "M : " << m_size << ", N : " << n_size << ", K : " << k_size
<< ", mismatch detected at IndexType m: " << j << " n: " << i << " CPU : " << t_result(j, i)
<< " vs SYCL:" << t_result_gpu(j, i) << std::endl;
VERIFY_IS_APPROX(t_result_gpu(j, i), t_result(j, i));
}
}
sycl_device.deallocate(d_t_left);
sycl_device.deallocate(d_t_right);
sycl_device.deallocate(d_t_result);
}
template <int DataLayout, typename DataType, typename IndexType, typename Device>
void contraction_lhs_transposed(const Device &sycl_device, IndexType m_size, IndexType k_size, IndexType n_size) {
typedef typename Tensor<DataType, 1, DataLayout, IndexType>::DimensionPair DimPair;
static const DataType error_threshold = DataType(1e-4);
Eigen::array<IndexType, 2> left_dims = {{k_size, m_size}};
Eigen::array<IndexType, 2> right_dims = {{k_size, n_size}};
Eigen::array<IndexType, 2> res_dims = {{m_size, n_size}};
Eigen::array<DimPair, 1> dims = {{DimPair(0, 0)}};
Tensor<DataType, 2, DataLayout, IndexType> t_left(left_dims);
Tensor<DataType, 2, DataLayout, IndexType> t_right(right_dims);
Tensor<DataType, 2, DataLayout, IndexType> t_result_gpu(res_dims);
Tensor<DataType, 2, DataLayout, IndexType> t_result(res_dims);
t_left.setRandom();
t_right.setRandom();
std::size_t t_left_bytes = t_left.size() * sizeof(DataType);
std::size_t t_right_bytes = t_right.size() * sizeof(DataType);
std::size_t t_result_bytes = t_result.size() * sizeof(DataType);
DataType *d_t_left = static_cast<DataType *>(sycl_device.allocate(t_left_bytes));
DataType *d_t_right = static_cast<DataType *>(sycl_device.allocate(t_right_bytes));
DataType *d_t_result = static_cast<DataType *>(sycl_device.allocate(t_result_bytes));
Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_t_left(d_t_left, left_dims);
Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_t_right(d_t_right, right_dims);
Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_t_result(d_t_result, res_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);
sycl_device.memcpyDeviceToHost(t_result_gpu.data(), d_t_result, t_result_bytes);
t_result = t_left.contract(t_right, dims);
for (IndexType i = 0; i < t_result.size(); i++) {
if (static_cast<DataType>(std::fabs(static_cast<DataType>(t_result(i) - t_result_gpu(i)))) < error_threshold) {
continue;
}
if (Eigen::internal::isApprox(t_result(i), t_result_gpu(i), error_threshold)) {
continue;
}
std::cout << "M : " << m_size << ", N : " << n_size << ", K : " << k_size << ", mismatch detected at IndexType "
<< i << ": " << t_result(i) << " vs " << t_result_gpu(i) << std::endl;
VERIFY_IS_APPROX(t_result_gpu(i), t_result(i));
}
sycl_device.deallocate(d_t_left);
sycl_device.deallocate(d_t_right);
sycl_device.deallocate(d_t_result);
}
template <int DataLayout, typename DataType, typename IndexType, typename Device>
void contraction_both_transposed(const Device &sycl_device, IndexType m_size, IndexType k_size, IndexType n_size) {
typedef typename Tensor<DataType, 1, DataLayout, IndexType>::DimensionPair DimPair;
static const DataType error_threshold = DataType(1e-4);
Eigen::array<IndexType, 2> left_dims = {{k_size, m_size}};
Eigen::array<IndexType, 2> right_dims = {{n_size, k_size}};
Eigen::array<IndexType, 2> res_dims = {{m_size, n_size}};
Eigen::array<DimPair, 1> dims = {{DimPair(0, 1)}};
Tensor<DataType, 2, DataLayout, IndexType> t_left(left_dims);
Tensor<DataType, 2, DataLayout, IndexType> t_right(right_dims);
Tensor<DataType, 2, DataLayout, IndexType> t_result_gpu(res_dims);
Tensor<DataType, 2, DataLayout, IndexType> t_result(res_dims);
t_left.setRandom();
t_right.setRandom();
std::size_t t_left_bytes = t_left.size() * sizeof(DataType);
std::size_t t_right_bytes = t_right.size() * sizeof(DataType);
std::size_t t_result_bytes = t_result.size() * sizeof(DataType);
DataType *d_t_left = static_cast<DataType *>(sycl_device.allocate(t_left_bytes));
DataType *d_t_right = static_cast<DataType *>(sycl_device.allocate(t_right_bytes));
DataType *d_t_result = static_cast<DataType *>(sycl_device.allocate(t_result_bytes));
Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_t_left(d_t_left, left_dims);
Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_t_right(d_t_right, right_dims);
Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_t_result(d_t_result, res_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);
sycl_device.memcpyDeviceToHost(t_result_gpu.data(), d_t_result, t_result_bytes);
t_result = t_left.contract(t_right, dims);
for (IndexType i = 0; i < t_result.size(); i++) {
if (static_cast<DataType>(std::fabs(static_cast<DataType>(t_result(i) - t_result_gpu(i)))) < error_threshold) {
continue;
}
if (Eigen::internal::isApprox(t_result(i), t_result_gpu(i), error_threshold)) {
continue;
}
std::cout << "M : " << m_size << ", N : " << n_size << ", K : " << k_size << ", mismatch detected at IndexType "
<< i << ": " << t_result(i) << " vs " << t_result_gpu(i) << std::endl;
VERIFY_IS_APPROX(t_result_gpu(i), t_result(i));
}
sycl_device.deallocate(d_t_left);
sycl_device.deallocate(d_t_right);
sycl_device.deallocate(d_t_result);
}
template <typename Dev>
void inline tensorOutofBound(const Dev &sycl_device) {
typedef float DataType;
typedef int64_t IndexType;
std::chrono::time_point<std::chrono::system_clock> start, end;
start = std::chrono::system_clock::now();
// Test out of bound for Tensor-Tensor
test_no_out_of_bounds<RowMajor, DataType, IndexType>(sycl_device, 10, 1024, 1024);
test_no_out_of_bounds<RowMajor, DataType, IndexType>(sycl_device, 1024, 1024, 4096);
test_no_out_of_bounds<RowMajor, DataType, IndexType>(sycl_device, 4096, 1024, 2048);
test_no_out_of_bounds<ColMajor, DataType, IndexType>(sycl_device, 784, 2048, 1024);
test_no_out_of_bounds<ColMajor, DataType, IndexType>(sycl_device, 2048, 1024, 784);
test_no_out_of_bounds<RowMajor, DataType, IndexType>(sycl_device, 10, 1024, 10);
test_no_out_of_bounds<RowMajor, DataType, IndexType>(sycl_device, 513, 4096, 513);
test_no_out_of_bounds<RowMajor, DataType, IndexType>(sycl_device, 783, 1024, 783);
test_no_out_of_bounds<ColMajor, DataType, IndexType>(sycl_device, 784, 2048, 784);
test_no_out_of_bounds<ColMajor, DataType, IndexType>(sycl_device, 11, 1024, 11);
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 << "tensor out of bound tests finished computation at " << std::ctime(&end_time)
<< "elapsed time: " << elapsed_seconds.count() << "s\n";
}
template <typename Dev>
void inline tensorTensor(const Dev &sycl_device) {
typedef float DataType;
typedef int64_t IndexType;
std::chrono::time_point<std::chrono::system_clock> start, end;
start = std::chrono::system_clock::now();
// Tensor Tensor Contraction
test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 128, 128, 128);
test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 128, 128, 128);
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 << "tensor tensor tests finished computation at " << std::ctime(&end_time)
<< "elapsed time: " << elapsed_seconds.count() << "s\n";
}
template <typename Dev>
void inline tensorTensor_m(const Dev &sycl_device) {
typedef float DataType;
typedef int64_t IndexType;
std::chrono::time_point<std::chrono::system_clock> start, end;
start = std::chrono::system_clock::now();
// Tensor Tensor Contraction
test_sycl_contraction_m<ColMajor, DataType, IndexType>(sycl_device);
test_sycl_contraction_m<RowMajor, DataType, IndexType>(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 << "tensor tensor tests finished computation at " << std::ctime(&end_time)
<< "elapsed time: " << elapsed_seconds.count() << "s\n";
}
template <typename Dev>
void inline tensorTensor_n(const Dev &sycl_device) {
typedef float DataType;
typedef int64_t IndexType;
std::chrono::time_point<std::chrono::system_clock> start, end;
start = std::chrono::system_clock::now();
// Tensor Tensor Contraction
test_sycl_contraction_n<ColMajor, DataType, IndexType>(sycl_device);
test_sycl_contraction_n<RowMajor, DataType, IndexType>(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 << "tensor tensor tests finished computation at " << std::ctime(&end_time)
<< "elapsed time: " << elapsed_seconds.count() << "s\n";
}
template <typename Dev>
void inline tensorTensor_k(const Dev &sycl_device) {
typedef float DataType;
typedef int64_t IndexType;
std::chrono::time_point<std::chrono::system_clock> start, end;
start = std::chrono::system_clock::now();
test_sycl_contraction_k<ColMajor, DataType, IndexType>(sycl_device);
test_sycl_contraction_k<RowMajor, DataType, IndexType>(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 << "tensor tensor tests finished computation at " << std::ctime(&end_time)
<< "elapsed time: " << elapsed_seconds.count() << "s\n";
}
template <typename Dev>
void inline tensorTensor_sizes(const Dev &sycl_device) {
typedef float DataType;
typedef int64_t IndexType;
std::chrono::time_point<std::chrono::system_clock> start, end;
start = std::chrono::system_clock::now();
// Tensor Tensor Contraction
test_sycl_contraction_sizes<ColMajor, DataType, IndexType>(sycl_device);
test_sycl_contraction_sizes<RowMajor, DataType, IndexType>(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 << "tensor tensor tests finished computation at " << std::ctime(&end_time)
<< "elapsed time: " << elapsed_seconds.count() << "s\n";
}
template <typename Dev>
void inline vectorVector(const Dev &sycl_device) {
typedef float DataType;
typedef int64_t IndexType;
std::chrono::time_point<std::chrono::system_clock> start, end;
start = std::chrono::system_clock::now();
// VECTOR-VECTOR
test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 1025, 1, 1025);
test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 1025, 1, 1025);
test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 1024, 1, 1024);
test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 1024, 1, 1024);
test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 1023, 1, 1023);
test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 1023, 1, 1023);
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 << "contracted tensor tests finished computation at " << std::ctime(&end_time)
<< "elapsed time: " << elapsed_seconds.count() << "s\n";
}
template <typename Dev>
void inline vectorTensor(const Dev &sycl_device) {
typedef float DataType;
typedef int64_t IndexType;
std::chrono::time_point<std::chrono::system_clock> start, end;
start = std::chrono::system_clock::now();
// Vector-Tensor
test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 1, 1025, 1025);
test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 1, 1025, 1025);
test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 1, 1024, 1024);
test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 1, 1024, 1024);
test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 1, 1023, 1023);
test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 1, 1023, 1023);
test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 1, 4097, 4097);
test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 1, 4097, 4097);
test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 1, 4096, 4096);
test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 1, 4096, 4096);
test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 1, 4095, 4095);
test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 1, 4095, 4095);
test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 1, 802816, 32);
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";
}
template <typename Dev>
void inline tensorVector(const Dev &sycl_device) {
typedef float DataType;
typedef int64_t IndexType;
std::chrono::time_point<std::chrono::system_clock> start, end;
start = std::chrono::system_clock::now();
// Matrix-Vector
test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 1025, 1025, 1);
test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 1125, 1025, 1);
test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 1224, 1024, 1);
test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 1024, 1024, 1);
test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 1023, 1023, 1);
test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 1023, 1023, 1);
test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 4097, 4197, 1);
test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 4097, 4097, 1);
test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 4096, 4096, 1);
test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 4096, 8196, 1);
test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 4095, 4095, 1);
test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 4095, 4095, 1);
// If the GEMV disabled it will creates one kernel to calculate the contraction.
// Therefore the acumuation of float number will overflow the precision
// threshold for float and cause the test to fail. While it the GMV multiple
// kernel will be created and each one run the overflow of accumutation breaks
// among the kernels.
#ifndef EIGEN_SYCL_DISABLE_GEMV
test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 32, 802032, 1);
#endif
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";
}
template <typename Dev>
void inline tensorScalar(const Dev &sycl_device) {
typedef float DataType;
typedef int64_t IndexType;
std::chrono::time_point<std::chrono::system_clock> start, end;
start = std::chrono::system_clock::now();
// SCALAR Contraction
test_scalar<ColMajor, DataType, IndexType>(sycl_device, 127, 127, 127);
test_scalar<RowMajor, DataType, IndexType>(sycl_device, 127, 127, 127);
test_scalar<ColMajor, DataType, IndexType>(sycl_device, 128, 128, 128);
test_scalar<RowMajor, DataType, IndexType>(sycl_device, 128, 128, 128);
test_scalar<ColMajor, DataType, IndexType>(sycl_device, 129, 129, 129);
test_scalar<RowMajor, DataType, IndexType>(sycl_device, 129, 129, 129);
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";
}
template <typename Dev>
void inline skinnyTensor_row(const Dev &sycl_device) {
typedef float DataType;
typedef int64_t IndexType;
std::chrono::time_point<std::chrono::system_clock> start, end;
start = std::chrono::system_clock::now();
// Tensor Tensor Contraction
test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 16, 4, 16);
test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 257, 131073, 257);
test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 256, 131072, 256);
test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 16, 131073, 16);
test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 17, 131072, 17);
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";
}
template <typename Dev>
void inline skinnyTensor_col(const Dev &sycl_device) {
typedef float DataType;
typedef int64_t IndexType;
std::chrono::time_point<std::chrono::system_clock> start, end;
start = std::chrono::system_clock::now();
// Tensor Tensor Contraction
test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 16, 4, 16);
test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 257, 131073, 257);
test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 256, 131072, 256);
test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 16, 131073, 16);
test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 17, 131072, 17);
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";
}
template <typename Dev>
void inline tensor_contraction_batch_per_device(const Dev &sycl_device) {
typedef float DataType;
typedef int64_t IndexType;
std::chrono::time_point<std::chrono::system_clock> start, end;
start = std::chrono::system_clock::now();
contraction_batch<RowMajor, DataType, IndexType>(sycl_device, 64, 75, 30, 4, 0, 4);
contraction_batch<ColMajor, DataType, IndexType>(sycl_device, 64, 75, 30, 4, 0, 4);
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";
}
template <typename Dev>
void inline tensor_contraction_lhs_transposed_per_device(const Dev &sycl_device) {
typedef float DataType;
typedef int64_t IndexType;
std::chrono::time_point<std::chrono::system_clock> start, end;
start = std::chrono::system_clock::now();
contraction_lhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 8, 4, 8);
contraction_lhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 32, 8, 32);
contraction_lhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 64, 16, 64);
contraction_lhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 784, 2048, 1024);
contraction_lhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 1024, 10, 1024);
contraction_lhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 4096, 1024, 1024);
contraction_lhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 2048, 4096, 1024);
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";
}
template <typename Dev>
void inline tensor_contraction_rhs_transposed_per_device(const Dev &sycl_device) {
typedef float DataType;
typedef int64_t IndexType;
std::chrono::time_point<std::chrono::system_clock> start, end;
start = std::chrono::system_clock::now();
contraction_rhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 16, 4, 16);
contraction_rhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 17, 5, 17);
contraction_rhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 32, 8, 32);
contraction_rhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 64, 16, 64);
contraction_rhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 10, 1024, 1024);
contraction_rhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 1024, 1024, 4096);
contraction_rhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 4096, 1024, 2048);
contraction_rhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 2048, 1024, 784);
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";
}
template <typename Dev>
void inline tensor_contraction_both_transposed_per_device(const Dev &sycl_device) {
typedef float DataType;
typedef int64_t IndexType;
std::chrono::time_point<std::chrono::system_clock> start, end;
start = std::chrono::system_clock::now();
contraction_both_transposed<RowMajor, DataType, IndexType>(sycl_device, 17, 5, 17);
contraction_both_transposed<RowMajor, DataType, IndexType>(sycl_device, 32, 8, 32);
contraction_both_transposed<RowMajor, DataType, IndexType>(sycl_device, 64, 16, 64);
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";
}
EIGEN_DECLARE_TEST(cxx11_tensor_contract_sycl) {
for (const auto &device : Eigen::get_sycl_supported_devices()) {
std::cout << "Running on " << device.template get_info<cl::sycl::info::device::name>() << std::endl;
QueueInterface queueInterface(device);
auto sycl_device = Eigen::SyclDevice(&queueInterface);
CALL_SUBTEST_1(tensorOutofBound(sycl_device));
CALL_SUBTEST_2(tensorTensor(sycl_device));
CALL_SUBTEST_2(tensorTensor_m(sycl_device));
CALL_SUBTEST_2(tensorTensor_n(sycl_device));
CALL_SUBTEST_2(tensorTensor_k(sycl_device));
CALL_SUBTEST_2(tensorTensor_sizes(sycl_device));
CALL_SUBTEST_3(vectorVector(sycl_device));
CALL_SUBTEST_4(vectorTensor(sycl_device));
CALL_SUBTEST_5(tensorVector(sycl_device));
CALL_SUBTEST_6(tensorScalar(sycl_device));
CALL_SUBTEST_7(skinnyTensor_row(sycl_device));
CALL_SUBTEST_7(skinnyTensor_col(sycl_device));
CALL_SUBTEST_8(tensor_contraction_batch_per_device(sycl_device));
CALL_SUBTEST_9(tensor_contraction_lhs_transposed_per_device(sycl_device));
CALL_SUBTEST_10(tensor_contraction_rhs_transposed_per_device(sycl_device));
CALL_SUBTEST_11(tensor_contraction_both_transposed_per_device(sycl_device));
}
}