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eigen/unsupported/test/cxx11_tensor_sycl.cpp
Mehdi Goli 00f32752f7 [SYCL] Rebasing the SYCL support branch on top of the Einge upstream master branch. 
* Unifying all loadLocalTile from lhs and rhs to an extract_block function.
* Adding get_tensor operation which was missing in TensorContractionMapper.
* Adding the -D method missing from cmake for Disable_Skinny Contraction operation.
* Wrapping all the indices in TensorScanSycl into Scan parameter struct.
* Fixing typo in Device SYCL
* Unifying load to private register for tall/skinny no shared
* Unifying load to vector tile for tensor-vector/vector-tensor operation
* Removing all the LHS/RHS class for extracting data from global
* Removing Outputfunction from TensorContractionSkinnyNoshared.
* Combining the local memory version of tall/skinny and normal tensor contraction into one kernel.
* Combining the no-local memory version of tall/skinny and normal tensor contraction into one kernel.
* Combining General Tensor-Vector and VectorTensor contraction into one kernel.
* Making double buffering optional for Tensor contraction when local memory is version is used.
* Modifying benchmark to accept custom Reduction Sizes
* Disabling AVX optimization for SYCL backend on the host to allow SSE optimization to the host
* Adding Test for SYCL
* Modifying SYCL CMake
2019-11-28 10:08:54 +00:00

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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>
// 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 int64_t
#define EIGEN_USE_SYCL
#include "main.h"
#include <unsupported/Eigen/CXX11/Tensor>
using Eigen::array;
using Eigen::SyclDevice;
using Eigen::Tensor;
using Eigen::TensorMap;
template <typename DataType, int DataLayout, typename IndexType>
void test_sycl_mem_transfers(const Eigen::SyclDevice &sycl_device) {
IndexType sizeDim1 = 5;
IndexType sizeDim2 = 5;
IndexType sizeDim3 = 1;
array<IndexType, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}};
Tensor<DataType, 3, DataLayout, IndexType> in1(tensorRange);
Tensor<DataType, 3, DataLayout, IndexType> out1(tensorRange);
Tensor<DataType, 3, DataLayout, IndexType> out2(tensorRange);
Tensor<DataType, 3, DataLayout, IndexType> out3(tensorRange);
in1 = in1.random();
DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(in1.size()*sizeof(DataType)));
DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(out1.size()*sizeof(DataType)));
TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu1(gpu_data1, tensorRange);
TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu2(gpu_data2, tensorRange);
sycl_device.memcpyHostToDevice(gpu_data1, in1.data(),(in1.size())*sizeof(DataType));
sycl_device.memcpyHostToDevice(gpu_data2, in1.data(),(in1.size())*sizeof(DataType));
gpu1.device(sycl_device) = gpu1 * 3.14f;
gpu2.device(sycl_device) = gpu2 * 2.7f;
sycl_device.memcpyDeviceToHost(out1.data(), gpu_data1,(out1.size())*sizeof(DataType));
sycl_device.memcpyDeviceToHost(out2.data(), gpu_data1,(out2.size())*sizeof(DataType));
sycl_device.memcpyDeviceToHost(out3.data(), gpu_data2,(out3.size())*sizeof(DataType));
sycl_device.synchronize();
for (IndexType i = 0; i < in1.size(); ++i) {
// std::cout << "SYCL DATA : " << out1(i) << " vs CPU DATA : " << in1(i) * 3.14f << "\n";
VERIFY_IS_APPROX(out1(i), in1(i) * 3.14f);
VERIFY_IS_APPROX(out2(i), in1(i) * 3.14f);
VERIFY_IS_APPROX(out3(i), in1(i) * 2.7f);
}
sycl_device.deallocate(gpu_data1);
sycl_device.deallocate(gpu_data2);
}
template <typename DataType, int DataLayout, typename IndexType>
void test_sycl_mem_sync(const Eigen::SyclDevice &sycl_device) {
IndexType size = 20;
array<IndexType, 1> tensorRange = {{size}};
Tensor<DataType, 1, DataLayout, IndexType> in1(tensorRange);
Tensor<DataType, 1, DataLayout, IndexType> in2(tensorRange);
Tensor<DataType, 1, DataLayout, IndexType> out(tensorRange);
in1 = in1.random();
in2 = in1;
DataType* gpu_data = static_cast<DataType*>(sycl_device.allocate(in1.size()*sizeof(DataType)));
TensorMap<Tensor<DataType, 1, DataLayout, IndexType>> gpu1(gpu_data, tensorRange);
sycl_device.memcpyHostToDevice(gpu_data, in1.data(),(in1.size())*sizeof(DataType));
sycl_device.synchronize();
in1.setZero();
sycl_device.memcpyDeviceToHost(out.data(), gpu_data, out.size()*sizeof(DataType));
sycl_device.synchronize();
for (IndexType i = 0; i < in1.size(); ++i) {
VERIFY_IS_APPROX(out(i), in2(i));
}
sycl_device.deallocate(gpu_data);
}
template <typename DataType, int DataLayout, typename IndexType>
void test_sycl_mem_sync_offsets(const Eigen::SyclDevice &sycl_device) {
using tensor_type = Tensor<DataType, 1, DataLayout, IndexType>;
IndexType full_size = 32;
IndexType half_size = full_size / 2;
array<IndexType, 1> tensorRange = {{full_size}};
tensor_type in1(tensorRange);
tensor_type out(tensorRange);
DataType* gpu_data = static_cast<DataType*>(sycl_device.allocate(full_size * sizeof(DataType)));
TensorMap<tensor_type> gpu1(gpu_data, tensorRange);
in1 = in1.random();
// Copy all data to device, then permute on copy back to host
sycl_device.memcpyHostToDevice(gpu_data, in1.data(), full_size * sizeof(DataType));
sycl_device.memcpyDeviceToHost(out.data(), gpu_data + half_size, half_size * sizeof(DataType));
sycl_device.memcpyDeviceToHost(out.data() + half_size, gpu_data, half_size * sizeof(DataType));
for (IndexType i = 0; i < half_size; ++i) {
VERIFY_IS_APPROX(out(i), in1(i + half_size));
VERIFY_IS_APPROX(out(i + half_size), in1(i));
}
in1 = in1.random();
out.setZero();
// Permute copies to device, then copy all back to host
sycl_device.memcpyHostToDevice(gpu_data + half_size, in1.data(), half_size * sizeof(DataType));
sycl_device.memcpyHostToDevice(gpu_data, in1.data() + half_size, half_size * sizeof(DataType));
sycl_device.memcpyDeviceToHost(out.data(), gpu_data, full_size * sizeof(DataType));
for (IndexType i = 0; i < half_size; ++i) {
VERIFY_IS_APPROX(out(i), in1(i + half_size));
VERIFY_IS_APPROX(out(i + half_size), in1(i));
}
in1 = in1.random();
out.setZero();
DataType* gpu_data_out = static_cast<DataType*>(sycl_device.allocate(full_size * sizeof(DataType)));
TensorMap<tensor_type> gpu2(gpu_data_out, tensorRange);
// Copy all to device, permute copies on device, then copy all back to host
sycl_device.memcpyHostToDevice(gpu_data, in1.data(), full_size * sizeof(DataType));
sycl_device.memcpy(gpu_data_out + half_size, gpu_data, half_size * sizeof(DataType));
sycl_device.memcpy(gpu_data_out, gpu_data + half_size, half_size * sizeof(DataType));
sycl_device.memcpyDeviceToHost(out.data(), gpu_data_out, full_size * sizeof(DataType));
for (IndexType i = 0; i < half_size; ++i) {
VERIFY_IS_APPROX(out(i), in1(i + half_size));
VERIFY_IS_APPROX(out(i + half_size), in1(i));
}
sycl_device.deallocate(gpu_data_out);
sycl_device.deallocate(gpu_data);
}
template <typename DataType, int DataLayout, typename IndexType>
void test_sycl_memset_offsets(const Eigen::SyclDevice &sycl_device) {
using tensor_type = Tensor<DataType, 1, DataLayout, IndexType>;
IndexType full_size = 32;
IndexType half_size = full_size / 2;
array<IndexType, 1> tensorRange = {{full_size}};
tensor_type cpu_out(tensorRange);
tensor_type out(tensorRange);
cpu_out.setZero();
std::memset(cpu_out.data(), 0, half_size * sizeof(DataType));
std::memset(cpu_out.data() + half_size, 1, half_size * sizeof(DataType));
DataType* gpu_data = static_cast<DataType*>(sycl_device.allocate(full_size * sizeof(DataType)));
TensorMap<tensor_type> gpu1(gpu_data, tensorRange);
sycl_device.memset(gpu_data, 0, half_size * sizeof(DataType));
sycl_device.memset(gpu_data + half_size, 1, half_size * sizeof(DataType));
sycl_device.memcpyDeviceToHost(out.data(), gpu_data, full_size * sizeof(DataType));
for (IndexType i = 0; i < full_size; ++i) {
VERIFY_IS_APPROX(out(i), cpu_out(i));
}
sycl_device.deallocate(gpu_data);
}
template <typename DataType, int DataLayout, typename IndexType>
void test_sycl_computations(const Eigen::SyclDevice &sycl_device) {
IndexType sizeDim1 = 100;
IndexType sizeDim2 = 10;
IndexType sizeDim3 = 20;
array<IndexType, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}};
Tensor<DataType, 3,DataLayout, IndexType> in1(tensorRange);
Tensor<DataType, 3,DataLayout, IndexType> in2(tensorRange);
Tensor<DataType, 3,DataLayout, IndexType> in3(tensorRange);
Tensor<DataType, 3,DataLayout, IndexType> out(tensorRange);
in2 = in2.random();
in3 = in3.random();
DataType * gpu_in1_data = static_cast<DataType*>(sycl_device.allocate(in1.size()*sizeof(DataType)));
DataType * gpu_in2_data = static_cast<DataType*>(sycl_device.allocate(in2.size()*sizeof(DataType)));
DataType * gpu_in3_data = static_cast<DataType*>(sycl_device.allocate(in3.size()*sizeof(DataType)));
DataType * gpu_out_data = static_cast<DataType*>(sycl_device.allocate(out.size()*sizeof(DataType)));
TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu_in1(gpu_in1_data, tensorRange);
TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu_in2(gpu_in2_data, tensorRange);
TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu_in3(gpu_in3_data, tensorRange);
TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu_out(gpu_out_data, tensorRange);
/// a=1.2f
gpu_in1.device(sycl_device) = gpu_in1.constant(1.2f);
sycl_device.memcpyDeviceToHost(in1.data(), gpu_in1_data ,(in1.size())*sizeof(DataType));
sycl_device.synchronize();
for (IndexType i = 0; i < sizeDim1; ++i) {
for (IndexType j = 0; j < sizeDim2; ++j) {
for (IndexType k = 0; k < sizeDim3; ++k) {
VERIFY_IS_APPROX(in1(i,j,k), 1.2f);
}
}
}
printf("a=1.2f Test passed\n");
/// a=b*1.2f
gpu_out.device(sycl_device) = gpu_in1 * 1.2f;
sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data ,(out.size())*sizeof(DataType));
sycl_device.synchronize();
for (IndexType i = 0; i < sizeDim1; ++i) {
for (IndexType j = 0; j < sizeDim2; ++j) {
for (IndexType k = 0; k < sizeDim3; ++k) {
VERIFY_IS_APPROX(out(i,j,k),
in1(i,j,k) * 1.2f);
}
}
}
printf("a=b*1.2f Test Passed\n");
/// c=a*b
sycl_device.memcpyHostToDevice(gpu_in2_data, in2.data(),(in2.size())*sizeof(DataType));
gpu_out.device(sycl_device) = gpu_in1 * gpu_in2;
sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.size())*sizeof(DataType));
sycl_device.synchronize();
for (IndexType i = 0; i < sizeDim1; ++i) {
for (IndexType j = 0; j < sizeDim2; ++j) {
for (IndexType k = 0; k < sizeDim3; ++k) {
VERIFY_IS_APPROX(out(i,j,k),
in1(i,j,k) *
in2(i,j,k));
}
}
}
printf("c=a*b Test Passed\n");
/// c=a+b
gpu_out.device(sycl_device) = gpu_in1 + gpu_in2;
sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.size())*sizeof(DataType));
sycl_device.synchronize();
for (IndexType i = 0; i < sizeDim1; ++i) {
for (IndexType j = 0; j < sizeDim2; ++j) {
for (IndexType k = 0; k < sizeDim3; ++k) {
VERIFY_IS_APPROX(out(i,j,k),
in1(i,j,k) +
in2(i,j,k));
}
}
}
printf("c=a+b Test Passed\n");
/// c=a*a
gpu_out.device(sycl_device) = gpu_in1 * gpu_in1;
sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.size())*sizeof(DataType));
sycl_device.synchronize();
for (IndexType i = 0; i < sizeDim1; ++i) {
for (IndexType j = 0; j < sizeDim2; ++j) {
for (IndexType k = 0; k < sizeDim3; ++k) {
VERIFY_IS_APPROX(out(i,j,k),
in1(i,j,k) *
in1(i,j,k));
}
}
}
printf("c= a*a Test Passed\n");
//a*3.14f + b*2.7f
gpu_out.device(sycl_device) = gpu_in1 * gpu_in1.constant(3.14f) + gpu_in2 * gpu_in2.constant(2.7f);
sycl_device.memcpyDeviceToHost(out.data(),gpu_out_data,(out.size())*sizeof(DataType));
sycl_device.synchronize();
for (IndexType i = 0; i < sizeDim1; ++i) {
for (IndexType j = 0; j < sizeDim2; ++j) {
for (IndexType k = 0; k < sizeDim3; ++k) {
VERIFY_IS_APPROX(out(i,j,k),
in1(i,j,k) * 3.14f
+ in2(i,j,k) * 2.7f);
}
}
}
printf("a*3.14f + b*2.7f Test Passed\n");
///d= (a>0.5? b:c)
sycl_device.memcpyHostToDevice(gpu_in3_data, in3.data(),(in3.size())*sizeof(DataType));
gpu_out.device(sycl_device) =(gpu_in1 > gpu_in1.constant(0.5f)).select(gpu_in2, gpu_in3);
sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.size())*sizeof(DataType));
sycl_device.synchronize();
for (IndexType i = 0; i < sizeDim1; ++i) {
for (IndexType j = 0; j < sizeDim2; ++j) {
for (IndexType k = 0; k < sizeDim3; ++k) {
VERIFY_IS_APPROX(out(i, j, k), (in1(i, j, k) > 0.5f)
? in2(i, j, k)
: in3(i, j, k));
}
}
}
printf("d= (a>0.5? b:c) Test Passed\n");
sycl_device.deallocate(gpu_in1_data);
sycl_device.deallocate(gpu_in2_data);
sycl_device.deallocate(gpu_in3_data);
sycl_device.deallocate(gpu_out_data);
}
template<typename Scalar1, typename Scalar2, int DataLayout, typename IndexType>
static void test_sycl_cast(const Eigen::SyclDevice& sycl_device){
IndexType size = 20;
array<IndexType, 1> tensorRange = {{size}};
Tensor<Scalar1, 1, DataLayout, IndexType> in(tensorRange);
Tensor<Scalar2, 1, DataLayout, IndexType> out(tensorRange);
Tensor<Scalar2, 1, DataLayout, IndexType> out_host(tensorRange);
in = in.random();
Scalar1* gpu_in_data = static_cast<Scalar1*>(sycl_device.allocate(in.size()*sizeof(Scalar1)));
Scalar2 * gpu_out_data = static_cast<Scalar2*>(sycl_device.allocate(out.size()*sizeof(Scalar2)));
TensorMap<Tensor<Scalar1, 1, DataLayout, IndexType>> gpu_in(gpu_in_data, tensorRange);
TensorMap<Tensor<Scalar2, 1, DataLayout, IndexType>> gpu_out(gpu_out_data, tensorRange);
sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.size())*sizeof(Scalar1));
gpu_out.device(sycl_device) = gpu_in. template cast<Scalar2>();
sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data, out.size()*sizeof(Scalar2));
out_host = in. template cast<Scalar2>();
for(IndexType i=0; i< size; i++)
{
VERIFY_IS_APPROX(out(i), out_host(i));
}
printf("cast Test Passed\n");
sycl_device.deallocate(gpu_in_data);
sycl_device.deallocate(gpu_out_data);
}
template<typename DataType, typename dev_Selector> void sycl_computing_test_per_device(dev_Selector s){
QueueInterface queueInterface(s);
auto sycl_device = Eigen::SyclDevice(&queueInterface);
test_sycl_mem_transfers<DataType, RowMajor, int64_t>(sycl_device);
test_sycl_computations<DataType, RowMajor, int64_t>(sycl_device);
test_sycl_mem_sync<DataType, RowMajor, int64_t>(sycl_device);
test_sycl_mem_sync_offsets<DataType, RowMajor, int64_t>(sycl_device);
test_sycl_memset_offsets<DataType, RowMajor, int64_t>(sycl_device);
test_sycl_mem_transfers<DataType, ColMajor, int64_t>(sycl_device);
test_sycl_computations<DataType, ColMajor, int64_t>(sycl_device);
test_sycl_mem_sync<DataType, ColMajor, int64_t>(sycl_device);
test_sycl_cast<DataType, int, RowMajor, int64_t>(sycl_device);
test_sycl_cast<DataType, int, ColMajor, int64_t>(sycl_device);
}
EIGEN_DECLARE_TEST(cxx11_tensor_sycl) {
for (const auto& device :Eigen::get_sycl_supported_devices()) {
CALL_SUBTEST(sycl_computing_test_per_device<float>(device));
}
}
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