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Merged in benoitsteiner/opencl (pull request PR-238)

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Added support for OpenCL to the Tensor Module
This commit is contained in:
Benoit Steiner 2016-10-24 15:30:45 +00:00
commit b11aab5fcc
35 changed files with 2559 additions and 98 deletions
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7
CMakeLists.txt
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@ -431,6 +431,13 @@ else()
add_subdirectory(lapack EXCLUDE_FROM_ALL)
endif()
# add SYCL
option(EIGEN_TEST_SYCL "Add Sycl support." OFF)
if(EIGEN_TEST_SYCL)
set (CMAKE_MODULE_PATH "${CMAKE_ROOT}/Modules" "cmake/Modules/" "${CMAKE_MODULE_PATH}")
include(FindComputeCpp)
endif()
add_subdirectory(unsupported)
add_subdirectory(demos EXCLUDE_FROM_ALL)

33
Eigen/Core
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@ -14,9 +14,19 @@
// first thing Eigen does: stop the compiler from committing suicide
#include "src/Core/util/DisableStupidWarnings.h"
// Handle NVCC/CUDA
#ifdef __CUDACC__
// Do not try asserts on CUDA!
/// This will no longer be needed after the next release of the computecppCE
#ifdef EIGEN_USE_SYCL
#undef min
#undef max
#undef isnan
#undef isinf
#undef isfinite
#include <SYCL/sycl.hpp>
#endif
// Handle NVCC/CUDA/SYCL
#if defined(__CUDACC__) || defined(__SYCL_DEVICE_ONLY__)
// Do not try asserts on CUDA and SYCL!
#ifndef EIGEN_NO_DEBUG
#define EIGEN_NO_DEBUG
#endif
@ -25,7 +35,7 @@
#undef EIGEN_INTERNAL_DEBUGGING
#endif
// Do not try to vectorize on CUDA!
// Do not try to vectorize on CUDA and SYCL!
#ifndef EIGEN_DONT_VECTORIZE
#define EIGEN_DONT_VECTORIZE
#endif
@ -35,11 +45,14 @@
#endif
// All functions callable from CUDA code must be qualified with __device__
#define EIGEN_DEVICE_FUNC __host__ __device__
// We need math_functions.hpp to ensure that that EIGEN_USING_STD_MATH macro
// works properly on the device side
#include <math_functions.hpp>
#ifdef __CUDACC__
#define EIGEN_DEVICE_FUNC __host__ __device__
// We need math_functions.hpp to ensure that that EIGEN_USING_STD_MATH macro
// works properly on the device side
#include <math_functions.hpp>
#else
#define EIGEN_DEVICE_FUNC
#endif
#else
#define EIGEN_DEVICE_FUNC
@ -55,7 +68,7 @@
#define EIGEN_USING_STD_MATH(FUNC) using std::FUNC;
#endif
#if (defined(_CPPUNWIND) || defined(__EXCEPTIONS)) && !defined(__CUDA_ARCH__) && !defined(EIGEN_EXCEPTIONS)
#if (defined(_CPPUNWIND) || defined(__EXCEPTIONS)) && !defined(__CUDA_ARCH__) && !defined(EIGEN_EXCEPTIONS) && !defined(EIGEN_USE_SYCL)
#define EIGEN_EXCEPTIONS
#endif

8
bench/tensors/README
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@ -11,5 +11,11 @@ nvcc tensor_benchmarks_gpu.cu benchmark_main.cc -I ../../ -std=c++11 -O2 -DNDEBU
We also provide a version of the generic GPU tensor benchmarks that uses half floats (aka fp16) instead of regular floats. To compile these benchmarks, simply call the command line below. You'll need a recent GPU that supports compute capability 5.3 or higher to run them and nvcc 7.5 or higher to compile the code.
nvcc tensor_benchmarks_fp16_gpu.cu benchmark_main.cc -I ../../ -std=c++11 -O2 -DNDEBUG -use_fast_math -ftz=true -arch compute_53 -o benchmarks_fp16_gpu
last but not least, we also provide a suite of benchmarks to measure the scalability of the contraction code on CPU. To compile these benchmarks, call
last but not least, we also provide a suite of benchmarks to measure the scalability of the contraction code on CPU. To compile these benchmarks, call
g++ contraction_benchmarks_cpu.cc benchmark_main.cc -I ../../ -std=c++11 -O3 -DNDEBUG -pthread -mavx -o benchmarks_cpu
To compile the benchmark for SYCL, using ComputeCpp you currently need 2 passes (only for translation units containing device code):
1. The device compilation pass that generates the device code (SYCL kernels and referenced device functions) and glue code needed by the host compiler to reference the device code from host code.
{ComputeCpp_ROOT}/bin/compute++ -I ../../ -I {ComputeCpp_ROOT}/include/ -std=c++11 -mllvm -inline-threshold=1000 -Wno-ignored-attributes -sycl -intelspirmetadata -emit-llvm -no-serial-memop -sycl-compress-name -DBUILD_PLATFORM_SPIR -DNDBUG -O3 -c tensor_benchmarks_sycl.cc
2. The host compilation pass that generates the final host binary.
clang++-3.7 -include tensor_benchmarks_sycl.sycl benchmark_main.cc tensor_benchmarks_sycl.cc -pthread -I ../../ -I {ComputeCpp_ROOT}/include/ -L {ComputeCpp_ROOT}/lib/ -lComputeCpp -lOpenCL -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++11 -o tensor_benchmark_sycl

37
bench/tensors/tensor_benchmarks_sycl.cc Normal file
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@ -0,0 +1,37 @@
#define EIGEN_USE_SYCL
#include <SYCL/sycl.hpp>
#include <iostream>
#include "tensor_benchmarks.h"
using Eigen::array;
using Eigen::SyclDevice;
using Eigen::Tensor;
using Eigen::TensorMap;
// Simple functions
template <typename device_selector>
cl::sycl::queue sycl_queue() {
return cl::sycl::queue(device_selector(), [=](cl::sycl::exception_list l) {
for (const auto& e : l) {
try {
std::rethrow_exception(e);
} catch (cl::sycl::exception e) {
std::cout << e.what() << std::endl;
}
}
});
}
#define BM_FuncGPU(FUNC) \
static void BM_##FUNC(int iters, int N) { \
StopBenchmarkTiming(); \
cl::sycl::queue q = sycl_queue<cl::sycl::gpu_selector>(); \
Eigen::SyclDevice device(q); \
BenchmarkSuite<Eigen::SyclDevice, float> suite(device, N); \
suite.FUNC(iters); \
} \
BENCHMARK_RANGE(BM_##FUNC, 10, 5000);
BM_FuncGPU(broadcasting);
BM_FuncGPU(coeffWiseOp);

135
cmake/EigenTesting.cmake
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@ -109,6 +109,103 @@ macro(ei_add_test_internal testname testname_with_suffix)
endmacro(ei_add_test_internal)
# SYCL
macro(ei_add_test_internal_sycl testname testname_with_suffix)
include_directories( SYSTEM ${COMPUTECPP_PACKAGE_ROOT_DIR}/include)
set(targetname ${testname_with_suffix})
if(EIGEN_ADD_TEST_FILENAME_EXTENSION)
set(filename ${testname}.${EIGEN_ADD_TEST_FILENAME_EXTENSION})
else()
set(filename ${testname}.cpp)
endif()
set( include_file ${CMAKE_CURRENT_BINARY_DIR}/inc_${filename})
set( bc_file ${CMAKE_CURRENT_BINARY_DIR}/${filename})
set( host_file ${CMAKE_CURRENT_SOURCE_DIR}/${filename})
ADD_CUSTOM_COMMAND(
OUTPUT ${include_file}
COMMAND ${CMAKE_COMMAND} -E echo "\\#include \\\"${host_file}\\\"" > ${include_file}
COMMAND ${CMAKE_COMMAND} -E echo "\\#include \\\"${bc_file}.sycl\\\"" >> ${include_file}
DEPENDS ${filename}
COMMENT "Building ComputeCpp integration header file ${include_file}"
)
# Add a custom target for the generated integration header
add_custom_target(${testname}_integration_header_woho DEPENDS ${include_file})
add_executable(${targetname} ${include_file})
add_dependencies(${targetname} ${testname}_integration_header_woho)
add_sycl_to_target(${targetname} ${filename} ${CMAKE_CURRENT_BINARY_DIR})
if (targetname MATCHES "^eigen2_")
add_dependencies(eigen2_buildtests ${targetname})
else()
add_dependencies(buildtests ${targetname})
endif()
if(EIGEN_NO_ASSERTION_CHECKING)
ei_add_target_property(${targetname} COMPILE_FLAGS "-DEIGEN_NO_ASSERTION_CHECKING=1")
else(EIGEN_NO_ASSERTION_CHECKING)
if(EIGEN_DEBUG_ASSERTS)
ei_add_target_property(${targetname} COMPILE_FLAGS "-DEIGEN_DEBUG_ASSERTS=1")
endif(EIGEN_DEBUG_ASSERTS)
endif(EIGEN_NO_ASSERTION_CHECKING)
ei_add_target_property(${targetname} COMPILE_FLAGS "-DEIGEN_TEST_MAX_SIZE=${EIGEN_TEST_MAX_SIZE}")
ei_add_target_property(${targetname} COMPILE_FLAGS "-DEIGEN_TEST_FUNC=${testname}")
if(MSVC AND NOT EIGEN_SPLIT_LARGE_TESTS)
ei_add_target_property(${targetname} COMPILE_FLAGS "/bigobj")
endif()
# let the user pass flags.
if(${ARGC} GREATER 2)
ei_add_target_property(${targetname} COMPILE_FLAGS "${ARGV2}")
endif(${ARGC} GREATER 2)
if(EIGEN_TEST_CUSTOM_CXX_FLAGS)
ei_add_target_property(${targetname} COMPILE_FLAGS "${EIGEN_TEST_CUSTOM_CXX_FLAGS}")
endif()
if(EIGEN_STANDARD_LIBRARIES_TO_LINK_TO)
target_link_libraries(${targetname} ${EIGEN_STANDARD_LIBRARIES_TO_LINK_TO})
endif()
if(EXTERNAL_LIBS)
target_link_libraries(${targetname} ${EXTERNAL_LIBS})
endif()
if(EIGEN_TEST_CUSTOM_LINKER_FLAGS)
target_link_libraries(${targetname} ${EIGEN_TEST_CUSTOM_LINKER_FLAGS})
endif()
if(${ARGC} GREATER 3)
set(libs_to_link ${ARGV3})
# it could be that some cmake module provides a bad library string " " (just spaces),
# and that severely breaks target_link_libraries ("can't link to -l-lstdc++" errors).
# so we check for strings containing only spaces.
string(STRIP "${libs_to_link}" libs_to_link_stripped)
string(LENGTH "${libs_to_link_stripped}" libs_to_link_stripped_length)
if(${libs_to_link_stripped_length} GREATER 0)
# notice: no double quotes around ${libs_to_link} here. It may be a list.
target_link_libraries(${targetname} ${libs_to_link})
endif()
endif()
add_test(${testname_with_suffix} "${targetname}")
# Specify target and test labels according to EIGEN_CURRENT_SUBPROJECT
get_property(current_subproject GLOBAL PROPERTY EIGEN_CURRENT_SUBPROJECT)
if ((current_subproject) AND (NOT (current_subproject STREQUAL "")))
set_property(TARGET ${targetname} PROPERTY LABELS "Build${current_subproject}")
add_dependencies("Build${current_subproject}" ${targetname})
set_property(TEST ${testname_with_suffix} PROPERTY LABELS "${current_subproject}")
endif()
endmacro(ei_add_test_internal_sycl)
# Macro to add a test
#
# the unique mandatory parameter testname must correspond to a file
@ -185,6 +282,39 @@ macro(ei_add_test testname)
endif(EIGEN_SPLIT_LARGE_TESTS AND suffixes)
endmacro(ei_add_test)
macro(ei_add_test_sycl testname)
get_property(EIGEN_TESTS_LIST GLOBAL PROPERTY EIGEN_TESTS_LIST)
set(EIGEN_TESTS_LIST "${EIGEN_TESTS_LIST}${testname}\n")
set_property(GLOBAL PROPERTY EIGEN_TESTS_LIST "${EIGEN_TESTS_LIST}")
if(EIGEN_ADD_TEST_FILENAME_EXTENSION)
set(filename ${testname}.${EIGEN_ADD_TEST_FILENAME_EXTENSION})
else()
set(filename ${testname}.cpp)
endif()
file(READ "${filename}" test_source)
set(parts 0)
string(REGEX MATCHALL "CALL_SUBTEST_[0-9]+|EIGEN_TEST_PART_[0-9]+|EIGEN_SUFFIXES(;[0-9]+)+"
occurences "${test_source}")
string(REGEX REPLACE "CALL_SUBTEST_|EIGEN_TEST_PART_|EIGEN_SUFFIXES" "" suffixes "${occurences}")
list(REMOVE_DUPLICATES suffixes)
if(EIGEN_SPLIT_LARGE_TESTS AND suffixes)
add_custom_target(${testname})
foreach(suffix ${suffixes})
ei_add_test_internal_sycl(${testname} ${testname}_${suffix}
"${ARGV1} -DEIGEN_TEST_PART_${suffix}=1" "${ARGV2}")
add_dependencies(${testname} ${testname}_${suffix})
endforeach(suffix)
else(EIGEN_SPLIT_LARGE_TESTS AND suffixes)
set(symbols_to_enable_all_parts "")
foreach(suffix ${suffixes})
set(symbols_to_enable_all_parts
"${symbols_to_enable_all_parts} -DEIGEN_TEST_PART_${suffix}=1")
endforeach(suffix)
ei_add_test_internal_sycl(${testname} ${testname} "${ARGV1} ${symbols_to_enable_all_parts}" "${ARGV2}")
endif(EIGEN_SPLIT_LARGE_TESTS AND suffixes)
endmacro(ei_add_test_sycl)
# adds a failtest, i.e. a test that succeed if the program fails to compile
# note that the test runner for these is CMake itself, when passed -DEIGEN_FAILTEST=ON
@ -330,6 +460,11 @@ macro(ei_testing_print_summary)
message(STATUS "C++11: OFF")
endif()
if(EIGEN_TEST_SYCL)
message(STATUS "SYCL: ON")
else()
message(STATUS "SYCL: OFF")
endif()
if(EIGEN_TEST_CUDA)
if(EIGEN_TEST_CUDA_CLANG)
message(STATUS "CUDA: ON (using clang)")

228
cmake/FindComputeCpp.cmake Normal file
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@ -0,0 +1,228 @@
#.rst:
# FindComputeCpp
#---------------
#########################
# FindComputeCpp.cmake
#########################
#
# Tools for finding and building with ComputeCpp.
#
# Require CMake version 3.2.2 or higher
cmake_minimum_required(VERSION 3.2.2)
# Check that a supported host compiler can be found
if(CMAKE_COMPILER_IS_GNUCXX)
# Require at least gcc 4.8
if (CMAKE_CXX_COMPILER_VERSION VERSION_LESS 4.8)
message(FATAL_ERROR
"host compiler - Not found! (gcc version must be at least 4.8)")
# Require the GCC dual ABI to be disabled for 5.1 or higher
elseif (CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 5.1)
set(COMPUTECPP_DISABLE_GCC_DUAL_ABI "True")
message(STATUS
"host compiler - gcc ${CMAKE_CXX_COMPILER_VERSION} (note pre 5.1 gcc ABI enabled)")
else()
message(STATUS "host compiler - gcc ${CMAKE_CXX_COMPILER_VERSION}")
endif()
elseif ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang")
# Require at least clang 3.6
if (${CMAKE_CXX_COMPILER_VERSION} VERSION_LESS 3.6)
message(FATAL_ERROR
"host compiler - Not found! (clang version must be at least 3.6)")
else()
set(COMPUTECPP_DISABLE_GCC_DUAL_ABI "True")
message(STATUS "host compiler - clang ${CMAKE_CXX_COMPILER_VERSION}")
endif()
else()
message(WARNING
"host compiler - Not found! (ComputeCpp supports GCC and Clang, see readme)")
endif()
set(COMPUTECPP_64_BIT_DEFAULT ON)
option(COMPUTECPP_64_BIT_CODE "Compile device code in 64 bit mode"
${COMPUTECPP_64_BIT_DEFAULT})
mark_as_advanced(COMPUTECPP_64_BIT_CODE)
# Find OpenCL package
find_package(OpenCL REQUIRED)
# Find ComputeCpp package
if(EXISTS ${COMPUTECPP_PACKAGE_ROOT_DIR})
message(STATUS "ComputeCpp package - Found (${COMPUTECPP_PACKAGE_ROOT_DIR})")
else()
message(FATAL_ERROR "ComputeCpp package - Not found! (please set COMPUTECPP_PACKAGE_ROOT_DIR) (${COMPUTECPP_PACKAGE_ROOT_DIR})")
endif()
option(COMPUTECPP_PACKAGE_ROOT_DIR "Path to the ComputeCpp Package")
# Obtain the path to compute++
find_program(COMPUTECPP_DEVICE_COMPILER compute++ PATHS
${COMPUTECPP_PACKAGE_ROOT_DIR} PATH_SUFFIXES bin)
if (EXISTS ${COMPUTECPP_DEVICE_COMPILER})
mark_as_advanced(COMPUTECPP_DEVICE_COMPILER)
message(STATUS "compute++ - Found (${COMPUTECPP_PACKAGE_ROOT_DIR})")
else()
message(FATAL_ERROR "compute++ - Not found! (${COMPUTECPP_DEVICE_COMPILER}) (${COMPUTECPP_PACKAGE_ROOT_DIR})")
endif()
# Obtain the path to computecpp_info
find_program(COMPUTECPP_INFO_TOOL computecpp_info PATHS
${COMPUTECPP_PACKAGE_ROOT_DIR} PATH_SUFFIXES bin)
if (EXISTS ${COMPUTECPP_INFO_TOOL})
mark_as_advanced(${COMPUTECPP_INFO_TOOL})
message(STATUS "computecpp_info - Found (${COMPUTECPP_PACKAGE_ROOT_DIR})")
else()
message(FATAL_ERROR "computecpp_info - Not found! (${COMPUTECPP_INFO_TOOL}) (${COMPUTECPP_PACKAGE_ROOT_DIR})")
endif()
# Obtain the path to the ComputeCpp runtime library
find_library(COMPUTECPP_RUNTIME_LIBRARY ComputeCpp PATHS ${COMPUTECPP_PACKAGE_ROOT_DIR}
HINTS ${COMPUTECPP_PACKAGE_ROOT_DIR}/lib PATH_SUFFIXES lib
DOC "ComputeCpp Runtime Library" NO_DEFAULT_PATH)
if (EXISTS ${COMPUTECPP_RUNTIME_LIBRARY})
mark_as_advanced(COMPUTECPP_RUNTIME_LIBRARY)
message(STATUS "libComputeCpp.so - Found")
else()
message(FATAL_ERROR "libComputeCpp.so - Not found! (${COMPUTECPP_PACKAGE_ROOT_DIR})")
endif()
# Obtain the ComputeCpp include directory
set(COMPUTECPP_INCLUDE_DIRECTORY ${COMPUTECPP_PACKAGE_ROOT_DIR}/include/)
if (NOT EXISTS ${COMPUTECPP_INCLUDE_DIRECTORY})
message(FATAL_ERROR "ComputeCpp includes - Not found! (${COMPUTECPP_PACKAGE_ROOT_DIR}/include/)")
else()
message(STATUS "ComputeCpp includes - Found (${COMPUTECPP_PACKAGE_ROOT_DIR})")
endif()
# Obtain the package version
execute_process(COMMAND ${COMPUTECPP_INFO_TOOL} "--dump-version"
OUTPUT_VARIABLE COMPUTECPP_PACKAGE_VERSION
RESULT_VARIABLE COMPUTECPP_INFO_TOOL_RESULT OUTPUT_STRIP_TRAILING_WHITESPACE)
if(NOT COMPUTECPP_INFO_TOOL_RESULT EQUAL "0")
message(FATAL_ERROR "Package version - Error obtaining version!")
else()
mark_as_advanced(COMPUTECPP_PACKAGE_VERSION)
message(STATUS "Package version - ${COMPUTECPP_PACKAGE_VERSION}")
endif()
# Obtain the device compiler flags
execute_process(COMMAND ${COMPUTECPP_INFO_TOOL} "--dump-device-compiler-flags"
OUTPUT_VARIABLE COMPUTECPP_DEVICE_COMPILER_FLAGS
RESULT_VARIABLE COMPUTECPP_INFO_TOOL_RESULT OUTPUT_STRIP_TRAILING_WHITESPACE)
if(NOT COMPUTECPP_INFO_TOOL_RESULT EQUAL "0")
message(FATAL_ERROR "compute++ flags - Error obtaining compute++ flags!")
else()
mark_as_advanced(COMPUTECPP_COMPILER_FLAGS)
message(STATUS "compute++ flags - ${COMPUTECPP_DEVICE_COMPILER_FLAGS}")
endif()
set(COMPUTECPP_DEVICE_COMPILER_FLAGS ${COMPUTECPP_DEVICE_COMPILER_FLAGS} -sycl-compress-name -no-serial-memop -DEIGEN_NO_ASSERTION_CHECKING=1)
# Check if the platform is supported
execute_process(COMMAND ${COMPUTECPP_INFO_TOOL} "--dump-is-supported"
OUTPUT_VARIABLE COMPUTECPP_PLATFORM_IS_SUPPORTED
RESULT_VARIABLE COMPUTECPP_INFO_TOOL_RESULT OUTPUT_STRIP_TRAILING_WHITESPACE)
if(NOT COMPUTECPP_INFO_TOOL_RESULT EQUAL "0")
message(FATAL_ERROR "platform - Error checking platform support!")
else()
mark_as_advanced(COMPUTECPP_PLATFORM_IS_SUPPORTED)
if (COMPUTECPP_PLATFORM_IS_SUPPORTED)
message(STATUS "platform - your system can support ComputeCpp")
else()
message(STATUS "platform - your system CANNOT support ComputeCpp")
endif()
endif()
####################
# __build_sycl
####################
#
# Adds a custom target for running compute++ and adding a dependency for the
# resulting integration header.
#
# targetName : Name of the target.
# sourceFile : Source file to be compiled.
# binaryDir : Intermediate output directory for the integration header.
#
function(__build_spir targetName sourceFile binaryDir)
# Retrieve source file name.
get_filename_component(sourceFileName ${sourceFile} NAME)
# Set the path to the Sycl file.
set(outputSyclFile ${binaryDir}/${sourceFileName}.sycl)
# Add any user-defined include to the device compiler
get_property(includeDirectories DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR} PROPERTY
INCLUDE_DIRECTORIES)
set(device_compiler_includes "")
foreach(directory ${includeDirectories})
set(device_compiler_includes "-I${directory}" ${device_compiler_includes})
endforeach()
if (CMAKE_INCLUDE_PATH)
foreach(directory ${CMAKE_INCLUDE_PATH})
set(device_compiler_includes "-I${directory}"
${device_compiler_includes})
endforeach()
endif()
# Convert argument list format
separate_arguments(COMPUTECPP_DEVICE_COMPILER_FLAGS)
# Add custom command for running compute++
add_custom_command(
OUTPUT ${outputSyclFile}
COMMAND ${COMPUTECPP_DEVICE_COMPILER}
${COMPUTECPP_DEVICE_COMPILER_FLAGS}
-I${COMPUTECPP_INCLUDE_DIRECTORY}
${COMPUTECPP_PLATFORM_SPECIFIC_ARGS}
${device_compiler_includes}
-o ${outputSyclFile}
-c ${CMAKE_CURRENT_SOURCE_DIR}/${sourceFile}
DEPENDS ${sourceFile}
COMMENT "Building ComputeCpp integration header file ${outputSyclFile}")
# Add a custom target for the generated integration header
add_custom_target(${targetName}_integration_header DEPENDS ${outputSyclFile})
# Add a dependency on the integration header
add_dependencies(${targetName} ${targetName}_integration_header)
# Force inclusion of the integration header for the host compiler
#set(compileFlags -include ${include_file} "-Wall")
target_compile_options(${targetName} PUBLIC ${compileFlags})
# Set the host compiler C++ standard to C++11
set_property(TARGET ${targetName} PROPERTY CXX_STANDARD 11)
# Disable GCC dual ABI on GCC 5.1 and higher
if(COMPUTECPP_DISABLE_GCC_DUAL_ABI)
set_property(TARGET ${targetName} APPEND PROPERTY COMPILE_DEFINITIONS
"_GLIBCXX_USE_CXX11_ABI=0")
endif()
endfunction()
#######################
# add_sycl_to_target
#######################
#
# Adds a SYCL compilation custom command associated with an existing
# target and sets a dependency on that new command.
#
# targetName : Name of the target to add a SYCL to.
# sourceFile : Source file to be compiled for SYCL.
# binaryDir : Intermediate output directory for the integration header.
#
function(add_sycl_to_target targetName sourceFile binaryDir)
# Add custom target to run compute++ and generate the integration header
__build_spir(${targetName} ${sourceFile} ${binaryDir})
# Link with the ComputeCpp runtime library
target_link_libraries(${targetName} PUBLIC ${COMPUTECPP_RUNTIME_LIBRARY}
PUBLIC ${OpenCL_LIBRARIES})
endfunction(add_sycl_to_target)

6
unsupported/Eigen/CXX11/Tensor
View File

@ -69,6 +69,10 @@ typedef unsigned __int64 uint64_t;
#endif
#endif
#ifdef EIGEN_USE_SYCL
#include <SYCL/sycl.hpp>
#endif
#include "src/Tensor/TensorMacros.h"
#include "src/Tensor/TensorForwardDeclarations.h"
#include "src/Tensor/TensorMeta.h"
@ -77,6 +81,8 @@ typedef unsigned __int64 uint64_t;
#include "src/Tensor/TensorDeviceDefault.h"
#include "src/Tensor/TensorDeviceThreadPool.h"
#include "src/Tensor/TensorDeviceCuda.h"
#include "src/Tensor/TensorSycl.h"
#include "src/Tensor/TensorDeviceSycl.h"
#include "src/Tensor/TensorIndexList.h"
#include "src/Tensor/TensorDimensionList.h"
#include "src/Tensor/TensorDimensions.h"

5
unsupported/Eigen/CXX11/src/Tensor/TensorAssign.h
View File

@ -163,6 +163,11 @@ struct TensorEvaluator<const TensorAssignOp<LeftArgType, RightArgType>, Device>
TensorOpCost(0, sizeof(CoeffReturnType), 0, vectorized, PacketSize);
}
/// required by sycl in order to extract the accessor
const TensorEvaluator<LeftArgType, Device>& left_impl() const { return m_leftImpl; }
/// required by sycl in order to extract the accessor
const TensorEvaluator<RightArgType, Device>& right_impl() const { return m_rightImpl; }
EIGEN_DEVICE_FUNC CoeffReturnType* data() const { return m_leftImpl.data(); }
private:

4
unsupported/Eigen/CXX11/src/Tensor/TensorBase.h
View File

@ -817,7 +817,7 @@ class TensorBase<Derived, ReadOnlyAccessors>
protected:
template <typename Scalar, int NumIndices, int Options, typename IndexType> friend class Tensor;
template <typename Scalar, typename Dimensions, int Option, typename IndexTypes> friend class TensorFixedSize;
template <typename Scalar, typename Dimensions, int Option, typename IndexTypes, template <class> class MakePointer_> friend class TensorFixedSize;
template <typename OtherDerived, int AccessLevel> friend class TensorBase;
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Derived& derived() const { return *static_cast<const Derived*>(this); }
@ -833,7 +833,7 @@ class TensorBase : public TensorBase<Derived, ReadOnlyAccessors> {
static const int NumDimensions = DerivedTraits::NumDimensions;
template <typename Scalar, int NumIndices, int Options, typename IndexType> friend class Tensor;
template <typename Scalar, typename Dimensions, int Option, typename IndexTypes> friend class TensorFixedSize;
template <typename Scalar, typename Dimensions, int Option, typename IndexTypes, template <class> class MakePointer_> friend class TensorFixedSize;
template <typename OtherDerived, int OtherAccessLevel> friend class TensorBase;
EIGEN_DEVICE_FUNC

7
unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h
View File

@ -113,7 +113,7 @@ struct TensorEvaluator<const TensorBroadcastingOp<Broadcast, ArgType>, Device>
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
: m_impl(op.expression(), device)
: m_broadcast(op.broadcast()),m_impl(op.expression(), device)
{
// The broadcasting op doesn't change the rank of the tensor. One can't broadcast a scalar
// and store the result in a scalar. Instead one should reshape the scalar into a a N-D
@ -374,7 +374,12 @@ struct TensorEvaluator<const TensorBroadcastingOp<Broadcast, ArgType>, Device>
EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
Broadcast functor() const { return m_broadcast; }
protected:
const Broadcast m_broadcast;
Dimensions m_dimensions;
array<Index, NumDims> m_outputStrides;
array<Index, NumDims> m_inputStrides;

122
unsupported/Eigen/CXX11/src/Tensor/TensorDeviceSycl.h Normal file
View File

@ -0,0 +1,122 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
// Mehdi Goli Codeplay Software Ltd.
// Ralph Potter Codeplay Software Ltd.
// Luke Iwanski Codeplay Software Ltd.
// Cummins Chris PhD student at The University of Edinburgh.
// 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/.
#if defined(EIGEN_USE_SYCL) && !defined(EIGEN_CXX11_TENSOR_TENSOR_DEVICE_SYCL_H)
#define EIGEN_CXX11_TENSOR_TENSOR_DEVICE_SYCL_H
namespace Eigen {
/// \struct BufferT is used to specialise add_sycl_buffer function for
// two types of buffer we have. When the MapAllocator is true, we create the
// sycl buffer with MapAllocator.
/// We have to const_cast the input pointer in order to work around the fact
/// that sycl does not accept map allocator for const pointer.
template <typename T, bool MapAllocator>
struct BufferT {
using Type = cl::sycl::buffer<T, 1, cl::sycl::map_allocator<T>>;
static inline void add_sycl_buffer(
const T *ptr, size_t num_bytes,
std::map<const void *, std::shared_ptr<void>> &buffer_map) {
buffer_map.insert(std::pair<const void *, std::shared_ptr<void>>(
ptr, std::shared_ptr<void>(std::make_shared<Type>(
Type(const_cast<T *>(ptr), cl::sycl::range<1>(num_bytes))))));
}
};
/// specialisation of the \ref BufferT when the MapAllocator is false. In this
/// case we only create the device-only buffer.
template <typename T>
struct BufferT<T, false> {
using Type = cl::sycl::buffer<T, 1>;
static inline void add_sycl_buffer(
const T *ptr, size_t num_bytes,
std::map<const void *, std::shared_ptr<void>> &buffer_map) {
buffer_map.insert(std::pair<const void *, std::shared_ptr<void>>(
ptr, std::shared_ptr<void>(
std::make_shared<Type>(Type(cl::sycl::range<1>(num_bytes))))));
}
};
struct SyclDevice {
/// class members
/// sycl queue
cl::sycl::queue &m_queue;
/// std::map is the container used to make sure that we create only one buffer
/// per pointer. The lifespan of the buffer
/// now depends on the lifespan of SyclDevice. If a non-read-only pointer is
/// needed to be accessed on the host we should manually deallocate it.
mutable std::map<const void *, std::shared_ptr<void>> buffer_map;
SyclDevice(cl::sycl::queue &q) : m_queue(q) {}
// destructor
~SyclDevice() { deallocate_all(); }
template <typename T>
void deallocate(const T *p) const {
auto it = buffer_map.find(p);
if (it != buffer_map.end()) {
buffer_map.erase(it);
}
}
void deallocate_all() const { buffer_map.clear(); }
/// creation of sycl accessor for a buffer. This function first tries to find
/// the buffer in the buffer_map.
/// If found it gets the accessor from it, if not, the function then adds an
/// entry by creating a sycl buffer
/// for that particular pointer.
template <cl::sycl::access::mode AcMd, bool MapAllocator, typename T>
inline cl::sycl::accessor<T, 1, AcMd, cl::sycl::access::target::global_buffer>
get_sycl_accessor(size_t num_bytes, cl::sycl::handler &cgh,
const T *ptr) const {
auto it = buffer_map.find(ptr);
if (it == buffer_map.end()) {
BufferT<T, MapAllocator>::add_sycl_buffer(ptr, num_bytes, buffer_map);
}
return (
((typename BufferT<T, MapAllocator>::Type *)(buffer_map.at(ptr).get()))
->template get_access<AcMd>(cgh));
}
/// allocating memory on the cpu
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void *allocate(size_t num_bytes) const {
return internal::aligned_malloc(num_bytes);
}
// some runtime conditions that can be applied here
bool isDeviceSuitable() const { return true; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void deallocate(void *buffer) const {
internal::aligned_free(buffer);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpy(void *dst, const void *src,
size_t n) const {
::memcpy(dst, src, n);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpyHostToDevice(
void *dst, const void *src, size_t n) const {
memcpy(dst, src, n);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpyDeviceToHost(
void *dst, const void *src, size_t n) const {
memcpy(dst, src, n);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memset(void *buffer, int c,
size_t n) const {
::memset(buffer, c, n);
}
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_DEVICE_SYCL_H

56
unsupported/Eigen/CXX11/src/Tensor/TensorEvalTo.h
View File

@ -20,8 +20,8 @@ namespace Eigen {
*
*/
namespace internal {
template<typename XprType>
struct traits<TensorEvalToOp<XprType> >
template<typename XprType, template <class> class MakePointer_>
struct traits<TensorEvalToOp<XprType, MakePointer_> >
{
// Type promotion to handle the case where the types of the lhs and the rhs are different.
typedef typename XprType::Scalar Scalar;
@ -36,16 +36,20 @@ struct traits<TensorEvalToOp<XprType> >
enum {
Flags = 0
};
template <class T>
struct MakePointer {
typedef typename MakePointer_<T>::Type Type;
};
};
template<typename XprType>
struct eval<TensorEvalToOp<XprType>, Eigen::Dense>
template<typename XprType, template <class> class MakePointer_>
struct eval<TensorEvalToOp<XprType, MakePointer_>, Eigen::Dense>
{
typedef const TensorEvalToOp<XprType>& type;
};
template<typename XprType>
struct nested<TensorEvalToOp<XprType>, 1, typename eval<TensorEvalToOp<XprType> >::type>
template<typename XprType, template <class> class MakePointer_>
struct nested<TensorEvalToOp<XprType, MakePointer_>, 1, typename eval<TensorEvalToOp<XprType, MakePointer_> >::type>
{
typedef TensorEvalToOp<XprType> type;
};
@ -55,37 +59,38 @@ struct nested<TensorEvalToOp<XprType>, 1, typename eval<TensorEvalToOp<XprType>
template<typename XprType>
class TensorEvalToOp : public TensorBase<TensorEvalToOp<XprType>, ReadOnlyAccessors>
template<typename XprType, template <class> class MakePointer_>
class TensorEvalToOp : public TensorBase<TensorEvalToOp<XprType, MakePointer_>, ReadOnlyAccessors>
{
public:
typedef typename Eigen::internal::traits<TensorEvalToOp>::Scalar Scalar;
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
typedef typename MakePointer_<CoeffReturnType>::Type PointerType;
typedef typename Eigen::internal::nested<TensorEvalToOp>::type Nested;
typedef typename Eigen::internal::traits<TensorEvalToOp>::StorageKind StorageKind;
typedef typename Eigen::internal::traits<TensorEvalToOp>::Index Index;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvalToOp(CoeffReturnType* buffer, const XprType& expr)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvalToOp(PointerType buffer, const XprType& expr)
: m_xpr(expr), m_buffer(buffer) {}
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename XprType::Nested>::type&
expression() const { return m_xpr; }
EIGEN_DEVICE_FUNC CoeffReturnType* buffer() const { return m_buffer; }
EIGEN_DEVICE_FUNC PointerType buffer() const { return m_buffer; }
protected:
typename XprType::Nested m_xpr;
CoeffReturnType* m_buffer;
PointerType m_buffer;
};
template<typename ArgType, typename Device>
struct TensorEvaluator<const TensorEvalToOp<ArgType>, Device>
template<typename ArgType, typename Device, template <class> class MakePointer_>
struct TensorEvaluator<const TensorEvalToOp<ArgType, MakePointer_>, Device>
{
typedef TensorEvalToOp<ArgType> XprType;
typedef TensorEvalToOp<ArgType, MakePointer_> XprType;
typedef typename ArgType::Scalar Scalar;
typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
typedef typename XprType::Index Index;
@ -102,15 +107,22 @@ struct TensorEvaluator<const TensorEvalToOp<ArgType>, Device>
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
: m_impl(op.expression(), device), m_device(device), m_buffer(op.buffer())
: m_impl(op.expression(), device), m_device(device),
m_buffer(op.buffer()), m_op(op), m_expression(op.expression())
{ }
// Used for accessor extraction in SYCL Managed TensorMap:
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const XprType& op() const {
return m_op;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ~TensorEvaluator() {
}
typedef typename internal::traits<const TensorEvalToOp<ArgType, MakePointer_> >::template MakePointer<CoeffReturnType>::Type DevicePointer;
EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_impl.dimensions(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType* scalar) {
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(DevicePointer scalar) {
EIGEN_UNUSED_VARIABLE(scalar);
eigen_assert(scalar == NULL);
return m_impl.evalSubExprsIfNeeded(m_buffer);
@ -145,12 +157,20 @@ struct TensorEvaluator<const TensorEvalToOp<ArgType>, Device>
TensorOpCost(0, sizeof(CoeffReturnType), 0, vectorized, PacketSize);
}
EIGEN_DEVICE_FUNC CoeffReturnType* data() const { return m_buffer; }
EIGEN_DEVICE_FUNC DevicePointer data() const { return m_buffer; }
ArgType expression() const { return m_expression; }
/// required by sycl in order to extract the accessor
const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
/// added for sycl in order to construct the buffer from the sycl device
const Device& device() const{return m_device;}
private:
TensorEvaluator<ArgType, Device> m_impl;
const Device& m_device;
CoeffReturnType* m_buffer;
DevicePointer m_buffer;
const XprType& m_op;
const ArgType m_expression;
};

56
unsupported/Eigen/CXX11/src/Tensor/TensorEvaluator.h
View File

@ -46,9 +46,11 @@ struct TensorEvaluator
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const Derived& m, const Device& device)
: m_data(const_cast<Scalar*>(m.data())), m_dims(m.dimensions()), m_device(device)
: m_data(const_cast<typename internal::traits<Derived>::template MakePointer<Scalar>::Type>(m.data())), m_dims(m.dimensions()), m_device(device), m_impl(m)
{ }
// Used for accessor extraction in SYCL Managed TensorMap:
const Derived& derived() const { return m_impl; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dims; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType* dest) {
@ -106,12 +108,16 @@ struct TensorEvaluator
internal::unpacket_traits<PacketReturnType>::size);
}
EIGEN_DEVICE_FUNC Scalar* data() const { return m_data; }
EIGEN_DEVICE_FUNC typename internal::traits<Derived>::template MakePointer<Scalar>::Type data() const { return m_data; }
/// required by sycl in order to construct sycl buffer from raw pointer
const Device& device() const{return m_device;}
protected:
Scalar* m_data;
typename internal::traits<Derived>::template MakePointer<Scalar>::Type m_data;
Dimensions m_dims;
const Device& m_device;
const Derived& m_impl;
};
namespace {
@ -159,8 +165,11 @@ struct TensorEvaluator<const Derived, Device>
RawAccess = true
};
// Used for accessor extraction in SYCL Managed TensorMap:
const Derived& derived() const { return m_impl; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const Derived& m, const Device& device)
: m_data(m.data()), m_dims(m.dimensions()), m_device(device)
: m_data(m.data()), m_dims(m.dimensions()), m_device(device), m_impl(m)
{ }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dims; }
@ -198,12 +207,16 @@ struct TensorEvaluator<const Derived, Device>
internal::unpacket_traits<PacketReturnType>::size);
}
EIGEN_DEVICE_FUNC const Scalar* data() const { return m_data; }
EIGEN_DEVICE_FUNC typename internal::traits<Derived>::template MakePointer<const Scalar>::Type data() const { return m_data; }
/// added for sycl in order to construct the buffer from the sycl device
const Device& device() const{return m_device;}
protected:
const Scalar* m_data;
typename internal::traits<Derived>::template MakePointer<const Scalar>::Type m_data;
Dimensions m_dims;
const Device& m_device;
const Derived& m_impl;
};
@ -260,6 +273,12 @@ struct TensorEvaluator<const TensorCwiseNullaryOp<NullaryOp, ArgType>, Device>
EIGEN_DEVICE_FUNC CoeffReturnType* data() const { return NULL; }
/// required by sycl in order to extract the accessor
const TensorEvaluator<ArgType, Device>& impl() const { return m_argImpl; }
/// required by sycl in order to extract the accessor
NullaryOp functor() const { return m_functor; }
private:
const NullaryOp m_functor;
TensorEvaluator<ArgType, Device> m_argImpl;
@ -324,6 +343,12 @@ struct TensorEvaluator<const TensorCwiseUnaryOp<UnaryOp, ArgType>, Device>
EIGEN_DEVICE_FUNC CoeffReturnType* data() const { return NULL; }
/// required by sycl in order to extract the accessor
const TensorEvaluator<ArgType, Device> & impl() const { return m_argImpl; }
/// added for sycl in order to construct the buffer from sycl device
UnaryOp functor() const { return m_functor; }
private:
const UnaryOp m_functor;
TensorEvaluator<ArgType, Device> m_argImpl;
@ -397,6 +422,12 @@ struct TensorEvaluator<const TensorCwiseBinaryOp<BinaryOp, LeftArgType, RightArg
}
EIGEN_DEVICE_FUNC CoeffReturnType* data() const { return NULL; }
/// required by sycl in order to extract the accessor
const TensorEvaluator<LeftArgType, Device>& left_impl() const { return m_leftImpl; }
/// required by sycl in order to extract the accessor
const TensorEvaluator<RightArgType, Device>& right_impl() const { return m_rightImpl; }
/// required by sycl in order to extract the accessor
BinaryOp functor() const { return m_functor; }
private:
const BinaryOp m_functor;
@ -492,6 +523,13 @@ struct TensorEvaluator<const TensorCwiseTernaryOp<TernaryOp, Arg1Type, Arg2Type,
EIGEN_DEVICE_FUNC CoeffReturnType* data() const { return NULL; }
/// required by sycl in order to extract the accessor
const TensorEvaluator<Arg1Type, Device> & arg1Impl() const { return m_arg1Impl; }
/// required by sycl in order to extract the accessor
const TensorEvaluator<Arg2Type, Device>& arg2Impl() const { return m_arg2Impl; }
/// required by sycl in order to extract the accessor
const TensorEvaluator<Arg3Type, Device>& arg3Impl() const { return m_arg3Impl; }
private:
const TernaryOp m_functor;
TensorEvaluator<Arg1Type, Device> m_arg1Impl;
@ -576,6 +614,12 @@ struct TensorEvaluator<const TensorSelectOp<IfArgType, ThenArgType, ElseArgType>
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType* data() const { return NULL; }
/// required by sycl in order to extract the accessor
const TensorEvaluator<IfArgType, Device> & cond_impl() const { return m_condImpl; }
/// required by sycl in order to extract the accessor
const TensorEvaluator<ThenArgType, Device>& then_impl() const { return m_thenImpl; }
/// required by sycl in order to extract the accessor
const TensorEvaluator<ElseArgType, Device>& else_impl() const { return m_elseImpl; }
private:
TensorEvaluator<IfArgType, Device> m_condImpl;

14
unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h
View File

@ -267,6 +267,20 @@ inline void TensorExecutor<Expression, GpuDevice, Vectorizable>::run(
#endif // __CUDACC__
#endif // EIGEN_USE_GPU
// SYCL Executor policy
#ifdef EIGEN_USE_SYCL
template <typename Expression, bool Vectorizable>
class TensorExecutor<Expression, SyclDevice, Vectorizable> {
public:
static inline void run(const Expression &expr, const SyclDevice &device) {
// call TensorSYCL module
TensorSycl::run(expr, device);
}
};
#endif
} // end namespace internal
} // end namespace Eigen

4
unsupported/Eigen/CXX11/src/Tensor/TensorFixedSize.h
View File

@ -23,8 +23,8 @@ namespace Eigen {
* Eigen::TensorFixedSize<float, Size<3,5,7>> t;
*/
template<typename Scalar_, typename Dimensions_, int Options_, typename IndexType>
class TensorFixedSize : public TensorBase<TensorFixedSize<Scalar_, Dimensions_, Options_, IndexType> >
template<typename Scalar_, typename Dimensions_, int Options_, typename IndexType, template <class> class MakePointer_>
class TensorFixedSize : public TensorBase<TensorFixedSize<Scalar_, Dimensions_, Options_, IndexType, MakePointer_> >
{
public:
typedef TensorFixedSize<Scalar_, Dimensions_, Options_, IndexType> Self;

49
unsupported/Eigen/CXX11/src/Tensor/TensorForcedEval.h
View File

@ -19,9 +19,15 @@ namespace Eigen {
*
*
*/
/// template <class> class MakePointer_ is added to convert the host pointer to the device pointer.
/// It is added due to the fact that for our device compiler T* is not allowed.
/// If we wanted to use the same Evaluator functions we have to convert that type to our pointer T.
/// This is done through our MakePointer_ class. By default the Type in the MakePointer_<T> is T* .
/// Therefore, by adding the default value, we managed to convert the type and it does not break any
/// existing code as its default value is T*.
namespace internal {
template<typename XprType>
struct traits<TensorForcedEvalOp<XprType> >
template<typename XprType, template <class> class MakePointer_>
struct traits<TensorForcedEvalOp<XprType, MakePointer_> >
{
// Type promotion to handle the case where the types of the lhs and the rhs are different.
typedef typename XprType::Scalar Scalar;
@ -36,26 +42,30 @@ struct traits<TensorForcedEvalOp<XprType> >
enum {
Flags = 0
};
template <class T>
struct MakePointer {
typedef typename MakePointer_<T>::Type Type;
};
};
template<typename XprType>
struct eval<TensorForcedEvalOp<XprType>, Eigen::Dense>
template<typename XprType, template <class> class MakePointer_>
struct eval<TensorForcedEvalOp<XprType, MakePointer_>, Eigen::Dense>
{
typedef const TensorForcedEvalOp<XprType>& type;
typedef const TensorForcedEvalOp<XprType, MakePointer_>& type;
};
template<typename XprType>
struct nested<TensorForcedEvalOp<XprType>, 1, typename eval<TensorForcedEvalOp<XprType> >::type>
template<typename XprType, template <class> class MakePointer_>
struct nested<TensorForcedEvalOp<XprType, MakePointer_>, 1, typename eval<TensorForcedEvalOp<XprType, MakePointer_> >::type>
{
typedef TensorForcedEvalOp<XprType> type;
typedef TensorForcedEvalOp<XprType, MakePointer_> type;
};
} // end namespace internal
template<typename XprType>
class TensorForcedEvalOp : public TensorBase<TensorForcedEvalOp<XprType>, ReadOnlyAccessors>
template<typename XprType, template <class> class MakePointer_>
class TensorForcedEvalOp : public TensorBase<TensorForcedEvalOp<XprType, MakePointer_>, ReadOnlyAccessors>
{
public:
typedef typename Eigen::internal::traits<TensorForcedEvalOp>::Scalar Scalar;
@ -77,10 +87,10 @@ class TensorForcedEvalOp : public TensorBase<TensorForcedEvalOp<XprType>, ReadOn
};
template<typename ArgType, typename Device>
struct TensorEvaluator<const TensorForcedEvalOp<ArgType>, Device>
template<typename ArgType, typename Device, template <class> class MakePointer_>
struct TensorEvaluator<const TensorForcedEvalOp<ArgType, MakePointer_>, Device>
{
typedef TensorForcedEvalOp<ArgType> XprType;
typedef TensorForcedEvalOp<ArgType, MakePointer_> XprType;
typedef typename ArgType::Scalar Scalar;
typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
typedef typename XprType::Index Index;
@ -96,6 +106,7 @@ struct TensorEvaluator<const TensorForcedEvalOp<ArgType>, Device>
};
EIGEN_DEVICE_FUNC TensorEvaluator(const XprType& op, const Device& device)
/// op_ is used for sycl
: m_impl(op.expression(), device), m_op(op.expression()), m_device(device), m_buffer(NULL)
{ }
@ -110,10 +121,10 @@ struct TensorEvaluator<const TensorForcedEvalOp<ArgType>, Device>
new(m_buffer+i) CoeffReturnType();
}
}
typedef TensorEvalToOp<const ArgType> EvalTo;
typedef TensorEvalToOp< const typename internal::remove_const<ArgType>::type > EvalTo;
EvalTo evalToTmp(m_buffer, m_op);
const bool PacketAccess = internal::IsVectorizable<Device, const ArgType>::value;
internal::TensorExecutor<const EvalTo, Device, PacketAccess>::run(evalToTmp, m_device);
internal::TensorExecutor<const EvalTo, typename internal::remove_const<Device>::type, PacketAccess>::run(evalToTmp, m_device);
return true;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
@ -136,13 +147,17 @@ struct TensorEvaluator<const TensorForcedEvalOp<ArgType>, Device>
return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketSize);
}
EIGEN_DEVICE_FUNC Scalar* data() const { return m_buffer; }
EIGEN_DEVICE_FUNC typename MakePointer<Scalar>::Type data() const { return m_buffer; }
/// required by sycl in order to extract the sycl accessor
const TensorEvaluator<ArgType, Device>& impl() { return m_impl; }
/// used by sycl in order to build the sycl buffer
const Device& device() const{return m_device;}
private:
TensorEvaluator<ArgType, Device> m_impl;
const ArgType m_op;
const Device& m_device;
CoeffReturnType* m_buffer;
typename MakePointer<CoeffReturnType>::Type m_buffer;
};

19
unsupported/Eigen/CXX11/src/Tensor/TensorForwardDeclarations.h
View File

@ -12,9 +12,19 @@
namespace Eigen {
// MakePointer class is used as a container of the adress space of the pointer
// on the host and on the device. From the host side it generates the T* pointer
// and when EIGEN_USE_SYCL is used it construct a buffer with a map_allocator to
// T* m_data on the host. It is always called on the device.
// Specialisation of MakePointer class for creating the sycl buffer with
// map_allocator.
template<class T> struct MakePointer{
typedef T* Type;
};
template<typename PlainObjectType, int Options_ = Unaligned, template <class> class MakePointer_ = MakePointer> class TensorMap;
template<typename Scalar_, int NumIndices_, int Options_ = 0, typename IndexType = DenseIndex> class Tensor;
template<typename Scalar_, typename Dimensions, int Options_ = 0, typename IndexType = DenseIndex> class TensorFixedSize;
template<typename PlainObjectType, int Options_ = Unaligned> class TensorMap;
template<typename Scalar_, typename Dimensions, int Options_ = 0, typename IndexType = DenseIndex, template <class> class MakePointer_ = MakePointer> class TensorFixedSize;
template<typename PlainObjectType> class TensorRef;
template<typename Derived, int AccessLevel> class TensorBase;
@ -52,8 +62,8 @@ template<typename Op, typename XprType> class TensorScanOp;
template<typename CustomUnaryFunc, typename XprType> class TensorCustomUnaryOp;
template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType> class TensorCustomBinaryOp;
template<typename XprType> class TensorEvalToOp;
template<typename XprType> class TensorForcedEvalOp;
template<typename XprType, template <class> class MakePointer_ = MakePointer> class TensorEvalToOp;
template<typename XprType, template <class> class MakePointer_ = MakePointer> class TensorForcedEvalOp;
template<typename ExpressionType, typename DeviceType> class TensorDevice;
template<typename Derived, typename Device> struct TensorEvaluator;
@ -61,6 +71,7 @@ template<typename Derived, typename Device> struct TensorEvaluator;
struct DefaultDevice;
struct ThreadPoolDevice;
struct GpuDevice;
struct SyclDevice;
enum FFTResultType {
RealPart = 0,

19
unsupported/Eigen/CXX11/src/Tensor/TensorMap.h
View File

@ -18,11 +18,16 @@ namespace Eigen {
* \brief A tensor expression mapping an existing array of data.
*
*/
template<typename PlainObjectType, int Options_> class TensorMap : public TensorBase<TensorMap<PlainObjectType, Options_> >
/// template <class> class MakePointer_ is added to convert the host pointer to the device pointer.
/// It is added due to the fact that for our device compiler T* is not allowed.
/// If we wanted to use the same Evaluator functions we have to convert that type to our pointer T.
/// This is done through our MakePointer_ class. By default the Type in the MakePointer_<T> is T* .
/// Therefore, by adding the default value, we managed to convert the type and it does not break any
/// existing code as its default value is T*.
template<typename PlainObjectType, int Options_, template <class> class MakePointer_> class TensorMap : public TensorBase<TensorMap<PlainObjectType, Options_, MakePointer_> >
{
public:
typedef TensorMap<PlainObjectType, Options_> Self;
typedef TensorMap<PlainObjectType, Options_, MakePointer_> Self;
typedef typename PlainObjectType::Base Base;
typedef typename Eigen::internal::nested<Self>::type Nested;
typedef typename internal::traits<PlainObjectType>::StorageKind StorageKind;
@ -36,7 +41,7 @@ template<typename PlainObjectType, int Options_> class TensorMap : public Tensor
Scalar *,
const Scalar *>::type
PointerType;*/
typedef Scalar* PointerType;
typedef typename MakePointer_<Scalar>::Type PointerType;
typedef PointerType PointerArgType;
static const int Options = Options_;
@ -109,9 +114,9 @@ template<typename PlainObjectType, int Options_> class TensorMap : public Tensor
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index size() const { return m_dimensions.TotalSize(); }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar* data() { return m_data; }
EIGEN_STRONG_INLINE PointerType data() { return m_data; }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar* data() const { return m_data; }
EIGEN_STRONG_INLINE const PointerType data() const { return m_data; }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& operator()(const array<Index, NumIndices>& indices) const
@ -307,7 +312,7 @@ template<typename PlainObjectType, int Options_> class TensorMap : public Tensor
}
private:
Scalar* m_data;
typename MakePointer_<Scalar>::Type m_data;
Dimensions m_dimensions;
};

50
unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h
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@ -423,15 +423,15 @@ struct TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType>, Device>
// Precompute output strides.
if (NumOutputDims > 0) {
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
m_outputStrides[0] = 1;
for (int i = 1; i < NumOutputDims; ++i) {
m_outputStrides[i] = m_outputStrides[i - 1] * m_dimensions[i - 1];
}
m_outputStrides[0] = 1;
for (int i = 1; i < NumOutputDims; ++i) {
m_outputStrides[i] = m_outputStrides[i - 1] * m_dimensions[i - 1];
}
} else {
m_outputStrides.back() = 1;
for (int i = NumOutputDims - 2; i >= 0; --i) {
m_outputStrides[i] = m_outputStrides[i + 1] * m_dimensions[i + 1];
}
m_outputStrides.back() = 1;
for (int i = NumOutputDims - 2; i >= 0; --i) {
m_outputStrides[i] = m_outputStrides[i + 1] * m_dimensions[i + 1];
}
}
}
@ -439,27 +439,27 @@ struct TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType>, Device>
if (NumInputDims > 0) {
array<Index, NumInputDims> input_strides;
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
input_strides[0] = 1;
for (int i = 1; i < NumInputDims; ++i) {
input_strides[i] = input_strides[i-1] * input_dims[i-1];
}
input_strides[0] = 1;
for (int i = 1; i < NumInputDims; ++i) {
input_strides[i] = input_strides[i-1] * input_dims[i-1];
}
} else {
input_strides.back() = 1;
for (int i = NumInputDims - 2; i >= 0; --i) {
input_strides[i] = input_strides[i + 1] * input_dims[i + 1];
}
input_strides.back() = 1;
for (int i = NumInputDims - 2; i >= 0; --i) {
input_strides[i] = input_strides[i + 1] * input_dims[i + 1];
}
}
int outputIndex = 0;
int reduceIndex = 0;
for (int i = 0; i < NumInputDims; ++i) {
if (m_reduced[i]) {
m_reducedStrides[reduceIndex] = input_strides[i];
++reduceIndex;
} else {
m_preservedStrides[outputIndex] = input_strides[i];
++outputIndex;
}
if (m_reduced[i]) {
m_reducedStrides[reduceIndex] = input_strides[i];
++reduceIndex;
} else {
m_preservedStrides[outputIndex] = input_strides[i];
++outputIndex;
}
}
}
@ -578,7 +578,7 @@ struct TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType>, Device>
Op reducer(m_reducer);
if (ReducingInnerMostDims || RunningFullReduction) {
const Index num_values_to_reduce =
(static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? m_preservedStrides[0] : m_preservedStrides[NumPreservedStrides - 1];
(static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? m_preservedStrides[0] : m_preservedStrides[NumPreservedStrides - 1];
return internal::InnerMostDimReducer<Self, Op>::reduce(*this, firstInput(index),
num_values_to_reduce, reducer);
} else {
@ -602,7 +602,7 @@ struct TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType>, Device>
EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
if (ReducingInnerMostDims) {
const Index num_values_to_reduce =
(static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? m_preservedStrides[0] : m_preservedStrides[NumPreservedStrides - 1];
(static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? m_preservedStrides[0] : m_preservedStrides[NumPreservedStrides - 1];
const Index firstIndex = firstInput(index);
for (Index i = 0; i < PacketSize; ++i) {
Op reducer(m_reducer);

77
unsupported/Eigen/CXX11/src/Tensor/TensorSycl.h Normal file
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@ -0,0 +1,77 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// 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/.
// General include header of SYCL target for Tensor Module
#ifndef UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_H
#define UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_H
#ifdef EIGEN_USE_SYCL
// global pointer to set different attribute state for a class
template <class T>
struct MakeGlobalPointer {
typedef typename cl::sycl::global_ptr<T>::pointer_t Type;
};
namespace Eigen {
namespace TensorSycl {
namespace internal {
/// This struct is used for special expression nodes with no operations (for example assign and selectOP).
struct NoOP;
template<bool IsConst, typename T> struct GetType{
typedef const T Type;
};
template<typename T> struct GetType<false, T>{
typedef T Type;
};
}
}
}
// tuple construction
#include "TensorSyclTuple.h"
// This file contains the PlaceHolder that replaces the actual data
#include "TensorSyclPlaceHolder.h"
#include "TensorSyclLeafCount.h"
// The index PlaceHolder takes the actual expression and replaces the actual
// data on it with the place holder. It uses the same pre-order expression tree
// traverse as the leaf count in order to give the right access number to each
// node in the expression
#include "TensorSyclPlaceHolderExpr.h"
// creation of an accessor tuple from a tuple of SYCL buffers
#include "TensorSyclExtractAccessor.h"
// actual data extraction using accessors
//#include "GetDeviceData.h"
// this is used to change the address space type in tensor map for GPU
#include "TensorSyclConvertToDeviceExpression.h"
// this is used to extract the functors
#include "TensorSyclExtractFunctors.h"
// this is used to create tensormap on the device
// this is used to construct the expression on the device
#include "TensorSyclExprConstructor.h"
// kernel execution using fusion
#include "TensorSyclRun.h"
#endif // end of EIGEN_USE_SYCL
#endif // UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_H

109
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@ -0,0 +1,109 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// 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/.
/*****************************************************************
* TensorSyclConvertToDeviceExpression.h
*
* \brief:
* Conversion from host pointer to device pointer
* inside leaf nodes of the expression.
*
*****************************************************************/
#ifndef UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_CONVERT_TO_DEVICE_EXPRESSION_HPP
#define UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_CONVERT_TO_DEVICE_EXPRESSION_HPP
namespace Eigen {
namespace TensorSycl {
namespace internal {
/// \struct ConvertToDeviceExpression
/// \brief This struct is used to convert the MakePointer in the host expression
/// to the MakeGlobalPointer for the device expression. For the leafNodes
/// containing the pointer. This is due to the fact that the address space of
/// the pointer T* is different on the host and the device.
template <typename Expr>
struct ConvertToDeviceExpression;
template<template<class...> class NonOpCategory, bool IsConst, typename... Args>
struct NonOpConversion{
typedef typename GetType<IsConst, NonOpCategory<typename ConvertToDeviceExpression<Args>::Type...> >::Type Type;
};
template<template<class, template <class> class > class NonOpCategory, bool IsConst, typename Args>
struct DeviceConvertor{
typedef typename GetType<IsConst, NonOpCategory<typename ConvertToDeviceExpression<Args>::Type, MakeGlobalPointer> >::Type Type;
};
/// specialisation of the \ref ConvertToDeviceExpression struct when the node
/// type is TensorMap
#define TENSORMAPCONVERT(CVQual)\
template <typename Scalar_, int Options_, int Options2_, int NumIndices_, typename IndexType_, template <class> class MakePointer_>\
struct ConvertToDeviceExpression<CVQual TensorMap<Tensor<Scalar_, NumIndices_, Options_, IndexType_>, Options2_, MakePointer_> > {\
typedef CVQual TensorMap<Tensor<Scalar_, NumIndices_, Options_, IndexType_>, Options2_, MakeGlobalPointer> Type;\
};
TENSORMAPCONVERT(const)
TENSORMAPCONVERT()
#undef TENSORMAPCONVERT
/// specialisation of the \ref ConvertToDeviceExpression struct when the node
/// type is TensorCwiseNullaryOp, TensorCwiseUnaryOp, TensorCwiseBinaryOp, TensorCwiseTernaryOp, TensorBroadcastingOp
#define CATEGORYCONVERT(CVQual)\
template <template<class, class...> class Category, typename OP, typename... subExprs>\
struct ConvertToDeviceExpression<CVQual Category<OP, subExprs...> > {\
typedef CVQual Category<OP, typename ConvertToDeviceExpression<subExprs>::Type... > Type;\
};
CATEGORYCONVERT(const)
CATEGORYCONVERT()
#undef CATEGORYCONVERT
/// specialisation of the \ref ConvertToDeviceExpression struct when the node
/// type is TensorCwiseSelectOp
#define SELECTOPCONVERT(CVQual, Res)\
template <typename IfExpr, typename ThenExpr, typename ElseExpr>\
struct ConvertToDeviceExpression<CVQual TensorSelectOp<IfExpr, ThenExpr, ElseExpr> >\
: NonOpConversion<TensorSelectOp, Res, IfExpr, ThenExpr, ElseExpr> {};
SELECTOPCONVERT(const, true)
SELECTOPCONVERT(, false)
#undef SELECTOPCONVERT
/// specialisation of the \ref ConvertToDeviceExpression struct when the node
/// type is const AssingOP
#define ASSIGNCONVERT(CVQual, Res)\
template <typename LHSExpr, typename RHSExpr>\
struct ConvertToDeviceExpression<CVQual TensorAssignOp<LHSExpr, RHSExpr> >\
: NonOpConversion<TensorAssignOp, Res, LHSExpr, RHSExpr>{};
ASSIGNCONVERT(const, true)
ASSIGNCONVERT(, false)
#undef ASSIGNCONVERT
/// specialisation of the \ref ConvertToDeviceExpression struct when the node
/// type is either TensorForcedEvalOp or TensorEvalToOp
#define KERNELBROKERCONVERT(CVQual, Res, ExprNode)\
template <typename Expr>\
struct ConvertToDeviceExpression<CVQual ExprNode<Expr> > \
: DeviceConvertor<ExprNode, Res, Expr>{};
KERNELBROKERCONVERT(const, true, TensorForcedEvalOp)
KERNELBROKERCONVERT(, false, TensorForcedEvalOp)
KERNELBROKERCONVERT(const, true, TensorEvalToOp)
KERNELBROKERCONVERT(, false, TensorEvalToOp)
#undef KERNELBROKERCONVERT
} // namespace internal
} // namespace TensorSycl
} // namespace Eigen
#endif // UNSUPPORTED_EIGEN_CXX1

213
unsupported/Eigen/CXX11/src/Tensor/TensorSyclExprConstructor.h Normal file
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@ -0,0 +1,213 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// 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/.
/*****************************************************************
* TensorSyclExprConstructor.h
*
* \brief:
* This file re-create an expression on the SYCL device in order
* to use the original tensor evaluator.
*
*****************************************************************/
#ifndef UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_EXPR_CONSTRUCTOR_HPP
#define UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_EXPR_CONSTRUCTOR_HPP
namespace Eigen {
namespace TensorSycl {
namespace internal {
/// this class is used by EvalToOp in order to create an lhs expression which is
/// a pointer from an accessor on device-only buffer
template <typename PtrType, size_t N, typename... Params>
struct EvalToLHSConstructor {
PtrType expr;
EvalToLHSConstructor(const utility::tuple::Tuple<Params...> &t): expr((&(*(utility::tuple::get<N>(t).get_pointer())))) {}
};
/// \struct ExprConstructor is used to reconstruct the expression on the device
/// and
/// recreate the expression with MakeGlobalPointer containing the device address
/// space for the TensorMap pointers used in eval function.
/// It receives the original expression type, the functor of the node, the tuple
/// of accessors, and the device expression type to re-instantiate the
/// expression tree for the device
template <typename OrigExpr, typename IndexExpr, typename... Params>
struct ExprConstructor;
/// specialisation of the \ref ExprConstructor struct when the node type is
/// TensorMap
#define TENSORMAP(CVQual)\
template <typename Scalar_, int Options_, int Options2_, int Options3_, int NumIndices_, typename IndexType_,\
template <class> class MakePointer_, size_t N, typename... Params>\
struct ExprConstructor< CVQual TensorMap<Tensor<Scalar_, NumIndices_, Options_, IndexType_>, Options2_, MakeGlobalPointer>,\
CVQual Eigen::internal::PlaceHolder<CVQual TensorMap<Tensor<Scalar_, NumIndices_, Options_, IndexType_>, Options3_, MakePointer_>, N>, Params...>{\
typedef CVQual TensorMap<Tensor<Scalar_, NumIndices_, Options_, IndexType_>, Options2_, MakeGlobalPointer> Type;\
Type expr;\
template <typename FuncDetector>\
ExprConstructor(FuncDetector &fd, const utility::tuple::Tuple<Params...> &t)\
: expr(Type((&(*(utility::tuple::get<N>(t).get_pointer()))), fd.dimensions())) {}\
};
TENSORMAP(const)
TENSORMAP()
#undef TENSORMAP
#define UNARYCATEGORY(CVQual)\
template <template<class, class> class UnaryCategory, typename OP, typename OrigRHSExpr, typename RHSExpr, typename... Params>\
struct ExprConstructor<CVQual UnaryCategory<OP, OrigRHSExpr>, CVQual UnaryCategory<OP, RHSExpr>, Params...> {\
typedef ExprConstructor<OrigRHSExpr, RHSExpr, Params...> my_type;\
my_type rhsExpr;\
typedef CVQual UnaryCategory<OP, typename my_type::Type> Type;\
Type expr;\
template <typename FuncDetector>\
ExprConstructor(FuncDetector &funcD, const utility::tuple::Tuple<Params...> &t)\
: rhsExpr(funcD.rhsExpr, t), expr(rhsExpr.expr, funcD.func) {}\
};
UNARYCATEGORY(const)
UNARYCATEGORY()
#undef UNARYCATEGORY
/// specialisation of the \ref ExprConstructor struct when the node type is
/// TensorBinaryOp
#define BINARYCATEGORY(CVQual)\
template <template<class, class, class> class BinaryCategory, typename OP, typename OrigLHSExpr, typename OrigRHSExpr, typename LHSExpr,\
typename RHSExpr, typename... Params>\
struct ExprConstructor<CVQual BinaryCategory<OP, OrigLHSExpr, OrigRHSExpr>, CVQual BinaryCategory<OP, LHSExpr, RHSExpr>, Params...> {\
typedef ExprConstructor<OrigLHSExpr, LHSExpr, Params...> my_left_type;\
typedef ExprConstructor<OrigRHSExpr, RHSExpr, Params...> my_right_type;\
typedef CVQual BinaryCategory<OP, typename my_left_type::Type, typename my_right_type::Type> Type;\
my_left_type lhsExpr;\
my_right_type rhsExpr;\
Type expr;\
template <typename FuncDetector>\
ExprConstructor(FuncDetector &funcD, const utility::tuple::Tuple<Params...> &t)\
: lhsExpr(funcD.lhsExpr, t),rhsExpr(funcD.rhsExpr, t), expr(lhsExpr.expr, rhsExpr.expr, funcD.func) {}\
};
BINARYCATEGORY(const)
BINARYCATEGORY()
#undef BINARYCATEGORY
/// specialisation of the \ref ExprConstructor struct when the node type is
/// TensorCwiseTernaryOp
#define TERNARYCATEGORY(CVQual)\
template <template <class, class, class, class> class TernaryCategory, typename OP, typename OrigArg1Expr, typename OrigArg2Expr,typename OrigArg3Expr,\
typename Arg1Expr, typename Arg2Expr, typename Arg3Expr, typename... Params>\
struct ExprConstructor<CVQual TernaryCategory<OP, OrigArg1Expr, OrigArg2Expr, OrigArg3Expr>, CVQual TernaryCategory<OP, Arg1Expr, Arg2Expr, Arg3Expr>, Params...> {\
typedef ExprConstructor<OrigArg1Expr, Arg1Expr, Params...> my_arg1_type;\
typedef ExprConstructor<OrigArg2Expr, Arg2Expr, Params...> my_arg2_type;\
typedef ExprConstructor<OrigArg3Expr, Arg3Expr, Params...> my_arg3_type;\
typedef CVQual TernaryCategory<OP, typename my_arg1_type::Type, typename my_arg2_type::Type, typename my_arg3_type::Type> Type;\
my_arg1_type arg1Expr;\
my_arg2_type arg2Expr;\
my_arg3_type arg3Expr;\
Type expr;\
template <typename FuncDetector>\
ExprConstructor(FuncDetector &funcD,const utility::tuple::Tuple<Params...> &t)\
: arg1Expr(funcD.arg1Expr, t), arg2Expr(funcD.arg2Expr, t), arg3Expr(funcD.arg3Expr, t), expr(arg1Expr.expr, arg2Expr.expr, arg3Expr.expr, funcD.func) {}\
};
TERNARYCATEGORY(const)
TERNARYCATEGORY()
#undef TERNARYCATEGORY
/// specialisation of the \ref ExprConstructor struct when the node type is
/// TensorCwiseSelectOp
#define SELECTOP(CVQual)\
template <typename OrigIfExpr, typename OrigThenExpr, typename OrigElseExpr, typename IfExpr, typename ThenExpr, typename ElseExpr, typename... Params>\
struct ExprConstructor< CVQual TensorSelectOp<OrigIfExpr, OrigThenExpr, OrigElseExpr>, CVQual TensorSelectOp<IfExpr, ThenExpr, ElseExpr>, Params...> {\
typedef ExprConstructor<OrigIfExpr, IfExpr, Params...> my_if_type;\
typedef ExprConstructor<OrigThenExpr, ThenExpr, Params...> my_then_type;\
typedef ExprConstructor<OrigElseExpr, ElseExpr, Params...> my_else_type;\
typedef CVQual TensorSelectOp<typename my_if_type::Type, typename my_then_type::Type, typename my_else_type::Type> Type;\
my_if_type ifExpr;\
my_then_type thenExpr;\
my_else_type elseExpr;\
Type expr;\
template <typename FuncDetector>\
ExprConstructor(FuncDetector &funcD, const utility::tuple::Tuple<Params...> &t)\
: ifExpr(funcD.ifExpr, t), thenExpr(funcD.thenExpr, t), elseExpr(funcD.elseExpr, t), expr(ifExpr.expr, thenExpr.expr, elseExpr.expr) {}\
};
SELECTOP(const)
SELECTOP()
#undef SELECTOP
/// specialisation of the \ref ExprConstructor struct when the node type is
/// const TensorAssignOp
#define ASSIGN(CVQual)\
template <typename OrigLHSExpr, typename OrigRHSExpr, typename LHSExpr, typename RHSExpr, typename... Params>\
struct ExprConstructor<CVQual TensorAssignOp<OrigLHSExpr, OrigRHSExpr>, CVQual TensorAssignOp<LHSExpr, RHSExpr>, Params...> {\
typedef ExprConstructor<OrigLHSExpr, LHSExpr, Params...> my_left_type;\
typedef ExprConstructor<OrigRHSExpr, RHSExpr, Params...> my_right_type;\
typedef CVQual TensorAssignOp<typename my_left_type::Type, typename my_right_type::Type> Type;\
my_left_type lhsExpr;\
my_right_type rhsExpr;\
Type expr;\
template <typename FuncDetector>\
ExprConstructor(FuncDetector &funcD, const utility::tuple::Tuple<Params...> &t)\
: lhsExpr(funcD.lhsExpr, t), rhsExpr(funcD.rhsExpr, t), expr(lhsExpr.expr, rhsExpr.expr) {}\
};
ASSIGN(const)
ASSIGN()
#undef ASSIGN
/// specialisation of the \ref ExprConstructor struct when the node type is
/// TensorEvalToOp
#define EVALTO(CVQual)\
template <typename OrigExpr, typename Expr, typename... Params>\
struct ExprConstructor<CVQual TensorEvalToOp<OrigExpr, MakeGlobalPointer>, CVQual TensorEvalToOp<Expr>, Params...> {\
typedef ExprConstructor<OrigExpr, Expr, Params...> my_expr_type;\
typedef typename TensorEvalToOp<OrigExpr, MakeGlobalPointer>::PointerType my_buffer_type;\
typedef CVQual TensorEvalToOp<typename my_expr_type::Type, MakeGlobalPointer> Type;\
my_expr_type nestedExpression;\
EvalToLHSConstructor<my_buffer_type, 0, Params...> buffer;\
Type expr;\
template <typename FuncDetector>\
ExprConstructor(FuncDetector &funcD, const utility::tuple::Tuple<Params...> &t)\
: nestedExpression(funcD.rhsExpr, t), buffer(t), expr(buffer.expr, nestedExpression.expr) {}\
};
EVALTO(const)
EVALTO()
#undef EVALTO
/// specialisation of the \ref ExprConstructor struct when the node type is
/// TensorForcedEvalOp
#define FORCEDEVAL(CVQual)\
template <typename OrigExpr, typename DevExpr, size_t N, typename... Params>\
struct ExprConstructor<CVQual TensorForcedEvalOp<OrigExpr, MakeGlobalPointer>,\
CVQual Eigen::internal::PlaceHolder<CVQual TensorForcedEvalOp<DevExpr>, N>, Params...> {\
typedef CVQual TensorMap<Tensor<typename TensorForcedEvalOp<DevExpr, MakeGlobalPointer>::Scalar,\
TensorForcedEvalOp<DevExpr, MakeGlobalPointer>::NumDimensions, 0, typename TensorForcedEvalOp<DevExpr>::Index>, 0, MakeGlobalPointer> Type;\
Type expr;\
template <typename FuncDetector>\
ExprConstructor(FuncDetector &fd, const utility::tuple::Tuple<Params...> &t)\
: expr(Type((&(*(utility::tuple::get<N>(t).get_pointer()))), fd.dimensions())) {}\
};
FORCEDEVAL(const)
FORCEDEVAL()
#undef FORCEDEVAL
/// template deduction for \ref ExprConstructor struct
template <typename OrigExpr, typename IndexExpr, typename FuncD, typename... Params>
auto createDeviceExpression(FuncD &funcD, const utility::tuple::Tuple<Params...> &t)
-> decltype(ExprConstructor<OrigExpr, IndexExpr, Params...>(funcD, t)) {
return ExprConstructor<OrigExpr, IndexExpr, Params...>(funcD, t);
}
}
}
} // namespace Eigen
#endif // UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_EXPR_CONSTRUCTOR_HPP

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@ -0,0 +1,201 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// 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/.
/*****************************************************************
* TensorSyclExtractAccessor.h
*
* \brief:
* ExtractAccessor takes Expression placeHolder expression and the tuple of sycl
* buffers as an input. Using pre-order tree traversal, ExtractAccessor
* recursively calls itself for its children in the expression tree. The
* leaf node in the PlaceHolder expression is nothing but a container preserving
* the order of the actual data in the tuple of sycl buffer. By invoking the
* extract accessor for the PlaceHolder<N>, an accessor is created for the Nth
* buffer in the tuple of buffers. This accessor is then added as an Nth
* element in the tuple of accessors. In this case we preserve the order of data
* in the expression tree.
*
* This is the specialisation of extract accessor method for different operation
* type in the PlaceHolder expression.
*
*****************************************************************/
#ifndef UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_EXTRACT_ACCESSOR_HPP
#define UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_EXTRACT_ACCESSOR_HPP
namespace Eigen {
namespace TensorSycl {
namespace internal {
/// \struct ExtractAccessor: Extract Accessor Class is used to extract the
/// accessor from a buffer.
/// Depending on the type of the leaf node we can get a read accessor or a
/// read_write accessor
template <typename Evaluator>
struct ExtractAccessor;
struct AccessorConstructor{
template<typename Arg> static inline auto getTuple(cl::sycl::handler& cgh, Arg eval)
-> decltype(ExtractAccessor<Arg>::getTuple(cgh, eval)) {
return ExtractAccessor<Arg>::getTuple(cgh, eval);
}
template<typename Arg1, typename Arg2> static inline auto getTuple(cl::sycl::handler& cgh, Arg1 eval1, Arg2 eval2)
-> decltype(utility::tuple::append(ExtractAccessor<Arg1>::getTuple(cgh, eval1), ExtractAccessor<Arg2>::getTuple(cgh, eval2))) {
return utility::tuple::append(ExtractAccessor<Arg1>::getTuple(cgh, eval1), ExtractAccessor<Arg2>::getTuple(cgh, eval2));
}
template<typename Arg1, typename Arg2, typename Arg3> static inline auto getTuple(cl::sycl::handler& cgh, Arg1 eval1 , Arg2 eval2 , Arg3 eval3)
-> decltype(utility::tuple::append(ExtractAccessor<Arg1>::getTuple(cgh, eval1),utility::tuple::append(ExtractAccessor<Arg2>::getTuple(cgh, eval2), ExtractAccessor<Arg3>::getTuple(cgh, eval3)))) {
return utility::tuple::append(ExtractAccessor<Arg1>::getTuple(cgh, eval1),utility::tuple::append(ExtractAccessor<Arg2>::getTuple(cgh, eval2), ExtractAccessor<Arg3>::getTuple(cgh, eval3)));
}
template< cl::sycl::access::mode AcM, typename Arg> static inline auto getAccessor(cl::sycl::handler& cgh, Arg eval)
-> decltype(utility::tuple::make_tuple( eval.device().template get_sycl_accessor<AcM, true,
typename Eigen::internal::remove_all<typename Arg::CoeffReturnType>::type>(eval.dimensions().TotalSize(), cgh,eval.data()))){
return utility::tuple::make_tuple(eval.device().template get_sycl_accessor<AcM, true, typename Eigen::internal::remove_all<typename Arg::CoeffReturnType>::type>(eval.dimensions().TotalSize(), cgh,eval.data()));
}
};
/// specialisation of the \ref ExtractAccessor struct when the node type is
/// const TensorCwiseNullaryOp, const TensorCwiseUnaryOp and const TensorBroadcastingOp
template <template<class, class> class UnaryCategory, typename OP, typename RHSExpr, typename Dev>
struct ExtractAccessor<TensorEvaluator<const UnaryCategory<OP, RHSExpr>, Dev> > {
static inline auto getTuple(cl::sycl::handler& cgh, const TensorEvaluator<const UnaryCategory<OP, RHSExpr>, Dev> eval)
-> decltype(AccessorConstructor::getTuple(cgh, eval.impl())){
return AccessorConstructor::getTuple(cgh, eval.impl());
}
};
/// specialisation of the \ref ExtractAccessor struct when the node type is
/// TensorCwiseNullaryOp, TensorCwiseUnaryOp and TensorBroadcastingOp
template <template<class, class> class UnaryCategory, typename OP, typename RHSExpr, typename Dev>
struct ExtractAccessor<TensorEvaluator<UnaryCategory<OP, RHSExpr>, Dev> >
: ExtractAccessor<TensorEvaluator<const UnaryCategory<OP, RHSExpr>, Dev> > {};
/// specialisation of the \ref ExtractAccessor struct when the node type is
/// const TensorCwiseBinaryOp
template <template<class, class, class> class BinaryCategory, typename OP, typename LHSExpr, typename RHSExpr, typename Dev>
struct ExtractAccessor<TensorEvaluator<const BinaryCategory<OP, LHSExpr, RHSExpr>, Dev> > {
static inline auto getTuple(cl::sycl::handler& cgh, const TensorEvaluator<const BinaryCategory<OP, LHSExpr, RHSExpr>, Dev> eval)
-> decltype(AccessorConstructor::getTuple(cgh, eval.left_impl(), eval.right_impl())){
return AccessorConstructor::getTuple(cgh, eval.left_impl(), eval.right_impl());
}
};
/// specialisation of the \ref ExtractAccessor struct when the node type is
/// TensorCwiseBinaryOp
template <template<class, class, class> class BinaryCategory, typename OP, typename LHSExpr, typename RHSExpr, typename Dev>
struct ExtractAccessor<TensorEvaluator<BinaryCategory<OP, LHSExpr, RHSExpr>, Dev> >
: ExtractAccessor<TensorEvaluator<const BinaryCategory<OP, LHSExpr, RHSExpr>, Dev> >{};
/// specialisation of the \ref ExtractAccessor struct when the node type is
/// const TensorCwiseTernaryOp
template <template<class, class, class, class> class TernaryCategory, typename OP, typename Arg1Expr, typename Arg2Expr, typename Arg3Expr, typename Dev>
struct ExtractAccessor<TensorEvaluator<const TernaryCategory<OP, Arg1Expr, Arg2Expr, Arg3Expr>, Dev> > {
static inline auto getTuple(cl::sycl::handler& cgh, const TensorEvaluator<const TernaryCategory<OP, Arg1Expr, Arg2Expr, Arg3Expr>, Dev> eval)
-> decltype(AccessorConstructor::getTuple(cgh, eval.arg1Impl(), eval.arg2Impl(), eval.arg3Impl())){
return AccessorConstructor::getTuple(cgh, eval.arg1Impl(), eval.arg2Impl(), eval.arg3Impl());
}
};
/// specialisation of the \ref ExtractAccessor struct when the node type is
/// TensorCwiseTernaryOp
template <template<class, class, class, class> class TernaryCategory, typename OP, typename Arg1Expr, typename Arg2Expr, typename Arg3Expr, typename Dev>
struct ExtractAccessor<TensorEvaluator<TernaryCategory<OP, Arg1Expr, Arg2Expr, Arg3Expr>, Dev> >
: ExtractAccessor<TensorEvaluator<const TernaryCategory<OP, Arg1Expr, Arg2Expr, Arg3Expr>, Dev> >{};
/// specialisation of the \ref ExtractAccessor struct when the node type is
/// const TensorCwiseSelectOp. This is a special case where there is no OP
template <typename IfExpr, typename ThenExpr, typename ElseExpr, typename Dev>
struct ExtractAccessor<TensorEvaluator<const TensorSelectOp<IfExpr, ThenExpr, ElseExpr>, Dev> > {
static inline auto getTuple(cl::sycl::handler& cgh, const TensorEvaluator<const TensorSelectOp<IfExpr, ThenExpr, ElseExpr>, Dev> eval)
-> decltype(AccessorConstructor::getTuple(cgh, eval.cond_impl(), eval.then_impl(), eval.else_impl())){
return AccessorConstructor::getTuple(cgh, eval.cond_impl(), eval.then_impl(), eval.else_impl());
}
};
/// specialisation of the \ref ExtractAccessor struct when the node type is
/// TensorCwiseSelectOp. This is a special case where there is no OP
template <typename IfExpr, typename ThenExpr, typename ElseExpr, typename Dev>
struct ExtractAccessor<TensorEvaluator<TensorSelectOp<IfExpr, ThenExpr, ElseExpr>, Dev> >
: ExtractAccessor<TensorEvaluator<const TensorSelectOp<IfExpr, ThenExpr, ElseExpr>, Dev> >{};
/// specialisation of the \ref ExtractAccessor struct when the node type is
/// const TensorAssignOp
template <typename LHSExpr, typename RHSExpr, typename Dev>
struct ExtractAccessor<TensorEvaluator<const TensorAssignOp<LHSExpr, RHSExpr>, Dev> > {
static inline auto getTuple(cl::sycl::handler& cgh, const TensorEvaluator<const TensorAssignOp<LHSExpr, RHSExpr>, Dev> eval)
-> decltype(AccessorConstructor::getTuple(cgh, eval.left_impl(), eval.right_impl())){
return AccessorConstructor::getTuple(cgh, eval.left_impl(), eval.right_impl());
}
};
/// specialisation of the \ref ExtractAccessor struct when the node type is
/// TensorAssignOp
template <typename LHSExpr, typename RHSExpr, typename Dev>
struct ExtractAccessor<TensorEvaluator<TensorAssignOp<LHSExpr, RHSExpr>, Dev> >
: ExtractAccessor<TensorEvaluator<const TensorAssignOp<LHSExpr, RHSExpr>, Dev> >{};
/// specialisation of the \ref ExtractAccessor struct when the node type is
/// const TensorMap
#define TENSORMAPEXPR(CVQual, ACCType)\
template <typename PlainObjectType, int Options_, typename Dev>\
struct ExtractAccessor<TensorEvaluator<CVQual TensorMap<PlainObjectType, Options_>, Dev> > {\
static inline auto getTuple(cl::sycl::handler& cgh,const TensorEvaluator<CVQual TensorMap<PlainObjectType, Options_>, Dev> eval)\
-> decltype(AccessorConstructor::template getAccessor<ACCType>(cgh, eval)){\
return AccessorConstructor::template getAccessor<ACCType>(cgh, eval);\
}\
};
TENSORMAPEXPR(const, cl::sycl::access::mode::read)
TENSORMAPEXPR(, cl::sycl::access::mode::read_write)
#undef TENSORMAPEXPR
/// specialisation of the \ref ExtractAccessor struct when the node type is
/// const TensorForcedEvalOp
template <typename Expr, typename Dev>
struct ExtractAccessor<TensorEvaluator<const TensorForcedEvalOp<Expr>, Dev> > {
static inline auto getTuple(cl::sycl::handler& cgh, const TensorEvaluator<const TensorForcedEvalOp<Expr>, Dev> eval)
-> decltype(AccessorConstructor::template getAccessor<cl::sycl::access::mode::read>(cgh, eval)){
return AccessorConstructor::template getAccessor<cl::sycl::access::mode::read>(cgh, eval);
}
};
/// specialisation of the \ref ExtractAccessor struct when the node type is
/// TensorForcedEvalOp
template <typename Expr, typename Dev>
struct ExtractAccessor<TensorEvaluator<TensorForcedEvalOp<Expr>, Dev> >
: ExtractAccessor<TensorEvaluator<const TensorForcedEvalOp<Expr>, Dev> >{};
/// specialisation of the \ref ExtractAccessor struct when the node type is
/// const TensorEvalToOp
template <typename Expr, typename Dev>
struct ExtractAccessor<TensorEvaluator<const TensorEvalToOp<Expr>, Dev> > {
static inline auto getTuple(cl::sycl::handler& cgh,const TensorEvaluator<const TensorEvalToOp<Expr>, Dev> eval)
-> decltype(utility::tuple::append(AccessorConstructor::template getAccessor<cl::sycl::access::mode::write>(cgh, eval), AccessorConstructor::getTuple(cgh, eval.impl()))){
return utility::tuple::append(AccessorConstructor::template getAccessor<cl::sycl::access::mode::write>(cgh, eval), AccessorConstructor::getTuple(cgh, eval.impl()));
}
};
/// specialisation of the \ref ExtractAccessor struct when the node type is
/// TensorEvalToOp
template <typename Expr, typename Dev>
struct ExtractAccessor<TensorEvaluator<TensorEvalToOp<Expr>, Dev> >
: ExtractAccessor<TensorEvaluator<const TensorEvalToOp<Expr>, Dev> >{};
/// template deduction for \ref ExtractAccessor
template <typename Evaluator>
auto createTupleOfAccessors(cl::sycl::handler& cgh, const Evaluator& expr)
-> decltype(ExtractAccessor<Evaluator>::getTuple(cgh, expr)) {
return ExtractAccessor<Evaluator>::getTuple(cgh, expr);
}
}
}
}
#endif // UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_EXTRACT_ACCESSOR_HPP

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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// 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/.
/*****************************************************************
* TensorSyclextractFunctors.h
*
* \brief:
* Used to extract all the functors allocated to each node of the expression
*tree.
*
*****************************************************************/
#ifndef UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_EXTRACT_FUNCTORS_HPP
#define UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_EXTRACT_FUNCTORS_HPP
namespace Eigen {
namespace TensorSycl {
namespace internal {
/// \struct FunctorExtractor: This struct is used to extract the functors
/// constructed on
/// the host-side, to pack them and reuse them in reconstruction of the
/// expression on the device.
/// We have to do that as in Eigen the functors are not stateless so we cannot
/// re-instantiate them on the device.
/// We have to pass instantiated functors to the device.
// This struct is used for leafNode (TensorMap) and nodes behaving like leafNode (TensorForcedEval).
template <typename Evaluator> struct FunctorExtractor{
typedef typename Evaluator::Dimensions Dimensions;
const Dimensions m_dimensions;
const Dimensions& dimensions() const { return m_dimensions; }
FunctorExtractor(const Evaluator& expr)
: m_dimensions(expr.dimensions()) {}
};
/// specialisation of the \ref FunctorExtractor struct when the node type is
/// const TensorCwiseNullaryOp, const TensorCwiseUnaryOp, and const TensorBroadcastingOp
template <template <class, class> class UnaryCategory, typename OP, typename RHSExpr, typename Dev>
struct FunctorExtractor<TensorEvaluator<const UnaryCategory<OP, RHSExpr>, Dev> > {
FunctorExtractor<TensorEvaluator<RHSExpr, Dev> > rhsExpr;
OP func;
FunctorExtractor(const TensorEvaluator<const UnaryCategory<OP, RHSExpr>, Dev>& expr)
: rhsExpr(expr.impl()), func(expr.functor()) {}
};
/// specialisation of the \ref FunctorExtractor struct when the node type is
/// TensorCwiseNullaryOp, TensorCwiseUnaryOp, and TensorBroadcastingOp
template <template <class, class> class UnaryCategory, typename OP, typename RHSExpr, typename Dev>
struct FunctorExtractor<TensorEvaluator<UnaryCategory<OP, RHSExpr>, Dev> >
: FunctorExtractor<TensorEvaluator<const UnaryCategory<OP, RHSExpr>, Dev> >{};
/// specialisation of the \ref FunctorExtractor struct when the node type is
/// const TensorCwiseBinaryOp
template <template<class, class, class> class BinaryCategory, typename OP, typename LHSExpr, typename RHSExpr, typename Dev>
struct FunctorExtractor<TensorEvaluator<const BinaryCategory<OP, LHSExpr, RHSExpr>, Dev> > {
FunctorExtractor<TensorEvaluator<LHSExpr, Dev> > lhsExpr;
FunctorExtractor<TensorEvaluator<RHSExpr, Dev> > rhsExpr;
OP func;
FunctorExtractor(const TensorEvaluator<const BinaryCategory<OP, LHSExpr, RHSExpr>, Dev>& expr)
: lhsExpr(expr.left_impl()),rhsExpr(expr.right_impl()),func(expr.functor()) {}
};
/// specialisation of the \ref FunctorExtractor struct when the node type is
/// const TensorCwiseBinaryOp
template <template <class, class, class> class BinaryCategory, typename OP, typename LHSExpr, typename RHSExpr, typename Dev>
struct FunctorExtractor<TensorEvaluator<BinaryCategory<OP, LHSExpr, RHSExpr>, Dev> >
: FunctorExtractor<TensorEvaluator<const BinaryCategory<OP, LHSExpr, RHSExpr>, Dev> >{};
/// specialisation of the \ref FunctorExtractor struct when the node type is
/// const TensorCwiseTernaryOp
template <template <class, class, class, class> class TernaryCategory, typename OP, typename Arg1Expr, typename Arg2Expr, typename Arg3Expr,typename Dev>
struct FunctorExtractor<TensorEvaluator<const TernaryCategory<OP, Arg1Expr, Arg2Expr, Arg3Expr>, Dev> > {
FunctorExtractor<TensorEvaluator<Arg1Expr, Dev> > arg1Expr;
FunctorExtractor<TensorEvaluator<Arg2Expr, Dev> > arg2Expr;
FunctorExtractor<TensorEvaluator<Arg3Expr, Dev> > arg3Expr;
OP func;
FunctorExtractor(const TensorEvaluator<const TernaryCategory<OP, Arg1Expr, Arg2Expr, Arg3Expr>, Dev>& expr)
: arg1Expr(expr.arg1Impl()), arg2Expr(expr.arg2Impl()), arg3Expr(expr.arg3Impl()), func(expr.functor()) {}
};
/// specialisation of the \ref FunctorExtractor struct when the node type is
/// TensorCwiseTernaryOp
template <template <class, class, class, class> class TernaryCategory, typename OP, typename Arg1Expr, typename Arg2Expr, typename Arg3Expr, typename Dev>
struct FunctorExtractor<TensorEvaluator< TernaryCategory<OP, Arg1Expr, Arg2Expr, Arg3Expr>, Dev> >
:FunctorExtractor<TensorEvaluator<const TernaryCategory<OP, Arg1Expr, Arg2Expr, Arg3Expr>, Dev> >{};
/// specialisation of the \ref FunctorExtractor struct when the node type is
/// const TensorCwiseSelectOp. This is an specialisation without OP so it has to be separated.
template <typename IfExpr, typename ThenExpr, typename ElseExpr, typename Dev>
struct FunctorExtractor< TensorEvaluator<const TensorSelectOp<IfExpr, ThenExpr, ElseExpr>, Dev> > {
FunctorExtractor<TensorEvaluator<IfExpr, Dev> > ifExpr;
FunctorExtractor<TensorEvaluator<ThenExpr, Dev> > thenExpr;
FunctorExtractor<TensorEvaluator<ElseExpr, Dev> > elseExpr;
FunctorExtractor(const TensorEvaluator<const TensorSelectOp<IfExpr, ThenExpr, ElseExpr>, Dev>& expr)
: ifExpr(expr.cond_impl()), thenExpr(expr.then_impl()), elseExpr(expr.else_impl()) {}
};
/// specialisation of the \ref FunctorExtractor struct when the node type is
/// TensorCwiseSelectOp. This is an specialisation without OP so it has to be separated
template <typename IfExpr, typename ThenExpr, typename ElseExpr, typename Dev>
struct FunctorExtractor<TensorEvaluator<TensorSelectOp<IfExpr, ThenExpr, ElseExpr>, Dev> >
:FunctorExtractor< TensorEvaluator<const TensorSelectOp<IfExpr, ThenExpr, ElseExpr>, Dev> > {};
/// specialisation of the \ref FunctorExtractor struct when the node type is
/// const TensorAssignOp. This is an specialisation without OP so it has to be separated.
template <typename LHSExpr, typename RHSExpr, typename Dev>
struct FunctorExtractor<TensorEvaluator<const TensorAssignOp<LHSExpr, RHSExpr>, Dev> > {
FunctorExtractor<TensorEvaluator<LHSExpr, Dev> > lhsExpr;
FunctorExtractor<TensorEvaluator<RHSExpr, Dev> > rhsExpr;
FunctorExtractor(const TensorEvaluator<const TensorAssignOp<LHSExpr, RHSExpr>, Dev>& expr)
: lhsExpr(expr.left_impl()), rhsExpr(expr.right_impl()) {}
};
/// specialisation of the \ref FunctorExtractor struct when the node type is
/// TensorAssignOp. This is an specialisation without OP so it has to be separated.
template <typename LHSExpr, typename RHSExpr, typename Dev>
struct FunctorExtractor<TensorEvaluator<TensorAssignOp<LHSExpr, RHSExpr>, Dev> >
:FunctorExtractor<TensorEvaluator<const TensorAssignOp<LHSExpr, RHSExpr>, Dev> >{};
/// specialisation of the \ref FunctorExtractor struct when the node type is
/// const TensorEvalToOp, This is an specialisation without OP so it has to be separated.
template <typename RHSExpr, typename Dev>
struct FunctorExtractor<TensorEvaluator<const TensorEvalToOp<RHSExpr>, Dev> > {
FunctorExtractor<TensorEvaluator<RHSExpr, Dev> > rhsExpr;
FunctorExtractor(const TensorEvaluator<const TensorEvalToOp<RHSExpr>, Dev>& expr)
: rhsExpr(expr.impl()) {}
};
/// specialisation of the \ref FunctorExtractor struct when the node type is
/// TensorEvalToOp. This is a specialisation without OP so it has to be separated.
template <typename RHSExpr, typename Dev>
struct FunctorExtractor<TensorEvaluator<TensorEvalToOp<RHSExpr>, Dev> >
: FunctorExtractor<TensorEvaluator<const TensorEvalToOp<RHSExpr>, Dev> > {};
/// template deduction function for FunctorExtractor
template <typename Evaluator>
auto inline extractFunctors(const Evaluator& evaluator)-> FunctorExtractor<Evaluator> {
return FunctorExtractor<Evaluator>(evaluator);
}
} // namespace internal
} // namespace TensorSycl
} // namespace Eigen
#endif // UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_EXTRACT_FUNCTORS_HPP

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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// 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/.
/*****************************************************************
* TensorSyclLeafCount.h
*
* \brief:
* The leaf count used the pre-order expression tree traverse in order to name
* count the number of leaf nodes in the expression
*
*****************************************************************/
#ifndef UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_LEAF_COUNT_HPP
#define UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_LEAF_COUNT_HPP
namespace Eigen {
namespace TensorSycl {
namespace internal {
/// \brief LeafCount used to counting terminal nodes. The total number of
/// leaf nodes is used by MakePlaceHolderExprHelper to find the order
/// of the leaf node in a expression tree at compile time.
template <typename Expr>
struct LeafCount;
template<typename... Args> struct CategoryCount;
template<> struct CategoryCount<>
{
static const size_t Count =0;
};
template<typename Arg, typename... Args>
struct CategoryCount<Arg,Args...>{
static const size_t Count = LeafCount<Arg>::Count + CategoryCount<Args...>::Count;
};
/// specialisation of the \ref LeafCount struct when the node type is const
/// TensorMap
template <typename PlainObjectType, int Options_, template <class> class MakePointer_>
struct LeafCount<const TensorMap<PlainObjectType, Options_, MakePointer_> > {
static const size_t Count =1;
};
/// specialisation of the \ref LeafCount struct when the node type is TensorMap
template <typename PlainObjectType, int Options_, template <class> class MakePointer_>
struct LeafCount<TensorMap<PlainObjectType, Options_, MakePointer_> > :LeafCount<const TensorMap<PlainObjectType, Options_, MakePointer_> >{};
// const TensorCwiseUnaryOp, const TensorCwiseNullaryOp, const TensorCwiseBinaryOp, const TensorCwiseTernaryOp, and Const TensorBroadcastingOp
template <template <class, class...> class CategoryExpr, typename OP, typename... RHSExpr>
struct LeafCount<const CategoryExpr<OP, RHSExpr...> >: CategoryCount<RHSExpr...> {};
// TensorCwiseUnaryOp, TensorCwiseNullaryOp, TensorCwiseBinaryOp, TensorCwiseTernaryOp, and TensorBroadcastingOp
template <template <class, class...> class CategoryExpr, typename OP, typename... RHSExpr>
struct LeafCount<CategoryExpr<OP, RHSExpr...> > :LeafCount<const CategoryExpr<OP, RHSExpr...> >{};
/// specialisation of the \ref LeafCount struct when the node type is
/// const TensorSelectOp is an exception
template <typename IfExpr, typename ThenExpr, typename ElseExpr>
struct LeafCount<const TensorSelectOp<IfExpr, ThenExpr, ElseExpr> > : CategoryCount<IfExpr, ThenExpr, ElseExpr> {};
/// specialisation of the \ref LeafCount struct when the node type is
/// TensorSelectOp
template <typename IfExpr, typename ThenExpr, typename ElseExpr>
struct LeafCount<TensorSelectOp<IfExpr, ThenExpr, ElseExpr> >: LeafCount<const TensorSelectOp<IfExpr, ThenExpr, ElseExpr> > {};
/// specialisation of the \ref LeafCount struct when the node type is const
/// TensorAssignOp
template <typename LHSExpr, typename RHSExpr>
struct LeafCount<const TensorAssignOp<LHSExpr, RHSExpr> >: CategoryCount<LHSExpr,RHSExpr> {};
/// specialisation of the \ref LeafCount struct when the node type is
/// TensorAssignOp is an exception. It is not the same as Unary
template <typename LHSExpr, typename RHSExpr>
struct LeafCount<TensorAssignOp<LHSExpr, RHSExpr> > :LeafCount<const TensorAssignOp<LHSExpr, RHSExpr> >{};
/// specialisation of the \ref LeafCount struct when the node type is const
/// TensorForcedEvalOp
template <typename Expr>
struct LeafCount<const TensorForcedEvalOp<Expr> > {
static const size_t Count =1;
};
/// specialisation of the \ref LeafCount struct when the node type is
/// TensorForcedEvalOp
template <typename Expr>
struct LeafCount<TensorForcedEvalOp<Expr> >: LeafCount<const TensorForcedEvalOp<Expr> > {};
/// specialisation of the \ref LeafCount struct when the node type is const
/// TensorEvalToOp
template <typename Expr>
struct LeafCount<const TensorEvalToOp<Expr> > {
static const size_t Count = 1 + CategoryCount<Expr>::Count;
};
/// specialisation of the \ref LeafCount struct when the node type is
/// TensorEvalToOp
template <typename Expr>
struct LeafCount<TensorEvalToOp<Expr> >: LeafCount<const TensorEvalToOp<Expr> >{};
}
}
} // namespace Eigen
#endif // UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_LEAF_COUNT_HPP

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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// 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/.
/*****************************************************************
* TensorSyclPlaceHolder.h
*
* \brief:
* The PlaceHolder expression are nothing but a container preserving
* the order of actual data in the tuple of sycl buffer.
*
*****************************************************************/
#ifndef UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_PLACEHOLDER_HPP
#define UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_PLACEHOLDER_HPP
namespace Eigen {
namespace internal {
/// \struct PlaceHolder
/// \brief PlaceHolder is used to replace the \ref TensorMap in the expression
/// tree.
/// PlaceHolder contains the order of the leaf node in the expression tree.
template <typename Scalar, size_t N>
struct PlaceHolder {
static constexpr size_t I = N;
typedef Scalar Type;
};
/// \brief specialisation of the PlaceHolder node for const TensorMap
#define TENSORMAPPLACEHOLDER(CVQual)\
template <typename PlainObjectType, int Options_, template <class> class MakePointer_, size_t N>\
struct PlaceHolder<CVQual TensorMap<PlainObjectType, Options_, MakePointer_>, N> {\
static const size_t I = N;\
typedef CVQual TensorMap<PlainObjectType, Options_, MakePointer_> Type;\
typedef typename Type::Self Self;\
typedef typename Type::Base Base;\
typedef typename Type::Nested Nested;\
typedef typename Type::StorageKind StorageKind;\
typedef typename Type::Index Index;\
typedef typename Type::Scalar Scalar;\
typedef typename Type::RealScalar RealScalar;\
typedef typename Type::CoeffReturnType CoeffReturnType;\
};
TENSORMAPPLACEHOLDER(const)
TENSORMAPPLACEHOLDER()
#undef TENSORMAPPLACEHOLDER
/// \brief specialisation of the PlaceHolder node for TensorForcedEvalOp. The
/// TensorForcedEvalOp acts as a leaf node for its parent node.
#define TENSORFORCEDEVALPLACEHOLDER(CVQual)\
template <typename Expression, size_t N>\
struct PlaceHolder<CVQual TensorForcedEvalOp<Expression>, N> {\
static const size_t I = N;\
typedef CVQual TensorForcedEvalOp<Expression> Type;\
typedef typename Type::Nested Nested;\
typedef typename Type::StorageKind StorageKind;\
typedef typename Type::Index Index;\
typedef typename Type::Scalar Scalar;\
typedef typename Type::Packet Packet;\
typedef typename Type::RealScalar RealScalar;\
typedef typename Type::CoeffReturnType CoeffReturnType;\
typedef typename Type::PacketReturnType PacketReturnType;\
};
TENSORFORCEDEVALPLACEHOLDER(const)
TENSORFORCEDEVALPLACEHOLDER()
#undef TENSORFORCEDEVALPLACEHOLDER
template <typename PlainObjectType, int Options_, template <class> class Makepointer_, size_t N>
struct traits<PlaceHolder<const TensorMap<PlainObjectType, Options_, Makepointer_>, N> >: public traits<PlainObjectType> {
typedef traits<PlainObjectType> BaseTraits;
typedef typename BaseTraits::Scalar Scalar;
typedef typename BaseTraits::StorageKind StorageKind;
typedef typename BaseTraits::Index Index;
static const int NumDimensions = BaseTraits::NumDimensions;
static const int Layout = BaseTraits::Layout;
enum {
Options = Options_,
Flags = BaseTraits::Flags,
};
};
template <typename PlainObjectType, int Options_, template <class> class Makepointer_, size_t N>
struct traits<PlaceHolder<TensorMap<PlainObjectType, Options_, Makepointer_>, N> >
: traits<PlaceHolder<const TensorMap<PlainObjectType, Options_, Makepointer_>, N> > {};
} // end namespace internal
} // end namespoace Eigen
#endif // UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_PLACEHOLDER_HPP

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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// 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/.
/*****************************************************************
* TensorSyclPlaceHolderExpr.h
*
* \brief:
* This is the specialisation of the placeholder expression based on the
* operation type
*
*****************************************************************/
#ifndef UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_PLACEHOLDER_EXPR_HPP
#define UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_PLACEHOLDER_EXPR_HPP
namespace Eigen {
namespace TensorSycl {
namespace internal {
/// \sttruct PlaceHolderExpression
/// \brief it is used to create the PlaceHolder expression. The PlaceHolder
/// expression is a copy of expression type in which the TensorMap of the has
/// been replaced with PlaceHolder.
template <typename Expr, size_t N>
struct PlaceHolderExpression;
template<size_t N, typename... Args>
struct CalculateIndex;
template<size_t N, typename Arg>
struct CalculateIndex<N, Arg>{
typedef typename PlaceHolderExpression<Arg, N>::Type ArgType;
typedef utility::tuple::Tuple<ArgType> ArgsTuple;
};
template<size_t N, typename Arg1, typename Arg2>
struct CalculateIndex<N, Arg1, Arg2>{
static const size_t Arg2LeafCount = LeafCount<Arg2>::Count;
typedef typename PlaceHolderExpression<Arg1, N - Arg2LeafCount>::Type Arg1Type;
typedef typename PlaceHolderExpression<Arg2, N>::Type Arg2Type;
typedef utility::tuple::Tuple<Arg1Type, Arg2Type> ArgsTuple;
};
template<size_t N, typename Arg1, typename Arg2, typename Arg3>
struct CalculateIndex<N, Arg1, Arg2, Arg3> {
static const size_t Arg3LeafCount = LeafCount<Arg3>::Count;
static const size_t Arg2LeafCount = LeafCount<Arg2>::Count;
typedef typename PlaceHolderExpression<Arg1, N - Arg3LeafCount - Arg2LeafCount>::Type Arg1Type;
typedef typename PlaceHolderExpression<Arg2, N - Arg3LeafCount>::Type Arg2Type;
typedef typename PlaceHolderExpression<Arg3, N>::Type Arg3Type;
typedef utility::tuple::Tuple<Arg1Type, Arg2Type, Arg3Type> ArgsTuple;
};
template<template<class...> class Category , class OP, class TPL>
struct CategoryHelper;
template<template<class...> class Category , class OP, class ...T >
struct CategoryHelper<Category, OP, utility::tuple::Tuple<T...> > {
typedef Category<OP, T... > Type;
};
template<template<class...> class Category , class ...T >
struct CategoryHelper<Category, NoOP, utility::tuple::Tuple<T...> > {
typedef Category<T... > Type;
};
/// specialisation of the \ref PlaceHolderExpression when the node is
/// TensorCwiseNullaryOp, TensorCwiseUnaryOp, TensorBroadcastingOp, TensorCwiseBinaryOp, TensorCwiseTernaryOp
#define OPEXPRCATEGORY(CVQual)\
template <template <class, class... > class Category, typename OP, typename... SubExpr, size_t N>\
struct PlaceHolderExpression<CVQual Category<OP, SubExpr...>, N>{\
typedef CVQual typename CategoryHelper<Category, OP, typename CalculateIndex<N, SubExpr...>::ArgsTuple>::Type Type;\
};
OPEXPRCATEGORY(const)
OPEXPRCATEGORY()
#undef OPEXPRCATEGORY
/// specialisation of the \ref PlaceHolderExpression when the node is
/// TensorCwiseSelectOp
#define SELECTEXPR(CVQual)\
template <typename IfExpr, typename ThenExpr, typename ElseExpr, size_t N>\
struct PlaceHolderExpression<CVQual TensorSelectOp<IfExpr, ThenExpr, ElseExpr>, N> {\
typedef CVQual typename CategoryHelper<TensorSelectOp, NoOP, typename CalculateIndex<N, IfExpr, ThenExpr, ElseExpr>::ArgsTuple>::Type Type;\
};
SELECTEXPR(const)
SELECTEXPR()
#undef SELECTEXPR
/// specialisation of the \ref PlaceHolderExpression when the node is
/// TensorAssignOp
#define ASSIGNEXPR(CVQual)\
template <typename LHSExpr, typename RHSExpr, size_t N>\
struct PlaceHolderExpression<CVQual TensorAssignOp<LHSExpr, RHSExpr>, N> {\
typedef CVQual typename CategoryHelper<TensorAssignOp, NoOP, typename CalculateIndex<N, LHSExpr, RHSExpr>::ArgsTuple>::Type Type;\
};
ASSIGNEXPR(const)
ASSIGNEXPR()
#undef ASSIGNEXPR
/// specialisation of the \ref PlaceHolderExpression when the node is
/// TensorMap
#define TENSORMAPEXPR(CVQual)\
template <typename Scalar_, int Options_, int Options2_, int NumIndices_, typename IndexType_, template <class> class MakePointer_, size_t N>\
struct PlaceHolderExpression< CVQual TensorMap< Tensor<Scalar_, NumIndices_, Options_, IndexType_>, Options2_, MakePointer_>, N> {\
typedef CVQual Eigen::internal::PlaceHolder<CVQual TensorMap<Tensor<Scalar_, NumIndices_, Options_, IndexType_>, Options2_, MakePointer_>, N> Type;\
};
TENSORMAPEXPR(const)
TENSORMAPEXPR()
#undef TENSORMAPEXPR
/// specialisation of the \ref PlaceHolderExpression when the node is
/// TensorForcedEvalOp
#define FORCEDEVAL(CVQual)\
template <typename Expr, size_t N>\
struct PlaceHolderExpression<CVQual TensorForcedEvalOp<Expr>, N> {\
typedef CVQual Eigen::internal::PlaceHolder<CVQual TensorForcedEvalOp<Expr>, N> Type;\
};
FORCEDEVAL(const)
FORCEDEVAL()
#undef FORCEDEVAL
/// specialisation of the \ref PlaceHolderExpression when the node is
/// TensorEvalToOp
#define EVALTO(CVQual)\
template <typename Expr, size_t N>\
struct PlaceHolderExpression<CVQual TensorEvalToOp<Expr>, N> {\
typedef CVQual TensorEvalToOp<typename CalculateIndex <N, Expr>::ArgType> Type;\
};
EVALTO(const)
EVALTO()
#undef EVALTO
/// template deduction for \ref PlaceHolderExpression struct
template <typename Expr>
struct createPlaceHolderExpression {
static const size_t TotalLeaves = LeafCount<Expr>::Count;
typedef typename PlaceHolderExpression<Expr, TotalLeaves - 1>::Type Type;
};
}
}
} // namespace Eigen
#endif // UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_PLACEHOLDER_EXPR_HPP

69
unsupported/Eigen/CXX11/src/Tensor/TensorSyclRun.h Normal file
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@ -0,0 +1,69 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Mehdi Goli Codeplay Software Ltd.
// Ralph Potter Codeplay Software Ltd.
// Luke Iwanski Codeplay Software Ltd.
// Cummins Chris PhD student at The University of Edinburgh.
// 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/.
/*****************************************************************
* TensorSyclRun.h
*
* \brief:
* Schedule_kernel invoke an specialised version of kernel struct. The
* specialisation is based on the data dimension in sycl buffer
*
*****************************************************************/
#ifndef UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_SYCLRUN_HPP
#define UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_SYCLRUN_HPP
namespace Eigen {
namespace TensorSycl {
/// The run function in tensor sycl convert the expression tree to a buffer
/// based expression tree;
/// creates the expression tree for the device with accessor to buffers;
/// construct the kernel and submit it to the sycl queue.
template <typename Expr, typename Dev>
void run(Expr &expr, Dev &dev) {
Eigen::TensorEvaluator<Expr, Dev> evaluator(expr, dev);
const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
if (needs_assign) {
typedef typename internal::createPlaceHolderExpression<Expr>::Type PlaceHolderExpr;
auto functors = internal::extractFunctors(evaluator);
dev.m_queue.submit([&](cl::sycl::handler &cgh) {
// create a tuple of accessors from Evaluator
auto tuple_of_accessors = internal::createTupleOfAccessors<decltype(evaluator)>(cgh, evaluator);
const auto range = utility::tuple::get<0>(tuple_of_accessors).get_range()[0];
size_t outTileSize = range;
if (range > 64) outTileSize = 64;
size_t yMode = range % outTileSize;
int yRange = static_cast<int>(range);
if (yMode != 0) yRange += (outTileSize - yMode);
// run the kernel
cgh.parallel_for<PlaceHolderExpr>( cl::sycl::nd_range<1>(cl::sycl::range<1>(yRange), cl::sycl::range<1>(outTileSize)), [=](cl::sycl::nd_item<1> itemID) {
typedef typename internal::ConvertToDeviceExpression<Expr>::Type DevExpr;
auto device_expr =internal::createDeviceExpression<DevExpr, PlaceHolderExpr>(functors, tuple_of_accessors);
auto device_evaluator = Eigen::TensorEvaluator<decltype(device_expr.expr), Eigen::DefaultDevice>(device_expr.expr, Eigen::DefaultDevice());
if (itemID.get_global_linear_id() < range) {
device_evaluator.evalScalar(static_cast<int>(itemID.get_global_linear_id()));
}
});
});
dev.m_queue.throw_asynchronous();
}
evaluator.cleanup();
}
} // namespace TensorSycl
} // namespace Eigen
#endif // UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_SYCLRUN_HPP

234
unsupported/Eigen/CXX11/src/Tensor/TensorSyclTuple.h Normal file
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@ -0,0 +1,234 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// 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/.
/*****************************************************************
* TensroSyclTuple.h
*
* \brief:
* Minimal implementation of std::tuple that can be used inside a SYCL kernel.
*
*****************************************************************/
#ifndef UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_TUPLE_HPP
#define UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_TUPLE_HPP
namespace utility {
namespace tuple {
/// \struct StaticIf
/// \brief The StaticIf struct is used to statically choose the type based on the
/// condition.
template <bool, typename T = void> struct StaticIf;
/// \brief specialisation of the \ref StaticIf when the condition is true
template <typename T>
struct StaticIf<true, T> {
typedef T type;
};
/// \struct Tuple
/// \brief is a fixed-size collection of heterogeneous values
/// \ztparam Ts... - the types of the elements that the tuple stores.
/// Empty list is supported.
template <class... Ts>
struct Tuple {};
/// \brief specialisation of the \ref Tuple class when the tuple has at least
/// one element.
/// \tparam T : the type of the first element in the tuple.
/// \tparam Ts... the rest of the elements in the tuple. Ts... can be empty.
template <class T, class... Ts>
struct Tuple<T, Ts...> {
Tuple(T t, Ts... ts) : head(t), tail(ts...) {}
T head;
Tuple<Ts...> tail;
};
///\ struct ElemTypeHolder
/// \brief ElemTypeHolder class is used to specify the types of the
/// elements inside the tuple
/// \tparam size_t the number of elements inside the tuple
/// \tparam class the tuple class
template <size_t, class>
struct ElemTypeHolder;
/// \brief specialisation of the \ref ElemTypeHolder class when the number of
/// elements inside the tuple is 1
template <class T, class... Ts>
struct ElemTypeHolder<0, Tuple<T, Ts...> > {
typedef T type;
};
/// \brief specialisation of the \ref ElemTypeHolder class when the number of
/// elements inside the tuple is bigger than 1. It recursively calls itself to
/// detect the type of each element in the tuple
/// \tparam T : the type of the first element in the tuple.
/// \tparam Ts... the rest of the elements in the tuple. Ts... can be empty.
/// \tparam K is the Kth element in the tuple
template <size_t k, class T, class... Ts>
struct ElemTypeHolder<k, Tuple<T, Ts...> > {
typedef typename ElemTypeHolder<k - 1, Tuple<Ts...> >::type type;
};
/// get
/// \brief Extracts the first element from the tuple.
/// K=0 represents the first element of the tuple. The tuple cannot be empty.
/// \tparam Ts... are the type of the elements in the tuple.
/// \param t is the tuple whose contents to extract
/// \return typename ElemTypeHolder<0, Tuple<Ts...> >::type &>::type
#define TERMINATE_CONDS_TUPLE_GET(CVQual) \
template <size_t k, class... Ts> \
typename StaticIf<k == 0, CVQual typename ElemTypeHolder<0, Tuple<Ts...> >::type &>::type \
get(CVQual Tuple<Ts...> &t) { \
static_assert(sizeof...(Ts)!=0, "The requseted value is bigger than the size of the tuple"); \
return t.head; \
}
TERMINATE_CONDS_TUPLE_GET(const)
TERMINATE_CONDS_TUPLE_GET()
#undef TERMINATE_CONDS_TUPLE_GET
/// get
/// \brief Extracts the Kth element from the tuple.
///\tparam K is an integer value in [0,sizeof...(Types)).
/// \tparam T is the (sizeof...(Types) -(K+1)) element in the tuple
/// \tparam Ts... are the type of the elements in the tuple.
/// \param t is the tuple whose contents to extract
/// \return typename ElemTypeHolder<K, Tuple<Ts...> >::type &>::type
#define RECURSIVE_TUPLE_GET(CVQual) \
template <size_t k, class T, class... Ts> \
typename StaticIf<k != 0, CVQual typename ElemTypeHolder<k, Tuple<T, Ts...> >::type &>::type \
get(CVQual Tuple<T, Ts...> &t) { \
return get<k - 1>(t.tail); \
}
RECURSIVE_TUPLE_GET(const)
RECURSIVE_TUPLE_GET()
#undef RECURSIVE_TUPLE_GET
/// make_tuple
/// \brief Creates a tuple object, deducing the target type from the types of
/// arguments.
/// \tparam Args the type of the arguments to construct the tuple from
/// \param args zero or more arguments to construct the tuple from
/// \return Tuple<Args...>
template <typename... Args>
Tuple<Args...> make_tuple(Args... args) {
return Tuple<Args...>(args...);
}
/// size
/// \brief Provides access to the number of elements in a tuple as a
/// compile-time constant expression.
/// \tparam Args the type of the arguments to construct the tuple from
/// \return size_t
template <typename... Args>
static constexpr size_t size(Tuple<Args...> &) {
return sizeof...(Args);
}
/// \struct IndexList
/// \brief Creates a list of index from the elements in the tuple
/// \tparam Is... a list of index from [0 to sizeof...(tuple elements))
template <size_t... Is>
struct IndexList {};
/// \struct RangeBuilder
/// \brief Collects internal details for generating index ranges [MIN, MAX)
/// Declare primary template for index range builder
/// \tparam MIN is the starting index in the tuple
/// \tparam N represents sizeof..(elemens)- sizeof...(Is)
/// \tparam Is... are the list of generated index so far
template <size_t MIN, size_t N, size_t... Is>
struct RangeBuilder;
/// \brief base Step: Specialisation of the \ref RangeBuilder when the
/// MIN==MAX. In this case the Is... is [0 to sizeof...(tuple elements))
/// \tparam MIN is the starting index of the tuple
/// \tparam Is is [0 to sizeof...(tuple elements))
template <size_t MIN, size_t... Is>
struct RangeBuilder<MIN, MIN, Is...> {
typedef IndexList<Is...> type;
};
/// Induction step: Specialisation of the RangeBuilder class when N!=MIN
/// in this case we are recursively subtracting N by one and adding one
/// index to Is... list until MIN==N
/// \tparam MIN is the starting index in the tuple
/// \tparam N represents sizeof..(elemens)- sizeof...(Is)
/// \tparam Is... are the list of generated index so far
template <size_t MIN, size_t N, size_t... Is>
struct RangeBuilder : public RangeBuilder<MIN, N - 1, N - 1, Is...> {};
/// \brief IndexRange that returns a [MIN, MAX) index range
/// \tparam MIN is the starting index in the tuple
/// \tparam MAX is the size of the tuple
template <size_t MIN, size_t MAX>
struct IndexRange: RangeBuilder<MIN, MAX>::type {};
/// append_base
/// \brief unpacking the elements of the input tuple t and creating a new tuple
/// by adding element a at the end of it.
///\tparam Args... the type of the elements inside the tuple t
/// \tparam T the type of the new element going to be added at the end of tuple
/// \tparam I... is the list of index from [0 to sizeof...(t))
/// \param t the tuple on which we want to append a.
/// \param a the new elements going to be added to the tuple
/// \return Tuple<Args..., T>
template <typename... Args, typename T, size_t... I>
Tuple<Args..., T> append_base(Tuple<Args...> t, T a,IndexList<I...>) {
return make_tuple(get<I>(t)..., a);
}
/// append
/// \brief the deduction function for \ref append_base that automatically
/// generate the \ref IndexRange
///\tparam Args... the type of the elements inside the tuple t
/// \tparam T the type of the new element going to be added at the end of tuple
/// \param t the tuple on which we want to append a.
/// \param a the new elements going to be added to the tuple
/// \return Tuple<Args..., T>
template <typename... Args, typename T>
Tuple<Args..., T> append(Tuple<Args...> t, T a) {
return append_base(t, a, IndexRange<0, sizeof...(Args)>());
}
/// append_base
/// \brief This is a specialisation of \ref append_base when we want to
/// concatenate
/// tuple t2 at the end of the tuple t1. Here we unpack both tuples, generate the
/// IndexRange for each of them and create an output tuple T that contains both
/// elements of t1 and t2.
///\tparam Args1... the type of the elements inside the tuple t1
///\tparam Args2... the type of the elements inside the tuple t2
/// \tparam I1... is the list of index from [0 to sizeof...(t1))
/// \tparam I2... is the list of index from [0 to sizeof...(t2))
/// \param t1 is the tuple on which we want to append t2.
/// \param t2 is the tuple that is going to be added on t1.
/// \return Tuple<Args1..., Args2...>
template <typename... Args1, typename... Args2, size_t... I1, size_t... I2>
Tuple<Args1..., Args2...> append_base(Tuple<Args1...> t1, Tuple<Args2...> t2, IndexList<I1...>, IndexList<I2...>) {
return make_tuple(get<I1>(t1)...,get<I2>(t2)...);
}
/// append
/// \brief deduction function for \ref append_base when we are appending tuple
/// t1 by tuple t2. In this case the \ref IndexRange for both tuple are
/// automatically generated.
///\tparam Args1... the type of the elements inside the tuple t1
///\tparam Args2... the type of the elements inside the tuple t2
/// \param t1 is the tuple on which we want to append t2.
/// \param t2 is the tuple that is going to be added on t1.
/// \return Tuple<Args1..., Args2...>
template <typename... Args1, typename... Args2>
Tuple<Args1..., Args2...> append(Tuple<Args1...> t1,Tuple<Args2...> t2) {
return append_base(t1, t2, IndexRange<0, sizeof...(Args1)>(), IndexRange<0, sizeof...(Args2)>());
}
} // tuple
} // utility
#endif // UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_TUPLE_HPP

19
unsupported/Eigen/CXX11/src/Tensor/TensorTraits.h
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@ -28,7 +28,7 @@ class compute_tensor_flags
#else
0
#endif
||
|
#if EIGEN_MAX_ALIGN_BYTES>0
is_dynamic_size_storage
#else
@ -56,11 +56,14 @@ struct traits<Tensor<Scalar_, NumIndices_, Options_, IndexType_> >
Options = Options_,
Flags = compute_tensor_flags<Scalar_, Options_>::ret | (is_const<Scalar_>::value ? 0 : LvalueBit)
};
template<class T> struct MakePointer{
typedef T* Type;
};
};
template<typename Scalar_, typename Dimensions, int Options_, typename IndexType_>
struct traits<TensorFixedSize<Scalar_, Dimensions, Options_, IndexType_> >
template<typename Scalar_, typename Dimensions, int Options_, typename IndexType_, template <class> class MakePointer_>
struct traits<TensorFixedSize<Scalar_, Dimensions, Options_, IndexType_, MakePointer_> >
{
typedef Scalar_ Scalar;
typedef Dense StorageKind;
@ -71,11 +74,14 @@ struct traits<TensorFixedSize<Scalar_, Dimensions, Options_, IndexType_> >
Options = Options_,
Flags = compute_tensor_flags<Scalar_, Options_>::ret | (is_const<Scalar_>::value ? 0: LvalueBit)
};
template<class T> struct MakePointer{
typedef typename MakePointer_<T>::Type Type;
};
};
template<typename PlainObjectType, int Options_>
struct traits<TensorMap<PlainObjectType, Options_> >
template<typename PlainObjectType, int Options_ , template <class> class MakePointer_>
struct traits<TensorMap<PlainObjectType, Options_ , MakePointer_> >
: public traits<PlainObjectType>
{
typedef traits<PlainObjectType> BaseTraits;
@ -88,6 +94,9 @@ struct traits<TensorMap<PlainObjectType, Options_> >
Options = Options_,
Flags = BaseTraits::Flags
};
template<class T> struct MakePointer{
typedef typename MakePointer_<T>::Type Type;
};
};
template<typename PlainObjectType>

6
unsupported/test/CMakeLists.txt
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@ -140,6 +140,12 @@ endif()
endif()
if(EIGEN_TEST_CXX11)
if(EIGEN_TEST_SYCL)
ei_add_test_sycl(cxx11_tensor_sycl "-std=c++11")
ei_add_test_sycl(cxx11_tensor_forced_eval_sycl "-std=c++11")
ei_add_test_sycl(cxx11_tensor_broadcast_sycl "-std=c++11")
ei_add_test_sycl(cxx11_tensor_device_sycl "-std=c++11")
endif(EIGEN_TEST_SYCL)
# It should be safe to always run these tests as there is some fallback code for
# older compiler that don't support cxx11.
set(CMAKE_CXX_STANDARD 11)

79
unsupported/test/cxx11_tensor_broadcast_sycl.cpp Normal file
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@ -0,0 +1,79 @@
// 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_broadcast_sycl
#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
#define EIGEN_USE_SYCL
#include "main.h"
#include <unsupported/Eigen/CXX11/Tensor>
using Eigen::array;
using Eigen::SyclDevice;
using Eigen::Tensor;
using Eigen::TensorMap;
// Types used in tests:
using TestTensor = Tensor<float, 3>;
using TestTensorMap = TensorMap<Tensor<float, 3>>;
static void test_broadcast_sycl(){
cl::sycl::gpu_selector s;
cl::sycl::queue q(s, [=](cl::sycl::exception_list l) {
for (const auto& e : l) {
try {
std::rethrow_exception(e);
} catch (cl::sycl::exception e) {
std::cout << e.what() << std::endl;
}
}
});
SyclDevice sycl_device(q);
// BROADCAST test:
array<int, 4> in_range = {{2, 3, 5, 7}};
array<int, in_range.size()> broadcasts = {{2, 3, 1, 4}};
array<int, in_range.size()> out_range; // = in_range * broadcasts
for (size_t i = 0; i < out_range.size(); ++i)
out_range[i] = in_range[i] * broadcasts[i];
Tensor<float, in_range.size()> input(in_range);
Tensor<float, out_range.size()> output(out_range);
for (int i = 0; i < input.size(); ++i)
input(i) = static_cast<float>(i);
TensorMap<decltype(input)> gpu_in(input.data(), in_range);
TensorMap<decltype(output)> gpu_out(output.data(), out_range);
gpu_out.device(sycl_device) = gpu_in.broadcast(broadcasts);
sycl_device.deallocate(output.data());
for (size_t i = 0; i < in_range.size(); ++i)
VERIFY_IS_EQUAL(output.dimension(i), out_range[i]);
for (int i = 0; i < 4; ++i) {
for (int j = 0; j < 9; ++j) {
for (int k = 0; k < 5; ++k) {
for (int l = 0; l < 28; ++l) {
VERIFY_IS_APPROX(input(i%2,j%3,k%5,l%7), output(i,j,k,l));
}
}
}
}
printf("Broadcast Test Passed\n");
}
void test_cxx11_tensor_broadcast_sycl() {
CALL_SUBTEST(test_broadcast_sycl());
}

39
unsupported/test/cxx11_tensor_device_sycl.cpp Normal file
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@ -0,0 +1,39 @@
// 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_device_sycl
#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
#define EIGEN_USE_SYCL
#include "main.h"
#include <unsupported/Eigen/CXX11/Tensor>
void test_device_sycl() {
cl::sycl::gpu_selector s;
cl::sycl::queue q(s, [=](cl::sycl::exception_list l) {
for (const auto& e : l) {
try {
std::rethrow_exception(e);
} catch (cl::sycl::exception e) {
std::cout << e.what() << std::endl;
}
}
});
Eigen::SyclDevice sycl_device(q);
printf("Helo from ComputeCpp: Device Exists\n");
}
void test_cxx11_tensor_device_sycl() {
CALL_SUBTEST(test_device_sycl());
}

68
unsupported/test/cxx11_tensor_forced_eval_sycl.cpp Normal file
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@ -0,0 +1,68 @@
// 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_forced_eval_sycl
#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
#define EIGEN_USE_SYCL
#include "main.h"
#include <unsupported/Eigen/CXX11/Tensor>
using Eigen::Tensor;
void test_forced_eval_sycl() {
cl::sycl::gpu_selector s;
cl::sycl::queue q(s, [=](cl::sycl::exception_list l) {
for (const auto& e : l) {
try {
std::rethrow_exception(e);
} catch (cl::sycl::exception e) {
std::cout << e.what() << std::endl;
}
}
});
SyclDevice sycl_device(q);
int sizeDim1 = 100;
int sizeDim2 = 200;
int sizeDim3 = 200;
Eigen::array<int, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}};
Eigen::Tensor<float, 3> in1(tensorRange);
Eigen::Tensor<float, 3> in2(tensorRange);
Eigen::Tensor<float, 3> out(tensorRange);
in1 = in1.random() + in1.constant(10.0f);
in2 = in2.random() + in2.constant(10.0f);
// creating TensorMap from tensor
Eigen::TensorMap<Eigen::Tensor<float, 3>> gpu_in1(in1.data(), tensorRange);
Eigen::TensorMap<Eigen::Tensor<float, 3>> gpu_in2(in2.data(), tensorRange);
Eigen::TensorMap<Eigen::Tensor<float, 3>> gpu_out(out.data(), tensorRange);
/// c=(a+b)*b
gpu_out.device(sycl_device) =(gpu_in1 + gpu_in2).eval() * gpu_in2;
sycl_device.deallocate(out.data());
for (int i = 0; i < sizeDim1; ++i) {
for (int j = 0; j < sizeDim2; ++j) {
for (int k = 0; k < sizeDim3; ++k) {
VERIFY_IS_APPROX(out(i, j, k),
(in1(i, j, k) + in2(i, j, k)) * in2(i, j, k));
}
}
}
printf("(a+b)*b Test Passed\n");
}
void test_cxx11_tensor_forced_eval_sycl() { CALL_SUBTEST(test_forced_eval_sycl()); }

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unsupported/test/cxx11_tensor_sycl.cpp Normal file
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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_TEST_FUNC cxx11_tensor_sycl
#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
#define EIGEN_USE_SYCL
#include "main.h"
#include <unsupported/Eigen/CXX11/Tensor>
using Eigen::array;
using Eigen::SyclDevice;
using Eigen::Tensor;
using Eigen::TensorMap;
// Types used in tests:
using TestTensor = Tensor<float, 3>;
using TestTensorMap = TensorMap<Tensor<float, 3>>;
void test_sycl_cpu() {
cl::sycl::gpu_selector s;
cl::sycl::queue q(s, [=](cl::sycl::exception_list l) {
for (const auto& e : l) {
try {
std::rethrow_exception(e);
} catch (cl::sycl::exception e) {
std::cout << e.what() << std::endl;
}
}
});
SyclDevice sycl_device(q);
int sizeDim1 = 100;
int sizeDim2 = 100;
int sizeDim3 = 100;
array<int, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}};
TestTensor in1(tensorRange);
TestTensor in2(tensorRange);
TestTensor in3(tensorRange);
TestTensor out(tensorRange);
in1 = in1.random();
in2 = in2.random();
in3 = in3.random();
TestTensorMap gpu_in1(in1.data(), tensorRange);
TestTensorMap gpu_in2(in2.data(), tensorRange);
TestTensorMap gpu_in3(in3.data(), tensorRange);
TestTensorMap gpu_out(out.data(), tensorRange);
/// a=1.2f
gpu_in1.device(sycl_device) = gpu_in1.constant(1.2f);
sycl_device.deallocate(in1.data());
for (int i = 0; i < sizeDim1; ++i) {
for (int j = 0; j < sizeDim2; ++j) {
for (int 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.deallocate(out.data());
for (int i = 0; i < sizeDim1; ++i) {
for (int j = 0; j < sizeDim2; ++j) {
for (int 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
gpu_out.device(sycl_device) = gpu_in1 * gpu_in2;
sycl_device.deallocate(out.data());
for (int i = 0; i < sizeDim1; ++i) {
for (int j = 0; j < sizeDim2; ++j) {
for (int 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.deallocate(out.data());
for (int i = 0; i < sizeDim1; ++i) {
for (int j = 0; j < sizeDim2; ++j) {
for (int 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.deallocate(out.data());
for (int i = 0; i < sizeDim1; ++i) {
for (int j = 0; j < sizeDim2; ++j) {
for (int 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.deallocate(out.data());
for (int i = 0; i < sizeDim1; ++i) {
for (int j = 0; j < sizeDim2; ++j) {
for (int 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)
gpu_out.device(sycl_device) =(gpu_in1 > gpu_in1.constant(0.5f)).select(gpu_in2, gpu_in3);
sycl_device.deallocate(out.data());
for (int i = 0; i < sizeDim1; ++i) {
for (int j = 0; j < sizeDim2; ++j) {
for (int 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");
}
void test_cxx11_tensor_sycl() {
CALL_SUBTEST(test_sycl_cpu());
}