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CMakeLists.txt
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# ---[ Generate and install header and cpp files
include(../cmake/Codegen.cmake)
# ---[ Vulkan code gen
if(USE_VULKAN)
include(../cmake/VulkanCodegen.cmake)
endif()
# ATen parallelism settings
# OMP - OpenMP for intra-op, native thread pool for inter-op parallelism
# NATIVE - using native thread pool for intra- and inter-op parallelism
# TBB - using TBB for intra- and native thread pool for inter-op parallelism
if(INTERN_BUILD_MOBILE AND NOT BUILD_CAFFE2_MOBILE)
set(ATEN_THREADING "NATIVE" CACHE STRING "ATen parallel backend")
else()
set(ATEN_THREADING "OMP" CACHE STRING "ATen parallel backend")
endif()
set(AT_PARALLEL_OPENMP 0)
set(AT_PARALLEL_NATIVE 0)
set(AT_PARALLEL_NATIVE_TBB 0)
message(STATUS "Using ATen parallel backend: ${ATEN_THREADING}")
if("${ATEN_THREADING}" STREQUAL "OMP")
set(AT_PARALLEL_OPENMP 1)
elseif("${ATEN_THREADING}" STREQUAL "NATIVE")
set(AT_PARALLEL_NATIVE 1)
elseif("${ATEN_THREADING}" STREQUAL "TBB")
if(NOT USE_TBB)
message(FATAL_ERROR "Using TBB backend but USE_TBB is off")
endif()
set(AT_PARALLEL_NATIVE_TBB 1)
else()
message(FATAL_ERROR "Unknown ATen parallel backend: ${ATEN_THREADING}")
endif()
# ---[ Declare source file lists
# ---[ ATen build
if(INTERN_BUILD_ATEN_OPS)
set(__caffe2_CMAKE_POSITION_INDEPENDENT_CODE ${CMAKE_POSITION_INDEPENDENT_CODE})
set(CMAKE_POSITION_INDEPENDENT_CODE ON)
add_subdirectory(../aten aten)
set(CMAKE_POSITION_INDEPENDENT_CODE ${__caffe2_CMAKE_POSITION_INDEPENDENT_CODE})
# Generate the headers wrapped by our operator
add_custom_command(OUTPUT ${CMAKE_CURRENT_BINARY_DIR}/contrib/aten/aten_op.h
COMMAND
"${PYTHON_EXECUTABLE}" ${CMAKE_CURRENT_SOURCE_DIR}/contrib/aten/gen_op.py
--aten_root=${CMAKE_CURRENT_SOURCE_DIR}/../aten
--template_dir=${CMAKE_CURRENT_SOURCE_DIR}/contrib/aten
--yaml_dir=${CMAKE_CURRENT_BINARY_DIR}/../aten/src/ATen
--install_dir=${CMAKE_CURRENT_BINARY_DIR}/contrib/aten
DEPENDS
ATEN_CPU_FILES_GEN_TARGET
${CMAKE_BINARY_DIR}/aten/src/ATen/Declarations.yaml
${CMAKE_CURRENT_SOURCE_DIR}/contrib/aten/gen_op.py
${CMAKE_CURRENT_SOURCE_DIR}/contrib/aten/aten_op_template.h)
add_custom_target(__aten_op_header_gen
DEPENDS ${CMAKE_CURRENT_BINARY_DIR}/contrib/aten/aten_op.h)
add_library(aten_op_header_gen INTERFACE)
add_dependencies(aten_op_header_gen __aten_op_header_gen)
# Add source, includes, and libs to lists
list(APPEND Caffe2_CPU_SRCS ${ATen_CPU_SRCS})
list(APPEND Caffe2_GPU_SRCS ${ATen_CUDA_SRCS})
list(APPEND Caffe2_GPU_SRCS_W_SORT_BY_KEY ${ATen_CUDA_SRCS_W_SORT_BY_KEY})
list(APPEND Caffe2_HIP_SRCS ${ATen_HIP_SRCS})
list(APPEND Caffe2_HIP_SRCS ${ATen_HIP_SRCS_W_SORT_BY_KEY})
list(APPEND Caffe2_CPU_TEST_SRCS ${ATen_CPU_TEST_SRCS})
list(APPEND Caffe2_GPU_TEST_SRCS ${ATen_CUDA_TEST_SRCS})
list(APPEND Caffe2_HIP_TEST_SRCS ${ATen_HIP_TEST_SRCS})
list(APPEND Caffe2_CPU_TEST_SRCS ${ATen_CORE_TEST_SRCS})
list(APPEND Caffe2_VULKAN_TEST_SRCS ${ATen_VULKAN_TEST_SRCS})
list(APPEND Caffe2_CPU_INCLUDE ${ATen_CPU_INCLUDE})
list(APPEND Caffe2_GPU_INCLUDE ${ATen_CUDA_INCLUDE})
list(APPEND Caffe2_HIP_INCLUDE ${ATen_HIP_INCLUDE})
list(APPEND Caffe2_VULKAN_INCLUDE ${ATen_VULKAN_INCLUDE})
list(APPEND Caffe2_DEPENDENCY_LIBS ${ATen_CPU_DEPENDENCY_LIBS})
list(APPEND Caffe2_CUDA_DEPENDENCY_LIBS ${ATen_CUDA_DEPENDENCY_LIBS})
list(APPEND Caffe2_HIP_DEPENDENCY_LIBS ${ATen_HIP_DEPENDENCY_LIBS})
list(APPEND Caffe2_DEPENDENCY_INCLUDE ${ATen_THIRD_PARTY_INCLUDE})
endif()
# ---[ Caffe2 build
# Note: the folders that are being commented out have not been properly
# addressed yet.
if(NOT MSVC AND USE_XNNPACK)
if(NOT TARGET fxdiv)
set(FXDIV_BUILD_TESTS OFF CACHE BOOL "")
set(FXDIV_BUILD_BENCHMARKS OFF CACHE BOOL "")
add_subdirectory(
"${FXDIV_SOURCE_DIR}"
"${CMAKE_BINARY_DIR}/FXdiv")
endif()
endif()
add_subdirectory(core)
add_subdirectory(serialize)
add_subdirectory(utils)
add_subdirectory(perfkernels)
# Skip modules that are not used by libtorch mobile yet.
if(NOT INTERN_BUILD_MOBILE OR BUILD_CAFFE2_MOBILE)
add_subdirectory(contrib)
add_subdirectory(predictor)
add_subdirectory(predictor/emulator)
add_subdirectory(core/nomnigraph)
if(USE_NVRTC)
add_subdirectory(cuda_rtc)
endif()
add_subdirectory(db)
add_subdirectory(distributed)
# add_subdirectory(experiments) # note, we may remove this folder at some point
add_subdirectory(ideep)
add_subdirectory(image)
add_subdirectory(video)
add_subdirectory(mobile)
add_subdirectory(mpi)
add_subdirectory(observers)
add_subdirectory(onnx)
if(BUILD_CAFFE2_OPS)
add_subdirectory(operators)
add_subdirectory(operators/rnn)
if(USE_FBGEMM)
add_subdirectory(quantization)
add_subdirectory(quantization/server)
endif()
if(USE_QNNPACK)
add_subdirectory(operators/quantized)
endif()
endif()
add_subdirectory(opt)
add_subdirectory(proto)
add_subdirectory(python)
add_subdirectory(queue)
add_subdirectory(sgd)
add_subdirectory(share)
# add_subdirectory(test) # todo: use caffe2_gtest_main instead of gtest_main because we will need to call GlobalInit
add_subdirectory(transforms)
endif()
# Advanced: if we have white list specified, we will do intersections for all
# main lib srcs.
if(CAFFE2_WHITELISTED_FILES)
caffe2_do_whitelist(Caffe2_CPU_SRCS CAFFE2_WHITELISTED_FILES)
caffe2_do_whitelist(Caffe2_GPU_SRCS CAFFE2_WHITELISTED_FILES)
caffe2_do_whitelist(Caffe2_HIP_SRCS CAFFE2_WHITELISTED_FILES)
endif()
# Debug messages - if you want to get a list of source files, enable the
# following.
if(FALSE)
message(STATUS "CPU sources: ")
foreach(tmp ${Caffe2_CPU_SRCS})
message(STATUS " " ${tmp})
endforeach()
message(STATUS "GPU sources: ")
foreach(tmp ${Caffe2_GPU_SRCS})
message(STATUS " " ${tmp})
endforeach()
message(STATUS "CPU include: ")
foreach(tmp ${Caffe2_CPU_INCLUDE})
message(STATUS " " ${tmp})
endforeach()
message(STATUS "GPU include: ")
foreach(tmp ${Caffe2_GPU_INCLUDE})
message(STATUS " " ${tmp})
endforeach()
message(STATUS "CPU test sources: ")
foreach(tmp ${Caffe2_CPU_TEST_SRCS})
message(STATUS " " ${tmp})
endforeach()
message(STATUS "GPU test sources: ")
foreach(tmp ${Caffe2_GPU_TEST_SRCS})
message(STATUS " " ${tmp})
endforeach()
message(STATUS "HIP sources: ")
foreach(tmp ${Caffe2_HIP_SRCS})
message(STATUS " " ${tmp})
endforeach()
message(STATUS "HIP test sources: ")
foreach(tmp ${Caffe2_HIP_TEST_SRCS})
message(STATUS " " ${tmp})
endforeach()
message(STATUS "ATen CPU test sources: ")
foreach(tmp ${ATen_CPU_TEST_SRCS})
message(STATUS " " ${tmp})
endforeach()
message(STATUS "ATen CUDA test sources: ")
foreach(tmp ${ATen_CUDA_TEST_SRCS})
message(STATUS " " ${tmp})
endforeach()
message(STATUS "ATen HIP test sources: ")
foreach(tmp ${ATen_HIP_TEST_SRCS})
message(STATUS " " ${tmp})
endforeach()
message(STATUS "ATen Vulkan test sources: ")
foreach(tmp ${ATen_VULKAN_TEST_SRCS})
message(STATUS " " ${tmp})
endforeach()
endif()
if(NOT INTERN_BUILD_MOBILE OR BUILD_CAFFE2_MOBILE)
# ---[ List of libraries to link with
add_library(caffe2_protos STATIC $<TARGET_OBJECTS:Caffe2_PROTO>)
add_dependencies(caffe2_protos Caffe2_PROTO)
# If we are going to link protobuf locally inside caffe2 libraries, what we will do is
# to create a helper static library that always contains libprotobuf source files, and
# link the caffe2 related dependent libraries to it.
target_include_directories(caffe2_protos INTERFACE $<INSTALL_INTERFACE:include>)
# Reason for this public dependency is as follows:
# (1) Strictly speaking, we should not expose any Protobuf related functions. We should
# only use function interfaces wrapped with our own public API, and link protobuf
# locally.
# (2) However, currently across the Caffe2 codebase, we have extensive use of protobuf
# functionalities. For example, not only libcaffe2.so uses it, but also other
# binaries such as python extensions etc. As a result, we will have to have a
# transitive dependency to libprotobuf.
#
# Good thing is that, if we specify CAFFE2_LINK_LOCAL_PROTOBUF, then we do not need to
# separately deploy protobuf binaries - libcaffe2.so will contain all functionalities
# one needs. One can verify this via ldd.
#
# TODO item in the future includes:
# (1) Enable using lite protobuf
# (2) Properly define public API that do not directly depend on protobuf itself.
# (3) Expose the libprotobuf.a file for dependent libraries to link to.
#
# What it means for users/developers?
# (1) Users: nothing affecting the users, other than the fact that CAFFE2_LINK_LOCAL_PROTOBUF
# avoids the need to deploy protobuf.
# (2) Developers: if one simply uses core caffe2 functionality without using protobuf,
# nothing changes. If one has a dependent library that uses protobuf, then one needs to
# have the right protobuf version as well as linking to libprotobuf.a.
target_link_libraries(caffe2_protos PUBLIC protobuf::libprotobuf)
if(NOT BUILD_SHARED_LIBS)
install(TARGETS caffe2_protos ARCHIVE DESTINATION "${CMAKE_INSTALL_LIBDIR}")
endif()
endif()
# ==========================================================
# formerly-libtorch
# ==========================================================
set(TORCH_SRC_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../torch")
set(TORCH_ROOT "${TORCH_SRC_DIR}/..")
if(NOT TORCH_INSTALL_BIN_DIR)
set(TORCH_INSTALL_BIN_DIR bin)
endif()
if(NOT TORCH_INSTALL_INCLUDE_DIR)
set(TORCH_INSTALL_INCLUDE_DIR include)
endif()
if(NOT TORCH_INSTALL_LIB_DIR)
set(TORCH_INSTALL_LIB_DIR lib)
endif()
if(NOT INTERN_BUILD_MOBILE OR NOT BUILD_CAFFE2_MOBILE)
set(CMAKE_POSITION_INDEPENDENT_CODE TRUE)
# Generate files
set(TOOLS_PATH "${TORCH_ROOT}/tools")
configure_file("${TORCH_ROOT}/aten/src/ATen/common_with_cwrap.py"
"${TOOLS_PATH}/shared/cwrap_common.py"
COPYONLY)
configure_file("${TORCH_SRC_DIR}/_utils_internal.py"
"${TOOLS_PATH}/shared/_utils_internal.py"
COPYONLY)
set(GENERATED_CXX_TORCH
"${TORCH_SRC_DIR}/csrc/autograd/generated/Functions.cpp"
"${TORCH_SRC_DIR}/csrc/jit/generated/generated_unboxing_wrappers_0.cpp"
"${TORCH_SRC_DIR}/csrc/jit/generated/generated_unboxing_wrappers_1.cpp"
"${TORCH_SRC_DIR}/csrc/jit/generated/generated_unboxing_wrappers_2.cpp"
)
if(NOT INTERN_DISABLE_AUTOGRAD)
list(APPEND GENERATED_CXX_TORCH
"${TORCH_SRC_DIR}/csrc/autograd/generated/VariableType_0.cpp"
"${TORCH_SRC_DIR}/csrc/autograd/generated/VariableType_1.cpp"
"${TORCH_SRC_DIR}/csrc/autograd/generated/VariableType_2.cpp"
"${TORCH_SRC_DIR}/csrc/autograd/generated/VariableType_3.cpp"
"${TORCH_SRC_DIR}/csrc/autograd/generated/VariableType_4.cpp"
"${TORCH_SRC_DIR}/csrc/autograd/generated/ProfiledType_0.cpp"
"${TORCH_SRC_DIR}/csrc/autograd/generated/ProfiledType_1.cpp"
"${TORCH_SRC_DIR}/csrc/autograd/generated/ProfiledType_2.cpp"
"${TORCH_SRC_DIR}/csrc/autograd/generated/ProfiledType_3.cpp"
"${TORCH_SRC_DIR}/csrc/autograd/generated/ProfiledType_4.cpp"
"${TORCH_SRC_DIR}/csrc/autograd/generated/TraceType_0.cpp"
"${TORCH_SRC_DIR}/csrc/autograd/generated/TraceType_1.cpp"
"${TORCH_SRC_DIR}/csrc/autograd/generated/TraceType_2.cpp"
"${TORCH_SRC_DIR}/csrc/autograd/generated/TraceType_3.cpp"
"${TORCH_SRC_DIR}/csrc/autograd/generated/TraceType_4.cpp"
)
endif()
set(GENERATED_H_TORCH
"${TORCH_SRC_DIR}/csrc/autograd/generated/Functions.h"
"${TORCH_SRC_DIR}/csrc/autograd/generated/variable_factories.h"
)
if(NOT INTERN_DISABLE_AUTOGRAD)
list(APPEND GENERATED_H_TORCH
"${TORCH_SRC_DIR}/csrc/autograd/generated/VariableType.h"
)
endif()
set(GENERATED_CXX_PYTHON
"${TORCH_SRC_DIR}/csrc/autograd/generated/python_functions.cpp"
"${TORCH_SRC_DIR}/csrc/autograd/generated/python_variable_methods.cpp"
"${TORCH_SRC_DIR}/csrc/autograd/generated/python_torch_functions.cpp"
"${TORCH_SRC_DIR}/csrc/autograd/generated/python_nn_functions.cpp"
)
set(GENERATED_H_PYTHON
"${TORCH_SRC_DIR}/csrc/autograd/generated/python_functions.h"
)
set(TORCH_GENERATED_CODE
${GENERATED_CXX_TORCH}
${GENERATED_H_TORCH}
${GENERATED_CXX_PYTHON}
${GENERATED_H_PYTHON}
)
add_custom_command(
OUTPUT
${TORCH_GENERATED_CODE}
COMMAND
"${PYTHON_EXECUTABLE}" tools/setup_helpers/generate_code.py
--declarations-path "${CMAKE_BINARY_DIR}/aten/src/ATen/Declarations.yaml"
--nn-path "aten/src"
$<$<BOOL:${INTERN_DISABLE_AUTOGRAD}>:--disable-autograd>
$<$<BOOL:${SELECTED_OP_LIST}>:--selected-op-list-path="${SELECTED_OP_LIST}">
--force_schema_registration
DEPENDS
"${CMAKE_BINARY_DIR}/aten/src/ATen/Declarations.yaml"
"${TOOLS_PATH}/autograd/templates/VariableType.h"
"${TOOLS_PATH}/autograd/templates/VariableType.cpp"
"${TOOLS_PATH}/autograd/templates/ProfiledType.cpp"
"${TOOLS_PATH}/autograd/templates/TraceType.cpp"
"${TOOLS_PATH}/autograd/templates/Functions.h"
"${TOOLS_PATH}/autograd/templates/Functions.cpp"
"${TOOLS_PATH}/autograd/templates/python_functions.h"
"${TOOLS_PATH}/autograd/templates/python_functions.cpp"
"${TOOLS_PATH}/autograd/templates/python_variable_methods.cpp"
"${TOOLS_PATH}/autograd/templates/python_torch_functions.cpp"
"${TOOLS_PATH}/autograd/templates/python_nn_functions.cpp"
"${TOOLS_PATH}/autograd/templates/variable_factories.h"
"${TOOLS_PATH}/autograd/deprecated.yaml"
"${TOOLS_PATH}/autograd/derivatives.yaml"
"${TOOLS_PATH}/autograd/gen_autograd_functions.py"
"${TOOLS_PATH}/autograd/gen_autograd.py"
"${TOOLS_PATH}/autograd/gen_python_functions.py"
"${TOOLS_PATH}/autograd/gen_variable_factories.py"
"${TOOLS_PATH}/autograd/gen_variable_type.py"
"${TOOLS_PATH}/autograd/load_derivatives.py"
"${TOOLS_PATH}/autograd/nested_dict.py"
"${TOOLS_PATH}/autograd/utils.py"
"${TOOLS_PATH}/jit/gen_unboxing_wrappers.py"
"${TOOLS_PATH}/jit/templates/generated_unboxing_wrappers.cpp"
WORKING_DIRECTORY "${TORCH_ROOT}")
# Required workaround for libtorch_python.so build
# see https://samthursfield.wordpress.com/2015/11/21/cmake-dependencies-between-targets-and-files-and-custom-commands/#custom-commands-in-different-directories
add_custom_target(
generate-torch-sources
DEPENDS ${TORCH_GENERATED_CODE}
)
set(TORCH_SRCS ${GENERATED_CXX_TORCH})
list(APPEND TORCH_SRCS ${GENERATED_H_TORCH})
append_filelist("libtorch_cmake_sources" TORCH_SRCS)
# Required workaround for LLVM 9 includes.
if(NOT MSVC)
set_source_files_properties(${TORCH_SRC_DIR}/csrc/jit/tensorexpr/llvm_jit.cpp PROPERTIES COMPILE_FLAGS -Wno-noexcept-type)
endif()
# Disable certain warnings for GCC-9.X
if(CMAKE_COMPILER_IS_GNUCXX AND (CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 9.0.0))
# See https://github.com/pytorch/pytorch/issues/38856
set_source_files_properties(${TORCH_SRC_DIR}/csrc/jit/tensorexpr/llvm_jit.cpp PROPERTIES COMPILE_FLAGS "-Wno-redundant-move -Wno-noexcept-type")
set_source_files_properties(${TORCH_SRC_DIR}/csrc/jit/tensorexpr/llvm_codegen.cpp PROPERTIES COMPILE_FLAGS -Wno-init-list-lifetime)
endif()
if(NOT INTERN_DISABLE_MOBILE_INTERP)
set(MOBILE_SRCS
${TORCH_SRC_DIR}/csrc/jit/mobile/function.cpp
${TORCH_SRC_DIR}/csrc/jit/mobile/import.cpp
${TORCH_SRC_DIR}/csrc/jit/mobile/module.cpp
${TORCH_SRC_DIR}/csrc/jit/mobile/observer.cpp
${TORCH_SRC_DIR}/csrc/jit/mobile/register_mobile_autograd.cpp
${TORCH_SRC_DIR}/csrc/jit/mobile/interpreter.cpp
)
list(APPEND TORCH_SRCS ${MOBILE_SRCS})
endif()
if(NOT INTERN_DISABLE_AUTOGRAD)
list(APPEND TORCH_SRCS
${TORCH_SRC_DIR}/csrc/autograd/TraceTypeManual.cpp
${TORCH_SRC_DIR}/csrc/autograd/VariableTypeManual.cpp
)
endif()
if(NOT INTERN_BUILD_MOBILE)
list(APPEND TORCH_SRCS
${TORCH_SRC_DIR}/csrc/api/src/jit.cpp
${TORCH_SRC_DIR}/csrc/jit/serialization/export.cpp
${TORCH_SRC_DIR}/csrc/jit/serialization/export_module.cpp
${TORCH_SRC_DIR}/csrc/jit/serialization/import_legacy.cpp
${TORCH_SRC_DIR}/csrc/jit/codegen/fuser/cpu/fused_kernel.cpp
${TORCH_SRC_DIR}/csrc/jit/api/module_save.cpp
${TORCH_SRC_DIR}/csrc/utils/byte_order.cpp
)
if(USE_DISTRIBUTED)
append_filelist("libtorch_distributed_sources" TORCH_SRCS)
endif()
endif()
if(USE_CUDA)
list(APPEND Caffe2_GPU_SRCS
${TORCH_SRC_DIR}/csrc/jit/codegen/fuser/cuda/fused_kernel.cpp
${TORCH_SRC_DIR}/csrc/autograd/profiler_cuda.cpp
${TORCH_SRC_DIR}/csrc/autograd/functions/comm.cpp
${TORCH_SRC_DIR}/csrc/cuda/comm.cpp
${TORCH_SRC_DIR}/csrc/jit/codegen/cuda/arith.cpp
${TORCH_SRC_DIR}/csrc/jit/codegen/cuda/compute_at.cpp
${TORCH_SRC_DIR}/csrc/jit/codegen/cuda/dispatch.cpp
${TORCH_SRC_DIR}/csrc/jit/codegen/cuda/expr_evaluator.cpp
${TORCH_SRC_DIR}/csrc/jit/codegen/cuda/fusion.cpp
${TORCH_SRC_DIR}/csrc/jit/codegen/cuda/graph_fuser.cpp
${TORCH_SRC_DIR}/csrc/jit/codegen/cuda/index_compute.cpp
${TORCH_SRC_DIR}/csrc/jit/codegen/cuda/ir_base_nodes.cpp
${TORCH_SRC_DIR}/csrc/jit/codegen/cuda/ir_cloner.cpp
${TORCH_SRC_DIR}/csrc/jit/codegen/cuda/ir_graphviz.cpp
${TORCH_SRC_DIR}/csrc/jit/codegen/cuda/ir_nodes.cpp
${TORCH_SRC_DIR}/csrc/jit/codegen/cuda/ir_iostream.cpp
${TORCH_SRC_DIR}/csrc/jit/codegen/cuda/iter_visitor.cpp
${TORCH_SRC_DIR}/csrc/jit/codegen/cuda/kernel.cpp
${TORCH_SRC_DIR}/csrc/jit/codegen/cuda/kernel_cache.cpp
${TORCH_SRC_DIR}/csrc/jit/codegen/cuda/manager.cpp
${TORCH_SRC_DIR}/csrc/jit/codegen/cuda/shape_inference.cpp
${TORCH_SRC_DIR}/csrc/jit/codegen/cuda/mutator.cpp
${TORCH_SRC_DIR}/csrc/jit/codegen/cuda/lower_index.cpp
${TORCH_SRC_DIR}/csrc/jit/codegen/cuda/lower_loops.cpp
${TORCH_SRC_DIR}/csrc/jit/codegen/cuda/lower_thread_predicate.cpp
${TORCH_SRC_DIR}/csrc/jit/codegen/cuda/lower_unroll.cpp
${TORCH_SRC_DIR}/csrc/jit/codegen/cuda/lower_utils.cpp
${TORCH_SRC_DIR}/csrc/jit/codegen/cuda/lower_validation.cpp
${TORCH_SRC_DIR}/csrc/jit/codegen/cuda/lower2device.cpp
${TORCH_SRC_DIR}/csrc/jit/codegen/cuda/parser.cpp
${TORCH_SRC_DIR}/csrc/jit/codegen/cuda/partition.cpp
${TORCH_SRC_DIR}/csrc/jit/codegen/cuda/predicate_compute.cpp
${TORCH_SRC_DIR}/csrc/jit/codegen/cuda/tensor_view.cpp
${TORCH_SRC_DIR}/csrc/jit/codegen/cuda/transform_iter.cpp
${TORCH_SRC_DIR}/csrc/jit/codegen/cuda/transform_replay.cpp
${TORCH_SRC_DIR}/csrc/jit/codegen/cuda/transform_rfactor.cpp
${TORCH_SRC_DIR}/csrc/jit/codegen/cuda/type.cpp
${TORCH_SRC_DIR}/csrc/jit/codegen/cuda/utils.cpp
${TORCH_SRC_DIR}/csrc/jit/codegen/cuda/register_interface.cpp
${TORCH_SRC_DIR}/csrc/jit/tensorexpr/cuda_codegen.cpp
)
add_library(caffe2_nvrtc SHARED ${ATen_NVRTC_STUB_SRCS})
if(MSVC)
# Delay load nvcuda.dll so we can import torch compiled with cuda on a CPU-only machine
set(DELAY_LOAD_FLAGS "-DELAYLOAD:nvcuda.dll;delayimp.lib")
else()
set(DELAY_LOAD_FLAGS "")
endif()
target_link_libraries(caffe2_nvrtc ${CUDA_NVRTC} ${CUDA_CUDA_LIB} ${CUDA_NVRTC_LIB} ${DELAY_LOAD_FLAGS})
target_include_directories(caffe2_nvrtc PRIVATE ${CUDA_INCLUDE_DIRS})
install(TARGETS caffe2_nvrtc DESTINATION "${TORCH_INSTALL_LIB_DIR}")
if(USE_NCCL)
list(APPEND Caffe2_GPU_SRCS
${TORCH_SRC_DIR}/csrc/cuda/nccl.cpp)
endif()
endif()
if(USE_ROCM)
list(APPEND Caffe2_HIP_SRCS
${TORCH_SRC_DIR}/csrc/jit/codegen/fuser/cuda/fused_kernel.cpp
${TORCH_SRC_DIR}/csrc/autograd/profiler_cuda.cpp
${TORCH_SRC_DIR}/csrc/autograd/functions/comm.cpp
${TORCH_SRC_DIR}/csrc/cuda/comm.cpp
${TORCH_SRC_DIR}/csrc/jit/tensorexpr/cuda_codegen.cpp
)
if(USE_NCCL)
list(APPEND Caffe2_HIP_SRCS
${TORCH_SRC_DIR}/csrc/cuda/nccl.cpp)
endif()
# caffe2_nvrtc's stubs to driver APIs are useful for HIP.
# See NOTE [ ATen NVRTC Stub and HIP ]
add_library(caffe2_nvrtc SHARED ${ATen_NVRTC_STUB_SRCS})
target_link_libraries(caffe2_nvrtc ${PYTORCH_HIP_HCC_LIBRARIES} ${ROCM_HIPRTC_LIB})
target_compile_definitions(caffe2_nvrtc PRIVATE USE_ROCM __HIP_PLATFORM_HCC__)
install(TARGETS caffe2_nvrtc DESTINATION "${TORCH_INSTALL_LIB_DIR}")
endif()
if(NOT NO_API)
list(APPEND TORCH_SRCS
${TORCH_SRC_DIR}/csrc/api/src/cuda.cpp
${TORCH_SRC_DIR}/csrc/api/src/data/datasets/mnist.cpp
${TORCH_SRC_DIR}/csrc/api/src/data/samplers/distributed.cpp
${TORCH_SRC_DIR}/csrc/api/src/data/samplers/random.cpp
${TORCH_SRC_DIR}/csrc/api/src/data/samplers/sequential.cpp
${TORCH_SRC_DIR}/csrc/api/src/data/samplers/stream.cpp
${TORCH_SRC_DIR}/csrc/api/src/enum.cpp
${TORCH_SRC_DIR}/csrc/api/src/serialize.cpp
${TORCH_SRC_DIR}/csrc/api/src/jit.cpp
${TORCH_SRC_DIR}/csrc/api/src/nn/init.cpp
${TORCH_SRC_DIR}/csrc/api/src/nn/module.cpp
${TORCH_SRC_DIR}/csrc/api/src/nn/modules/_functions.cpp
${TORCH_SRC_DIR}/csrc/api/src/nn/modules/activation.cpp
${TORCH_SRC_DIR}/csrc/api/src/nn/modules/adaptive.cpp
${TORCH_SRC_DIR}/csrc/api/src/nn/modules/batchnorm.cpp
${TORCH_SRC_DIR}/csrc/api/src/nn/modules/normalization.cpp
${TORCH_SRC_DIR}/csrc/api/src/nn/modules/instancenorm.cpp
${TORCH_SRC_DIR}/csrc/api/src/nn/modules/conv.cpp
${TORCH_SRC_DIR}/csrc/api/src/nn/modules/dropout.cpp
${TORCH_SRC_DIR}/csrc/api/src/nn/modules/distance.cpp
${TORCH_SRC_DIR}/csrc/api/src/nn/modules/embedding.cpp
${TORCH_SRC_DIR}/csrc/api/src/nn/modules/fold.cpp
${TORCH_SRC_DIR}/csrc/api/src/nn/modules/linear.cpp
${TORCH_SRC_DIR}/csrc/api/src/nn/modules/loss.cpp
${TORCH_SRC_DIR}/csrc/api/src/nn/modules/padding.cpp
${TORCH_SRC_DIR}/csrc/api/src/nn/modules/pixelshuffle.cpp
${TORCH_SRC_DIR}/csrc/api/src/nn/modules/pooling.cpp
${TORCH_SRC_DIR}/csrc/api/src/nn/modules/rnn.cpp
${TORCH_SRC_DIR}/csrc/api/src/nn/modules/upsampling.cpp
${TORCH_SRC_DIR}/csrc/api/src/nn/modules/container/functional.cpp
${TORCH_SRC_DIR}/csrc/api/src/nn/options/activation.cpp
${TORCH_SRC_DIR}/csrc/api/src/nn/options/adaptive.cpp
${TORCH_SRC_DIR}/csrc/api/src/nn/options/batchnorm.cpp
${TORCH_SRC_DIR}/csrc/api/src/nn/options/embedding.cpp
${TORCH_SRC_DIR}/csrc/api/src/nn/options/instancenorm.cpp
${TORCH_SRC_DIR}/csrc/api/src/nn/options/normalization.cpp
${TORCH_SRC_DIR}/csrc/api/src/nn/options/conv.cpp
${TORCH_SRC_DIR}/csrc/api/src/nn/options/dropout.cpp
${TORCH_SRC_DIR}/csrc/api/src/nn/options/linear.cpp
${TORCH_SRC_DIR}/csrc/api/src/nn/options/padding.cpp
${TORCH_SRC_DIR}/csrc/api/src/nn/options/pooling.cpp
${TORCH_SRC_DIR}/csrc/api/src/nn/options/rnn.cpp
${TORCH_SRC_DIR}/csrc/api/src/nn/options/vision.cpp
${TORCH_SRC_DIR}/csrc/api/src/optim/adagrad.cpp
${TORCH_SRC_DIR}/csrc/api/src/optim/adam.cpp
${TORCH_SRC_DIR}/csrc/api/src/optim/adamw.cpp
${TORCH_SRC_DIR}/csrc/api/src/optim/lbfgs.cpp
${TORCH_SRC_DIR}/csrc/api/src/optim/optimizer.cpp
${TORCH_SRC_DIR}/csrc/api/src/optim/rmsprop.cpp
${TORCH_SRC_DIR}/csrc/api/src/optim/serialize.cpp
${TORCH_SRC_DIR}/csrc/api/src/optim/sgd.cpp
${TORCH_SRC_DIR}/csrc/api/src/serialize/input-archive.cpp
${TORCH_SRC_DIR}/csrc/api/src/serialize/output-archive.cpp
)
endif()
list(APPEND Caffe2_CPU_SRCS ${TORCH_SRCS})
endif()
# NOTE [ Linking AVX and non-AVX files ]
#
# Regardless of the CPU capabilities, we build some files with AVX and AVX2
# instruction set. If the host CPU doesn't support those, we simply ignore their
# functions at runtime during dispatch.
#
# We must make sure that those files are at the end of the input list when
# linking the torch_cpu library. Otherwise, the following error scenario might
# occur:
# 1. A non-AVX and an AVX file both call a function defined with the `inline`
# keyword
# 2. The compiler decides not to inline this function
# 3. Two different versions of the machine code are generated for this function:
# one without AVX instructions and one with AVX.
# 4. When linking, the AVX version is found earlier in the input object files,
# so the linker makes the entire library use it, even in code not guarded by
# the dispatcher.
# 5. A CPU without AVX support executes this function, encounters an AVX
# instruction and crashes.
#
# Thus we organize the input files in the following order:
# 1. All files with no AVX support
# 2. All files with AVX support (conveniently, they all have names ending with
# 'AVX.cpp')
# 3. All files with AVX2 support ('*AVX2.cpp')
set(Caffe2_CPU_SRCS_NON_AVX)
set(Caffe2_CPU_SRCS_AVX)
set(Caffe2_CPU_SRCS_AVX2)
foreach(input_filename ${Caffe2_CPU_SRCS})
if(${input_filename} MATCHES "AVX\\.cpp")
list(APPEND Caffe2_CPU_SRCS_AVX ${input_filename})
elseif(${input_filename} MATCHES "AVX2\\.cpp")
list(APPEND Caffe2_CPU_SRCS_AVX2 ${input_filename})
else()
list(APPEND Caffe2_CPU_SRCS_NON_AVX ${input_filename})
endif()
endforeach(input_filename)
set(Caffe2_CPU_SRCS ${Caffe2_CPU_SRCS_NON_AVX} ${Caffe2_CPU_SRCS_AVX} ${Caffe2_CPU_SRCS_AVX2})
# ==========================================================
# END formerly-libtorch sources
# ==========================================================
add_library(torch_cpu ${Caffe2_CPU_SRCS})
if(HAVE_SOVERSION)
set_target_properties(torch_cpu PROPERTIES
VERSION ${TORCH_VERSION} SOVERSION ${TORCH_SOVERSION})
endif()
torch_compile_options(torch_cpu) # see cmake/public/utils.cmake
if(USE_LLVM AND LLVM_FOUND)
llvm_map_components_to_libnames(LLVM_LINK_LIBS
support core analysis executionengine instcombine
scalaropts transformutils native orcjit)
target_link_libraries(torch_cpu PRIVATE ${LLVM_LINK_LIBS})
endif(USE_LLVM AND LLVM_FOUND)
# This is required for older versions of CMake, which don't allow
# specifying add_library() without a list of source files
set(DUMMY_EMPTY_FILE ${CMAKE_BINARY_DIR}/empty.cpp)
if(MSVC)
set(DUMMY_FILE_CONTENT "__declspec(dllexport) int ignore_this_library_placeholder(){return 0\\;}")
else()
set(DUMMY_FILE_CONTENT "")
endif()
file(WRITE ${DUMMY_EMPTY_FILE} ${DUMMY_FILE_CONTENT})
# Wrapper library for people who link against torch and expect both CPU and CUDA support
# Contains "torch_cpu" and "torch_cuda"
add_library(torch ${DUMMY_EMPTY_FILE})
if(HAVE_SOVERSION)
set_target_properties(torch PROPERTIES
VERSION ${TORCH_VERSION} SOVERSION ${TORCH_SOVERSION})
endif()
if(USE_ROCM)
filter_list(__caffe2_hip_srcs_cpp Caffe2_HIP_SRCS "\\.(cu|hip)$")
set_source_files_properties(${__caffe2_hip_srcs_cpp} PROPERTIES HIP_SOURCE_PROPERTY_FORMAT 1)
endif()
# Compile exposed libraries.
if(USE_ROCM)
set(CUDA_LINK_LIBRARIES_KEYWORD PRIVATE)
hip_add_library(torch_hip ${Caffe2_HIP_SRCS})
set(CUDA_LINK_LIBRARIES_KEYWORD)
torch_compile_options(torch_hip) # see cmake/public/utils.cmake
# TODO: Not totally sure if this is live or not
if(USE_NCCL)
target_link_libraries(torch_hip PRIVATE __caffe2_nccl)
target_compile_definitions(torch_hip PRIVATE USE_NCCL)
endif()
elseif(USE_CUDA)
set(CUDA_LINK_LIBRARIES_KEYWORD PRIVATE)
if(CUDA_SEPARABLE_COMPILATION)
# Separate compilation fails when kernels using `thrust::sort_by_key`
# are linked with the rest of CUDA code. Workaround by linking the separateley
set(_generated_name "torch_cuda_w_sort_by_key_intermediate_link${CMAKE_C_OUTPUT_EXTENSION}")
set(torch_cuda_w_sort_by_key_link_file "${CMAKE_CURRENT_BINARY_DIR}/CMakeFiles/torch_cuda.dir/${CMAKE_CFG_INTDIR}/${_generated_name}")
cuda_wrap_srcs(torch_cuda OBJ Caffe2_GPU_W_SORT_BY_KEY_OBJ ${Caffe2_GPU_SRCS_W_SORT_BY_KEY})
CUDA_LINK_SEPARABLE_COMPILATION_OBJECTS("${torch_cuda_w_sort_by_key_link_file}" torch_cpu "${_options}" "${torch_cuda_SEPARABLE_COMPILATION_OBJECTS}")
set( torch_cuda_SEPARABLE_COMPILATION_OBJECTS )
# Pass compiled sort-by-key object + device-linked fatbin as extra dependencies of torch_cuda
cuda_add_library(torch_cuda ${Caffe2_GPU_SRCS} ${torch_cuda_w_sort_by_key_link_file} ${Caffe2_GPU_W_SORT_BY_KEY_OBJ})
else()
cuda_add_library(torch_cuda ${Caffe2_GPU_SRCS} ${Caffe2_GPU_SRCS_W_SORT_BY_KEY})
endif()
set(CUDA_LINK_LIBRARIES_KEYWORD)
torch_compile_options(torch_cuda) # see cmake/public/utils.cmake
if(USE_NCCL)
target_link_libraries(torch_cuda PRIVATE __caffe2_nccl)
target_compile_definitions(torch_cuda PRIVATE USE_NCCL)
endif()
endif()
if(NOT MSVC AND USE_XNNPACK)
TARGET_LINK_LIBRARIES(torch_cpu PRIVATE fxdiv)
endif()
# ==========================================================
# formerly-libtorch flags
# ==========================================================
if(NOT INTERN_BUILD_MOBILE)
# Forces caffe2.pb.h to be generated before its dependents are compiled.
# Adding the generated header file to the ${TORCH_SRCS} list is not sufficient
# to establish the dependency, since the generation procedure is declared in a different CMake file.
# See https://samthursfield.wordpress.com/2015/11/21/cmake-dependencies-between-targets-and-files-and-custom-commands/#custom-commands-in-different-directories
add_dependencies(torch_cpu Caffe2_PROTO)
endif()
if(NOT INTERN_BUILD_MOBILE OR NOT BUILD_CAFFE2_MOBILE)
if(NOT NO_API)
target_include_directories(torch_cpu PRIVATE
${TORCH_SRC_DIR}/csrc/api
${TORCH_SRC_DIR}/csrc/api/include)
endif()
if(USE_CUDA AND MSVC)
# -INCLUDE is used to ensure torch_cuda is linked against in a project that relies on it.
# Related issue: https://github.com/pytorch/pytorch/issues/31611
target_link_libraries(torch_cuda INTERFACE "-INCLUDE:?warp_size@cuda@at@@YAHXZ")
endif()
set(TH_CPU_INCLUDE
# dense
aten/src/TH
${CMAKE_CURRENT_BINARY_DIR}/aten/src/TH
${TORCH_ROOT}/aten/src
${CMAKE_CURRENT_BINARY_DIR}/aten/src
${CMAKE_BINARY_DIR}/aten/src)
target_include_directories(torch_cpu PRIVATE ${TH_CPU_INCLUDE})
set(ATen_CPU_INCLUDE
${TORCH_ROOT}/aten/src
${CMAKE_CURRENT_BINARY_DIR}/../aten/src
${CMAKE_CURRENT_BINARY_DIR}/../aten/src/ATen
${CMAKE_BINARY_DIR}/aten/src)
if(USE_TBB)
list(APPEND ATen_CPU_INCLUDE ${TBB_ROOT_DIR}/include)
target_link_libraries(torch_cpu PUBLIC tbb)
endif()
target_include_directories(torch_cpu PRIVATE ${ATen_CPU_INCLUDE})
target_include_directories(torch_cpu PRIVATE
${TORCH_SRC_DIR}/csrc)
target_include_directories(torch_cpu PRIVATE
${TORCH_ROOT}/third_party/miniz-2.0.8)
install(DIRECTORY "${TORCH_SRC_DIR}/csrc"
DESTINATION ${TORCH_INSTALL_INCLUDE_DIR}/torch
FILES_MATCHING PATTERN "*.h")
install(FILES
"${TORCH_SRC_DIR}/script.h"
"${TORCH_SRC_DIR}/extension.h"
"${TORCH_SRC_DIR}/custom_class.h"
"${TORCH_SRC_DIR}/library.h"
"${TORCH_SRC_DIR}/custom_class_detail.h"
DESTINATION ${TORCH_INSTALL_INCLUDE_DIR}/torch)
if(BUILD_TEST AND NOT USE_ROCM)
add_subdirectory(${TORCH_ROOT}/test/cpp/jit ${CMAKE_BINARY_DIR}/test_jit)
add_subdirectory(${TORCH_ROOT}/test/cpp/tensorexpr ${CMAKE_BINARY_DIR}/test_tensorexpr)
if(USE_DISTRIBUTED)
add_subdirectory(${TORCH_ROOT}/test/cpp/rpc ${CMAKE_BINARY_DIR}/test_cpp_rpc)
endif()
endif()
if(BUILD_TEST AND NOT NO_API)
add_subdirectory(${TORCH_ROOT}/test/cpp/api ${CMAKE_BINARY_DIR}/test_api)
add_subdirectory(${TORCH_ROOT}/test/cpp/dist_autograd ${CMAKE_BINARY_DIR}/dist_autograd)
endif()
# XXX This ABI check cannot be run with arm-linux-androideabi-g++
if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU")
if(DEFINED GLIBCXX_USE_CXX11_ABI)
message(STATUS "_GLIBCXX_USE_CXX11_ABI is already defined as a cmake variable")
else()
message(STATUS "${CMAKE_CXX_COMPILER} ${TORCH_SRC_DIR}/abi-check.cpp -o ${CMAKE_BINARY_DIR}/abi-check")
execute_process(
COMMAND
"${CMAKE_CXX_COMPILER}"
"${TORCH_SRC_DIR}/abi-check.cpp"
"-o"
"${CMAKE_BINARY_DIR}/abi-check"
RESULT_VARIABLE ABI_CHECK_COMPILE_RESULT)
if(ABI_CHECK_COMPILE_RESULT)
message(FATAL_ERROR "Could not compile ABI Check: ${ABI_CHECK_COMPILE_RESULT}")
endif()
execute_process(
COMMAND "${CMAKE_BINARY_DIR}/abi-check"
RESULT_VARIABLE ABI_CHECK_RESULT
OUTPUT_VARIABLE GLIBCXX_USE_CXX11_ABI)
if(ABI_CHECK_RESULT)
message(WARNING "Could not run ABI Check: ${ABI_CHECK_RESULT}")
endif()
endif()
message(STATUS "Determined _GLIBCXX_USE_CXX11_ABI=${GLIBCXX_USE_CXX11_ABI}")
endif()
# CMake config for external projects.
configure_file(
${PROJECT_SOURCE_DIR}/cmake/TorchConfigVersion.cmake.in
${PROJECT_BINARY_DIR}/TorchConfigVersion.cmake
@ONLY)
configure_file(
${TORCH_ROOT}/cmake/TorchConfig.cmake.in
${PROJECT_BINARY_DIR}/TorchConfig.cmake
@ONLY)
install(FILES
${PROJECT_BINARY_DIR}/TorchConfigVersion.cmake
${PROJECT_BINARY_DIR}/TorchConfig.cmake
DESTINATION share/cmake/Torch)
if(USE_DISTRIBUTED)
if(NOT MSVC)
add_subdirectory(${TORCH_SRC_DIR}/lib/c10d lib_c10d)
endif()
endif()
# ---[ Torch python bindings build
add_subdirectory(../torch torch)
endif()
# ==========================================================
# END formerly-libtorch flags
# ==========================================================
if(NOT NO_API)
target_include_directories(torch_cpu PUBLIC
$<BUILD_INTERFACE:${TORCH_SRC_DIR}/csrc/api>
$<BUILD_INTERFACE:${TORCH_SRC_DIR}/csrc/api/include>)
endif()
if(USE_OPENMP)
find_package(OpenMP QUIET)
endif()
if(USE_OPENMP AND OPENMP_FOUND)
message(STATUS "pytorch is compiling with OpenMP. \n"
"OpenMP CXX_FLAGS: ${OpenMP_CXX_FLAGS}. \n"
"OpenMP libraries: ${OpenMP_CXX_LIBRARIES}.")
target_compile_options(torch_cpu PRIVATE ${OpenMP_CXX_FLAGS})
target_link_libraries(torch_cpu PRIVATE ${OpenMP_CXX_LIBRARIES})
endif()
if(USE_ROCM)
target_compile_definitions(torch_hip PRIVATE
USE_ROCM
__HIP_PLATFORM_HCC__
)
# NB: Massive hack. torch/csrc/jit/codegen/fuser/codegen.cpp includes
# torch/csrc/jit/codegen/fuser/cuda/resource_strings.h which changes the
# strings depending on if you're __HIP_PLATFORM_HCC__ or not.
# But that file is in torch_cpu! So, against all odds, this macro
# has to be set on torch_cpu too. I also added it to torch for
# better luck
target_compile_definitions(torch_cpu PRIVATE
USE_ROCM
__HIP_PLATFORM_HCC__
)
target_compile_definitions(torch PRIVATE
USE_ROCM
__HIP_PLATFORM_HCC__
)
target_include_directories(torch_hip PRIVATE
/opt/rocm/include
/opt/rocm/hcc/include
/opt/rocm/rocblas/include
/opt/rocm/hipsparse/include
)
endif()
# Pass USE_DISTRIBUTED to torch_cpu, as some codes in jit/pickler.cpp and
# jit/unpickler.cpp need to be compiled only when USE_DISTRIBUTED is set
if(USE_DISTRIBUTED)
target_compile_definitions(torch_cpu PRIVATE
USE_DISTRIBUTED
)
# Pass USE_TENSORPIPE to torch_cpu as some parts of rpc/utils.cpp
# can only be compiled with USE_TENSORPIPE is set.
if(USE_TENSORPIPE)
target_compile_definitions(torch_cpu PRIVATE
USE_TENSORPIPE
)
endif()
endif()
if(NOT INTERN_BUILD_MOBILE OR BUILD_CAFFE2_MOBILE)
caffe2_interface_library(caffe2_protos caffe2_protos_whole)
target_link_libraries(torch_cpu PRIVATE caffe2_protos_whole)
if(${CAFFE2_LINK_LOCAL_PROTOBUF})
target_link_libraries(torch_cpu INTERFACE protobuf::libprotobuf)
else()
target_link_libraries(torch_cpu PUBLIC protobuf::libprotobuf)
endif()
endif()
if(USE_OPENMP AND OPENMP_FOUND)
message(STATUS "Caffe2 is compiling with OpenMP. \n"
"OpenMP CXX_FLAGS: ${OpenMP_CXX_FLAGS}. \n"
"OpenMP libraries: ${OpenMP_CXX_LIBRARIES}.")
target_link_libraries(torch_cpu PRIVATE ${OpenMP_CXX_LIBRARIES})
endif()
if($ENV{TH_BINARY_BUILD})
if(NOT MSVC AND USE_CUDA AND NOT APPLE)
# Note [Extra MKL symbols for MAGMA in torch_cpu]
#
# When we build CUDA libraries and link against MAGMA, MAGMA makes use of
# some BLAS symbols in its CPU fallbacks when it has no GPU versions
# of kernels. Previously, we ensured the BLAS symbols were filled in by
# MKL by linking torch_cuda with BLAS, but when we are statically linking
# against MKL (when we do wheel builds), this actually ends up pulling in a
# decent chunk of MKL into torch_cuda, inflating our torch_cuda binary
# size by 8M. torch_cpu exposes most of the MKL symbols we need, but
# empirically we determined that there are four which it doesn't provide. If
# we link torch_cpu with these --undefined symbols, we can ensure they
# do get pulled in, and then we can avoid statically linking in MKL to
# torch_cuda at all!
#
# We aren't really optimizing for binary size on Windows (and this link
# line doesn't work on Windows), so don't do it there.
#
# These linker commands do not work on OS X, do not attempt this there.
# (It shouldn't matter anyway, though, because OS X has dropped CUDA support)
set_target_properties(torch_cpu PROPERTIES LINK_FLAGS "-Wl,--undefined=mkl_lapack_slaed0 -Wl,--undefined=mkl_lapack_dlaed0 -Wl,--undefined=mkl_lapack_dormql -Wl,--undefined=mkl_lapack_sormql")
endif()
endif()
target_link_libraries(torch_cpu PUBLIC c10)
target_link_libraries(torch_cpu PUBLIC ${Caffe2_PUBLIC_DEPENDENCY_LIBS})
target_link_libraries(torch_cpu PRIVATE ${Caffe2_DEPENDENCY_LIBS})
target_link_libraries(torch_cpu PRIVATE ${Caffe2_DEPENDENCY_WHOLE_LINK_LIBS})
target_include_directories(torch_cpu INTERFACE $<INSTALL_INTERFACE:include>)
target_include_directories(torch_cpu PRIVATE ${Caffe2_CPU_INCLUDE})
target_include_directories(torch_cpu SYSTEM PRIVATE "${Caffe2_DEPENDENCY_INCLUDE}")
# Set standard properties on the target
torch_set_target_props(torch_cpu)
target_compile_options(torch_cpu PRIVATE "-DCAFFE2_BUILD_MAIN_LIB")
if(USE_CUDA)
target_compile_options(torch_cuda PRIVATE "-DTORCH_CUDA_BUILD_MAIN_LIB")
# NB: This must be target_compile_definitions, not target_compile_options,
# as the latter is not respected by nvcc
target_compile_definitions(torch_cuda PRIVATE "-DTORCH_CUDA_BUILD_MAIN_LIB")
elseif(USE_ROCM)
target_compile_options(torch_hip PRIVATE "-DTORCH_HIP_BUILD_MAIN_LIB")
target_compile_definitions(torch_hip PRIVATE "-DTORCH_HIP_BUILD_MAIN_LIB")
endif()
set(EXPERIMENTAL_SINGLE_THREAD_POOL "0" CACHE STRING
"Experimental option to use a single thread pool for inter- and intra-op parallelism")
if("${EXPERIMENTAL_SINGLE_THREAD_POOL}")
target_compile_definitions(torch_cpu PUBLIC "-DAT_EXPERIMENTAL_SINGLE_THREAD_POOL=1")
endif()
if(MSVC AND NOT BUILD_SHARED_LIBS)
# Note [Supporting both static and dynamic libraries on Windows]
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# A Windows library may be distributed as either a static or dynamic
# library. The chosen distribution mechanism affects how you setup
# the headers for the library: if you statically link a function,
# all you need is an ordinary signature:
#
# void f();
#
# But if you *dynamically* link it, then you must provide a __declspec
# specifying that it should be imported from a DLL:
#
# __declspec(dllimport) void f();
#
# Mixing the two situations will not work: if you specify dllimport
# while statically linking, the linker will complain it cannot find
# the __imp_f symbol (which serve as the DLL entrypoint); if you
# fail to specify dllimport for a symbol that's coming from a DLL,
# the linker will complain that it can't find f. Joy!
#
# Most places on the Internet, you will find people have written