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CMakeLists.txt
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# ---[ Generate and install header and cpp files
include(../cmake/Codegen.cmake)
# ---[ 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)
set(AT_LINK_STYLE INTERFACE)
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_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_HIP_SRCS ${ATen_HIP_SRCS})
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_CPU_INCLUDE ${ATen_CPU_INCLUDE})
list(APPEND Caffe2_GPU_INCLUDE ${ATen_CUDA_INCLUDE})
list(APPEND Caffe2_HIP_INCLUDE ${ATen_HIP_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})
else()
# Only add "ATen Core", a minimal, easy-to-compile fragment of ATen.
# This codepath should only be exercised by the Android build.
add_subdirectory(../aten/src/ATen/core ATen_core)
list(APPEND Caffe2_CPU_SRCS ${ATen_CORE_SRCS})
list(APPEND Caffe2_CPU_INCLUDE ${ATen_CORE_INCLUDE})
list(APPEND Caffe2_CPU_TEST_SRCS ${ATen_CORE_TEST_SRCS})
# See cmake/Codegen.cmake for header installation
endif()
# ---[ Caffe2 build
# Note: the folders that are being commented out have not been properly
# addressed yet.
if(NOT BUILD_ATEN_ONLY)
add_subdirectory(proto)
add_subdirectory(contrib)
add_subdirectory(core)
add_subdirectory(utils)
add_subdirectory(predictor)
add_subdirectory(predictor/emulator)
add_subdirectory(core/nomnigraph)
add_subdirectory(serialize)
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(perfkernels)
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()
endif()
# ---[ List of libraries to link with
if(NOT BUILD_ATEN_ONLY)
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"
"${TORCH_SRC_DIR}/csrc/autograd/generated/VariableType_0.cpp"
"${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/jit/generated/register_aten_ops_0.cpp"
"${TORCH_SRC_DIR}/csrc/jit/generated/register_aten_ops_1.cpp"
"${TORCH_SRC_DIR}/csrc/jit/generated/register_aten_ops_2.cpp"
)
set(GENERATED_H_TORCH
"${TORCH_SRC_DIR}/csrc/autograd/generated/VariableType.h"
"${TORCH_SRC_DIR}/csrc/autograd/generated/Functions.h"
"${TORCH_SRC_DIR}/csrc/autograd/generated/variable_factories.h"
)
set(GENERATED_THNN_CXX_CUDA ${TORCH_SRC_DIR}/csrc/nn/THCUNN.cpp)
set(GENERATED_THNN_CXX ${TORCH_SRC_DIR}/csrc/nn/THNN.cpp)
set(GENERATED_CXX_PYTHON
"${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"
"${TORCH_SRC_DIR}/csrc/autograd/generated/python_variable_methods_dispatch.h"
"${TORCH_SRC_DIR}/csrc/autograd/generated/python_torch_functions_dispatch.h"
"${TORCH_SRC_DIR}/csrc/autograd/generated/python_nn_functions.h"
"${TORCH_SRC_DIR}/csrc/autograd/generated/python_nn_functions_dispatch.h"
)
set(GENERATED_THNN_SOURCES
${GENERATED_THNN_CXX}
${GENERATED_THNN_CXX_CUDA}
)
set(TORCH_GENERATED_CODE
${GENERATED_CXX_TORCH}
${GENERATED_THNN_SOURCES}
${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 "${CMAKE_BINARY_DIR}/aten/src/ATen/Declarations.yaml"
--nn-path "aten/src"
DEPENDS
"${CMAKE_BINARY_DIR}/aten/src/ATen/Declarations.yaml"
"${CMAKE_CURRENT_LIST_DIR}/../aten/src/THNN/generic/THNN.h"
"${TOOLS_PATH}/autograd/templates/VariableType.h"
"${TOOLS_PATH}/autograd/templates/VariableType.cpp"
"${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"
"${TOOLS_PATH}/autograd/templates/python_variable_methods_dispatch.h"
"${TOOLS_PATH}/autograd/templates/python_torch_functions.cpp"
"${TOOLS_PATH}/autograd/templates/python_torch_functions_dispatch.h"
"${TOOLS_PATH}/autograd/templates/python_nn_functions.cpp"
"${TOOLS_PATH}/autograd/templates/python_nn_functions.h"
"${TOOLS_PATH}/autograd/templates/python_nn_functions_dispatch.h"
"${TOOLS_PATH}/autograd/templates/variable_factories.h"
"${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_jit_dispatch.py"
"${TOOLS_PATH}/jit/templates/register_aten_ops.cpp"
WORKING_DIRECTORY "${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}
${GENERATED_H_TORCH}
${TORCH_SRC_DIR}/csrc/autograd/anomaly_mode.cpp
${TORCH_SRC_DIR}/csrc/autograd/engine.cpp
${TORCH_SRC_DIR}/csrc/autograd/function.cpp
${TORCH_SRC_DIR}/csrc/autograd/function_hook.cpp
${TORCH_SRC_DIR}/csrc/autograd/functions/accumulate_grad.cpp
${TORCH_SRC_DIR}/csrc/autograd/functions/basic_ops.cpp
${TORCH_SRC_DIR}/csrc/autograd/functions/tensor.cpp
${TORCH_SRC_DIR}/csrc/autograd/functions/utils.cpp
${TORCH_SRC_DIR}/csrc/autograd/grad_mode.cpp
${TORCH_SRC_DIR}/csrc/autograd/input_buffer.cpp
${TORCH_SRC_DIR}/csrc/autograd/profiler.cpp
${TORCH_SRC_DIR}/csrc/autograd/record_function.cpp
${TORCH_SRC_DIR}/csrc/autograd/saved_variable.cpp
${TORCH_SRC_DIR}/csrc/autograd/variable.cpp
${TORCH_SRC_DIR}/csrc/autograd/VariableTypeManual.cpp
${TORCH_SRC_DIR}/csrc/jit/autodiff.cpp
${TORCH_SRC_DIR}/csrc/jit/attributes.cpp
${TORCH_SRC_DIR}/csrc/jit/argument_spec.cpp
${TORCH_SRC_DIR}/csrc/jit/export.cpp
${TORCH_SRC_DIR}/csrc/jit/pass_manager.cpp
${TORCH_SRC_DIR}/csrc/jit/pickler.cpp
${TORCH_SRC_DIR}/csrc/jit/graph_executor.cpp
${TORCH_SRC_DIR}/csrc/jit/import_source.cpp
${TORCH_SRC_DIR}/csrc/jit/import.cpp
${TORCH_SRC_DIR}/csrc/jit/import_export_helpers.cpp
${TORCH_SRC_DIR}/csrc/jit/interpreter.cpp
${TORCH_SRC_DIR}/csrc/jit/constants.cpp
${TORCH_SRC_DIR}/csrc/jit/node_hashing.cpp
${TORCH_SRC_DIR}/csrc/jit/ir.cpp
${TORCH_SRC_DIR}/csrc/jit/irparser.cpp
${TORCH_SRC_DIR}/csrc/jit/jit_log.cpp
${TORCH_SRC_DIR}/csrc/jit/netdef_converter.cpp
${TORCH_SRC_DIR}/csrc/jit/operator.cpp
${TORCH_SRC_DIR}/csrc/jit/register_c10_ops.cpp
${TORCH_SRC_DIR}/csrc/jit/subgraph_matcher.cpp
${TORCH_SRC_DIR}/csrc/jit/symbolic_script.cpp
${TORCH_SRC_DIR}/csrc/jit/profiling_record.cpp
${TORCH_SRC_DIR}/csrc/jit/profiling_graph_executor_impl.cpp
${TORCH_SRC_DIR}/csrc/jit/passes/alias_analysis.cpp
${TORCH_SRC_DIR}/csrc/jit/passes/batch_mm.cpp
${TORCH_SRC_DIR}/csrc/jit/passes/bailout_graph.cpp
${TORCH_SRC_DIR}/csrc/jit/passes/canonicalize.cpp
${TORCH_SRC_DIR}/csrc/jit/passes/constant_propagation.cpp
${TORCH_SRC_DIR}/csrc/jit/passes/constant_pooling.cpp
${TORCH_SRC_DIR}/csrc/jit/passes/common_subexpression_elimination.cpp
${TORCH_SRC_DIR}/csrc/jit/passes/create_autodiff_subgraphs.cpp
${TORCH_SRC_DIR}/csrc/jit/passes/inline_autodiff_subgraphs.cpp
${TORCH_SRC_DIR}/csrc/jit/passes/insert_guards.cpp
${TORCH_SRC_DIR}/csrc/jit/passes/inliner.cpp
${TORCH_SRC_DIR}/csrc/jit/passes/lift_closures.cpp
${TORCH_SRC_DIR}/csrc/jit/passes/inline_forked_closures.cpp
${TORCH_SRC_DIR}/csrc/jit/passes/dead_code_elimination.cpp
${TORCH_SRC_DIR}/csrc/jit/passes/decompose_ops.cpp
${TORCH_SRC_DIR}/csrc/jit/passes/canonicalize_ops.cpp
${TORCH_SRC_DIR}/csrc/jit/passes/erase_number_types.cpp
${TORCH_SRC_DIR}/csrc/jit/passes/inline_fork_wait.cpp
${TORCH_SRC_DIR}/csrc/jit/passes/graph_fuser.cpp
${TORCH_SRC_DIR}/csrc/jit/passes/guard_elimination.cpp
${TORCH_SRC_DIR}/csrc/jit/passes/inplace_check.cpp
${TORCH_SRC_DIR}/csrc/jit/passes/liveness.cpp
${TORCH_SRC_DIR}/csrc/jit/passes/loop_unrolling.cpp
${TORCH_SRC_DIR}/csrc/jit/passes/lower_grad_of.cpp
${TORCH_SRC_DIR}/csrc/jit/passes/lower_tuples.cpp
${TORCH_SRC_DIR}/csrc/jit/passes/peephole.cpp
${TORCH_SRC_DIR}/csrc/jit/passes/remove_expands.cpp
${TORCH_SRC_DIR}/csrc/jit/passes/remove_inplace_ops.cpp
${TORCH_SRC_DIR}/csrc/jit/passes/shape_analysis.cpp
${TORCH_SRC_DIR}/csrc/jit/passes/requires_grad_analysis.cpp
${TORCH_SRC_DIR}/csrc/jit/passes/specialize_autogradzero.cpp
${TORCH_SRC_DIR}/csrc/jit/passes/subgraph_rewrite.cpp
${TORCH_SRC_DIR}/csrc/jit/passes/python_print.cpp
${TORCH_SRC_DIR}/csrc/jit/passes/utils/subgraph_utils.cpp
${TORCH_SRC_DIR}/csrc/jit/passes/utils/check_alias_annotation.cpp
${TORCH_SRC_DIR}/csrc/jit/passes/utils/memory_dag.cpp
${TORCH_SRC_DIR}/csrc/jit/passes/quantization.cpp
${TORCH_SRC_DIR}/csrc/jit/print_handler.cpp
${TORCH_SRC_DIR}/csrc/jit/fuser/interface.cpp
${TORCH_SRC_DIR}/csrc/jit/register_prim_ops.cpp
${TORCH_SRC_DIR}/csrc/jit/register_string_ops.cpp
${TORCH_SRC_DIR}/csrc/jit/register_special_ops.cpp
${TORCH_SRC_DIR}/csrc/jit/scope.cpp
${TORCH_SRC_DIR}/csrc/jit/script/compiler.cpp
${TORCH_SRC_DIR}/csrc/api/src/jit.cpp
${TORCH_SRC_DIR}/csrc/jit/testing/file_check.cpp
${TORCH_SRC_DIR}/csrc/jit/script/final_returns.cpp
${TORCH_SRC_DIR}/csrc/jit/script/convert_to_ssa.cpp
${TORCH_SRC_DIR}/csrc/jit/script/schema_matching.cpp
${TORCH_SRC_DIR}/csrc/jit/script/script_type_parser.cpp
${TORCH_SRC_DIR}/csrc/jit/script/sugared_value.cpp
${TORCH_SRC_DIR}/csrc/jit/script/class_type.cpp
${TORCH_SRC_DIR}/csrc/jit/script/parser.cpp
${TORCH_SRC_DIR}/csrc/jit/script/builtin_functions.cpp
${TORCH_SRC_DIR}/csrc/jit/script/edit_distance.cpp
${TORCH_SRC_DIR}/csrc/jit/script/logging.cpp
${TORCH_SRC_DIR}/csrc/jit/script/module.cpp
${TORCH_SRC_DIR}/csrc/jit/script/jit_exception.cpp
${TORCH_SRC_DIR}/csrc/jit/source_range_serialization.cpp
${TORCH_SRC_DIR}/csrc/jit/tracer.cpp
${TORCH_SRC_DIR}/csrc/jit/hooks_for_testing.cpp
${TORCH_SRC_DIR}/csrc/utils/tensor_flatten.cpp
${TORCH_SRC_DIR}/csrc/utils/variadic.cpp
${TORCH_SRC_DIR}/csrc/jit/fuser/kernel_cache.cpp
${TORCH_SRC_DIR}/csrc/jit/fuser/compiler.cpp
${TORCH_SRC_DIR}/csrc/jit/fuser/executor.cpp
${TORCH_SRC_DIR}/csrc/jit/fuser/codegen.cpp
${TORCH_SRC_DIR}/csrc/jit/fuser/fallback.cpp
${TORCH_SRC_DIR}/csrc/jit/function.cpp
${TORCH_ROOT}/test/cpp/jit/test.cpp
)
if (WIN32)
list(APPEND TORCH_SRCS
${TORCH_SRC_DIR}/csrc/jit/fuser/cpu/dynamic_library_win.cpp
)
if (USE_CUDA AND NOT USE_ROCM)
list(APPEND Caffe2_GPU_SRCS
${TORCH_SRC_DIR}/csrc/jit/fuser/cuda/fused_kernel.cpp
)
add_library(thnvrtc SHARED ${TORCH_SRC_DIR}/csrc/jit/fuser/cuda/thnvrtc.cpp)
target_link_libraries(thnvrtc ${CUDA_NVRTC} ${CUDA_CUDA_LIB} ${CUDA_NVRTC_LIB})
target_include_directories(thnvrtc PRIVATE ${CUDA_INCLUDE_DIRS})
install(TARGETS thnvrtc DESTINATION "${TORCH_INSTALL_LIB_DIR}")
endif()
else ()
list(APPEND TORCH_SRCS
${TORCH_SRC_DIR}/csrc/jit/fuser/cpu/dynamic_library_unix.cpp
${TORCH_SRC_DIR}/csrc/jit/fuser/cpu/fused_kernel.cpp
)
if (USE_CUDA AND NOT USE_ROCM)
list(APPEND Caffe2_GPU_SRCS
${TORCH_SRC_DIR}/csrc/jit/fuser/cuda/fused_kernel.cpp
)
add_library(thnvrtc SHARED ${TORCH_SRC_DIR}/csrc/jit/fuser/cuda/thnvrtc.cpp)
target_link_libraries(thnvrtc ${CUDA_NVRTC} ${CUDA_CUDA_LIB} ${CUDA_NVRTC_LIB})
target_include_directories(thnvrtc PRIVATE ${CUDA_INCLUDE_DIRS})
install(TARGETS thnvrtc DESTINATION "${TORCH_INSTALL_LIB_DIR}")
endif()
endif ()
if (USE_CUDA)
list(APPEND Caffe2_GPU_SRCS
${TORCH_SRC_DIR}/csrc/autograd/profiler_cuda.cpp
${TORCH_SRC_DIR}/csrc/autograd/functions/comm.cpp
${TORCH_SRC_DIR}/csrc/cuda/comm.cpp
)
endif()
if (USE_ROCM)
list(APPEND Caffe2_HIP_SRCS
${TORCH_SRC_DIR}/csrc/autograd/functions/comm.cpp
${TORCH_SRC_DIR}/csrc/cuda/comm.cpp
)
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/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/batchnorm.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/embedding.cpp
${TORCH_SRC_DIR}/csrc/api/src/nn/modules/functional.cpp
${TORCH_SRC_DIR}/csrc/api/src/nn/modules/linear.cpp
${TORCH_SRC_DIR}/csrc/api/src/nn/modules/named_any.cpp
${TORCH_SRC_DIR}/csrc/api/src/nn/modules/rnn.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/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()
# ==========================================================
# END formerly-libtorch sources
# ==========================================================
# Instead of separate .so libraries, GPU sources are now conditionally
# compiled into the main torch.so library.
if(USE_CUDA)
list(APPEND Caffe2_CPU_SRCS ${Caffe2_GPU_SRCS})
foreach(tmp ${Caffe2_GPU_SRCS})
message(STATUS " " ${tmp})
endforeach()
endif()
if(USE_ROCM)
filter_list(__caffe2_hip_srcs_cpp Caffe2_HIP_SRCS "\\.(cu|hip)$")
set_source_files_properties(${__caffe2_hip_srcs_cpp} PROPERTIES HIP_SOURCE_PROPERTY_FORMAT 1)
list(APPEND Caffe2_CPU_SRCS ${Caffe2_HIP_SRCS})
endif()
# Compile exposed libraries.
IF (USE_ROCM)
set(CUDA_LINK_LIBRARIES_KEYWORD PRIVATE)
hip_add_library(torch ${Caffe2_CPU_SRCS})
set(CUDA_LINK_LIBRARIES_KEYWORD)
ELSEIF(USE_CUDA)
set(CUDA_LINK_LIBRARIES_KEYWORD PRIVATE)
cuda_add_library(torch ${Caffe2_CPU_SRCS})
set(CUDA_LINK_LIBRARIES_KEYWORD)
ELSE()
add_library(torch ${Caffe2_CPU_SRCS})
ENDIF()
# ==========================================================
# formerly-libtorch flags
# ==========================================================
if (NOT INTERN_BUILD_MOBILE OR NOT BUILD_CAFFE2_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 Caffe2_PROTO)
target_compile_definitions(torch PUBLIC _THP_CORE)
# until they can be unified, keep these lists synced with setup.py
if(MSVC)
if (MSVC_Z7_OVERRIDE)
set(MSVC_DEBINFO_OPTION "/Z7")
else()
set(MSVC_DEBINFO_OPTION "/Zi")
endif()
target_compile_options(torch PUBLIC
${MSVC_RUNTIME_LIBRARY_OPTION}
${MSVC_DEBINFO_OPTION}
/EHa
/DNOMINMAX
/wd4267
/wd4251
/wd4522
/wd4522
/wd4838
/wd4305
/wd4244
/wd4190
/wd4101
/wd4996
/wd4275
/bigobj
)
else()
target_compile_options(torch PUBLIC
# -std=c++11
-Wall
-Wextra
-Wno-unused-parameter
-Wno-missing-field-initializers
-Wno-write-strings
-Wno-unknown-pragmas
# Clang has an unfixed bug leading to spurious missing braces
# warnings, see https://bugs.llvm.org/show_bug.cgi?id=21629
-Wno-missing-braces
)
if(NOT APPLE)
target_compile_options(torch PRIVATE
# Considered to be flaky. See the discussion at
# https://github.com/pytorch/pytorch/pull/9608
-Wno-maybe-uninitialized)
endif()
endif()
if (MSVC)
elseif (WERROR)
target_compile_options(torch PRIVATE -Werror -Wno-strict-overflow)
endif()
if (NOT NO_API)
target_include_directories(torch PRIVATE
${TORCH_SRC_DIR}/csrc/api
${TORCH_SRC_DIR}/csrc/api/include)
endif()
if(USE_CUDA)
if(MSVC)
if (NOT NVTOOLEXT_HOME)
set(NVTOOLEXT_HOME "C:/Program Files/NVIDIA Corporation/NvToolsExt")
endif()
if (DEFINED ENV{NVTOOLSEXT_PATH})
set(NVTOOLEXT_HOME $ENV{NVTOOLSEXT_PATH})
file(TO_CMAKE_PATH ${NVTOOLEXT_HOME} NVTOOLEXT_HOME)
endif()
set(TORCH_CUDA_LIBRARIES
${NVTOOLEXT_HOME}/lib/x64/nvToolsExt64_1.lib
${CUDA_LIBRARIES})
target_include_directories(torch PUBLIC "${NVTOOLEXT_HOME}/include")
elseif(APPLE)
set(TORCH_CUDA_LIBRARIES
${CUDA_TOOLKIT_ROOT_DIR}/lib/libcudart.dylib
${CUDA_TOOLKIT_ROOT_DIR}/lib/libnvrtc.dylib
${CUDA_TOOLKIT_ROOT_DIR}/lib/libnvToolsExt.dylib
${CUDA_LIBRARIES})
else()
find_library(LIBNVTOOLSEXT libnvToolsExt.so PATHS ${CUDA_TOOLKIT_ROOT_DIR}/lib64/)
set(TORCH_CUDA_LIBRARIES
${LIBNVTOOLSEXT}
${CUDA_LIBRARIES})
endif()
target_compile_definitions(torch PRIVATE USE_CUDA)
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 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 PUBLIC tbb)
ENDIF()
target_include_directories(torch PRIVATE ${ATen_CPU_INCLUDE})
target_include_directories(torch PRIVATE
${TORCH_SRC_DIR}/csrc)
target_include_directories(torch PRIVATE
${TORCH_ROOT}/third_party/miniz-2.0.8)
set_property(TARGET torch PROPERTY CXX_STANDARD 11)
install(DIRECTORY "${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"
DESTINATION ${TORCH_INSTALL_INCLUDE_DIR}/torch)
if (BUILD_TEST AND NOT MSVC AND NOT USE_ROCM)
add_subdirectory(${TORCH_ROOT}/test/cpp/jit ${CMAKE_BINARY_DIR}/test_jit)
endif()
if (BUILD_TEST AND NOT NO_API)
add_subdirectory(${TORCH_ROOT}/test/cpp/api ${CMAKE_BINARY_DIR}/test_api)
endif()
# XXX This ABI check cannot be run with arm-linux-androideabi-g++
if ("${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
${PROJECT_BINARY_DIR}/TorchConfig.cmake
DESTINATION share/cmake/Torch)
if (USE_DISTRIBUTED)
if (NOT MSVC AND NOT APPLE)
add_subdirectory(${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 PUBLIC
$<BUILD_INTERFACE:${TORCH_SRC_DIR}/csrc/api>
$<BUILD_INTERFACE:${TORCH_SRC_DIR}/csrc/api/include>)
endif()
find_package(OpenMP QUIET)
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 INTERFACE ${OpenMP_CXX_FLAGS})
target_link_libraries(torch PRIVATE ${OpenMP_CXX_LIBRARIES})
endif()
if(USE_ROCM)
target_compile_definitions(torch PRIVATE
USE_ROCM
__HIP_PLATFORM_HCC__
)
target_include_directories(torch PRIVATE
/opt/rocm/include
/opt/rocm/hcc/include
/opt/rocm/rocblas/include
/opt/rocm/hipsparse/include
)
endif()
if (NOT WIN32 AND NOT USE_ASAN)
# Enable hidden visibility by default to make it easier to debug issues with
# TORCH_API annotations. Hidden visibility with selective default visibility
# behaves close enough to Windows' dllimport/dllexport.
#
# Unfortunately, hidden visibility messes up some ubsan warnings because
# templated classes crossing library boundary get duplicated (but identical)
# definitions. It's easier to just disable it.
target_compile_options(torch PRIVATE "-fvisibility=hidden")
endif()
if(NOT BUILD_ATEN_ONLY)
caffe2_interface_library(caffe2_protos caffe2_protos_whole)
target_link_libraries(torch PRIVATE caffe2_protos_whole)
if (${CAFFE2_LINK_LOCAL_PROTOBUF})
target_link_libraries(torch INTERFACE protobuf::libprotobuf)
else()
target_link_libraries(torch 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 PRIVATE ${OpenMP_CXX_LIBRARIES})
endif()
target_link_libraries(torch PUBLIC c10)
target_link_libraries(torch PUBLIC ${Caffe2_PUBLIC_DEPENDENCY_LIBS})
target_link_libraries(torch PRIVATE ${Caffe2_DEPENDENCY_LIBS})
target_link_libraries(torch PRIVATE ${Caffe2_DEPENDENCY_WHOLE_LINK_LIBS})
target_include_directories(torch INTERFACE $<INSTALL_INTERFACE:include>)
target_include_directories(torch PRIVATE ${Caffe2_CPU_INCLUDE})
target_include_directories(torch SYSTEM PRIVATE "${Caffe2_DEPENDENCY_INCLUDE}")
# Set standard properties on the target
torch_set_target_props(torch)
target_compile_options(torch PRIVATE "-DCAFFE2_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 PRIVATE "-DCAFFE2_BUILD_MAIN_LIB")
# Parallelism settings
# OPENMP - OpenMP for intra-op, native thread pool for inter-op parallelism
# NATIVE - using native thread pool for intra- and inter-op parallelism
# NATIVE_TBB - using TBB for intra- and native thread pool for inter-op parallelism
set(PARALLEL_BACKEND "OPENMP" CACHE STRING "ATen parallel backend")
message(STATUS "Using parallel backend: ${PARALLEL_BACKEND}")
if ("${PARALLEL_BACKEND}" STREQUAL "OPENMP")
target_compile_definitions(torch PUBLIC "-DAT_PARALLEL_OPENMP=1")
elseif ("${PARALLEL_BACKEND}" STREQUAL "NATIVE")
target_compile_definitions(torch PUBLIC "-DAT_PARALLEL_NATIVE=1")
elseif ("${PARALLEL_BACKEND}" STREQUAL "NATIVE_TBB")
if (NOT USE_TBB)
message(FATAL_ERROR "Using NATIVE_TBB backend but USE_TBB is off")
endif()
target_compile_definitions(torch PUBLIC "-DAT_PARALLEL_NATIVE_TBB=1")
else()
message(FATAL_ERROR "Unknown parallel backend: ${PARALLEL_BACKEND}")
endif()
set(EXPERIMENTAL_SINGLE_THREAD_POOL "0" CACHE STRING
"Experimental option to use a single thread pool for inter- and intra-op parallelism")
if ("${EXPERIMENTAL_SINGLE_THREAD_POOL}")
target_compile_definitions(torch 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
# their headers under the assumption that the application will
# only ever be dynamically linked, as they define a macro which
# tags a function as __declspec(dllexport) if you are actually
# building the library, and __declspec(dllimport) otherwise. But
# if you want these headers to also work if you are linking against
# a static library, you need a way to avoid adding these __declspec's
# at all. And that "mechanism" needs to apply to any downstream
# libraries/executables which are going to link against your library.
#
# As an aside, why do we need to support both modes?
# For historical reasons, PyTorch ATen on Windows is built dynamically,
# while Caffe2 on Windows is built statically (mostly because if
# we build it dynamically, we are over the DLL exported symbol limit--and
# that is because Caffe2 hasn't comprehensively annotated all symbols
# which cross the DLL boundary with CAFFE_API). So any code
# which is used by both PyTorch and Caffe2 needs to support both
# modes of linking.
#
# So, you have a macro (call it AT_CORE_STATIC_WINDOWS) which you need to have
# set for any downstream library/executable that transitively includes your
# headers. How are you going to do this? You have two options:
#
# 1. Write out a config.h header which stores whether or not
# you are linking statically or dynamically.
#
# 2. Force all of users to set the the macro themselves. If they
# use cmake, you can set -DAT_CORE_STATIC_WINDOWS=1 as a PUBLIC
# compile option, in which case cmake will automatically
# add the macro for you.
#
# Which one is better? Well, it depends: they trade off implementor
# ease versus user ease: (1) is more work for the library author
# but the user doesn't have to worry about it; (2) requires the user
# to set the macro themselves... but only if they don't use cmake.
#
# So, which is appropriate in our situation? In my mind, here is
# the distinguishing factor: it is more common to distribute
# DLLs, since they don't require you to line up the CRT version
# (/MD, /MDd, /MT, /MTd) and MSVC version at the use site. So,
# if a user is already in the business of static linkage, they're
# already in "expert user" realm. So, I've decided that at this
# point in time, the simplicity of implementation of (2) wins out.
#
# NB: This must be target_compile_definitions, not target_compile_options,
# as the latter is not respected by nvcc
target_compile_definitions(torch PUBLIC "AT_CORE_STATIC_WINDOWS=1")
endif()
if (MSVC AND BUILD_SHARED_LIBS)
# ONNX is linked statically and needs to be exported from this library
# to be used externally. Make sure that references match the export.
target_compile_options(torch PRIVATE "-DONNX_BUILD_MAIN_LIB")
endif()
# Use -O2 for release builds (-O3 doesn't improve perf, and -Os results in perf regression)
target_compile_options(torch PRIVATE "$<$<OR:$<CONFIG:Release>,$<CONFIG:RelWithDebInfo>>:-O2>")
install(TARGETS torch EXPORT Caffe2Targets DESTINATION "${TORCH_INSTALL_LIB_DIR}")
caffe2_interface_library(torch torch_library)
list(APPEND Caffe2_MAIN_LIBS torch_library)
if (USE_TBB)
list(APPEND Caffe2_MAIN_LIB tbb)
endif()
# Install PDB files for MSVC builds
if (MSVC AND BUILD_SHARED_LIBS)
install(FILES $<TARGET_PDB_FILE:torch> DESTINATION "${TORCH_INSTALL_LIB_DIR}" OPTIONAL)
endif()
# ---[ CUDA library.
if(USE_CUDA)
target_link_libraries(torch INTERFACE torch::cudart)
target_link_libraries(torch PUBLIC c10_cuda)
target_link_libraries(torch PUBLIC ${TORCH_CUDA_LIBRARIES})
target_include_directories(
torch INTERFACE $<INSTALL_INTERFACE:include>)
target_include_directories(
torch PRIVATE ${Caffe2_GPU_INCLUDE})
target_link_libraries(
torch PRIVATE ${Caffe2_CUDA_DEPENDENCY_LIBS})
# These public dependencies must go after the previous dependencies, as the