torch-mlir/CMakeLists.txt

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cmake_minimum_required(VERSION 3.10)
if(POLICY CMP0068)
cmake_policy(SET CMP0068 NEW)
set(CMAKE_BUILD_WITH_INSTALL_NAME_DIR ON)
endif()
if(POLICY CMP0075)
cmake_policy(SET CMP0075 NEW)
endif()
if(POLICY CMP0077)
cmake_policy(SET CMP0077 NEW)
endif()
#-------------------------------------------------------------------------------
# Project setup and globals
#-------------------------------------------------------------------------------
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project(npcomp LANGUAGES CXX C)
set(CMAKE_C_STANDARD 11)
set(CMAKE_CXX_STANDARD 14)
list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake/modules")
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#-------------------------------------------------------------------------------
# Options and settings
#-------------------------------------------------------------------------------
option(NPCOMP_ENABLE_IREE "Enables the IREE backend (must configure location via IREE_DIR)." OFF)
option(NPCOMP_ENABLE_REFJIT "Enables the reference JIT backend." ON)
option(NPCOMP_BUILD_NPCOMP_DYLIB "Enables shared build of NPCOMP dylib (depends on LLVM/MLIR dylib support)" ON)
set(NPCOMP_IREE_SRCDIR "" CACHE STRING "If building IREE, then setting this elects to build from a source directory (versus installed package)")
#-------------------------------------------------------------------------------
# MSVC defaults
#-------------------------------------------------------------------------------
if(MSVC)
add_compile_options(
$<$<CONFIG:>:/MD>
$<$<CONFIG:Debug>:/MD>
$<$<CONFIG:Release>:/MD>
)
endif()
#-------------------------------------------------------------------------------
# MLIR/LLVM Configuration
#-------------------------------------------------------------------------------
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find_package(MLIR REQUIRED CONFIG)
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message(STATUS "Using MLIRConfig.cmake in: ${MLIR_DIR}")
message(STATUS "Using LLVMConfig.cmake in: ${LLVM_DIR}")
#set(LLVM_RUNTIME_OUTPUT_INTDIR ${CMAKE_BINARY_DIR}/bin)
set(LLVM_LIBRARY_OUTPUT_INTDIR ${CMAKE_BINARY_DIR}/lib)
# Define the default arguments to use with 'lit', and an option for the user to
# override.
set(LIT_ARGS_DEFAULT "-sv")
if (MSVC_IDE OR XCODE)
set(LIT_ARGS_DEFAULT "${LIT_ARGS_DEFAULT} --no-progress-bar")
endif()
set(LLVM_LIT_ARGS "${LIT_ARGS_DEFAULT}" CACHE STRING "Default options for lit")
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list(APPEND CMAKE_MODULE_PATH "${MLIR_CMAKE_DIR}")
list(APPEND CMAKE_MODULE_PATH "${LLVM_CMAKE_DIR}")
include(TableGen)
include(AddLLVM)
include(AddMLIR)
include(AddNPCOMP)
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include(HandleLLVMOptions)
include_directories(${LLVM_INCLUDE_DIRS})
include_directories(${MLIR_INCLUDE_DIRS})
include_directories(${PROJECT_SOURCE_DIR}/include)
include_directories(${PROJECT_BINARY_DIR}/include)
link_directories(${LLVM_BUILD_LIBRARY_DIR})
add_definitions(${LLVM_DEFINITIONS})
set(NPCOMP_TABLEGEN_ARGS "")
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#-------------------------------------------------------------------------------
# Optional feature selection
#-------------------------------------------------------------------------------
if(NPCOMP_ENABLE_REFJIT)
add_compile_definitions(NPCOMP_ENABLE_REFJIT)
message(STATUS "Reference JIT backend enabled")
endif()
#-------------------------------------------------------------------------------
# IREE configuration
#-------------------------------------------------------------------------------
function(npcomp_add_iree_src)
# TODO: Find a way to fix this upstream as globally demoting warnings
# like this is a really bad thing to be doing. Also, Abseil seems to try
# really hard to keep us from touching these so some still get through.
add_compile_options(-Wno-sign-compare -Wno-unused-template)
add_subdirectory("${NPCOMP_IREE_SRCDIR}" "iree" EXCLUDE_FROM_ALL)
endfunction()
if(NPCOMP_ENABLE_IREE)
add_compile_definitions(NPCOMP_ENABLE_IREE)
string(APPEND NPCOMP_TABLEGEN_ARGS "-DNPCOMP_ENABLE_IREE")
if(NPCOMP_IREE_SRCDIR)
message(STATUS "Depending on IREE source: ${NPCOMP_IREE_SRCDIR}")
set(IREE_BUILD_TESTS OFF CACHE BOOL "Override IREE setting" FORCE)
set(IREE_BUILD_SAMPLES OFF CACHE BOOL "Override IREE setting" FORCE)
set(IREE_BUILD_PYTHON_BINDINGS ON CACHE BOOL "Override IREE setting" FORCE)
set(IREE_MLIR_DEP_MODE "DISABLED" CACHE STRING "Override IREE setting")
npcomp_add_iree_src()
else()
find_package(IREE REQUIRED CONFIG)
endif()
message(STATUS "IREE INCLUDE DIRS: ${IREE_PUBLIC_INCLUDE_DIRS}")
include_directories("${IREE_PUBLIC_INCLUDE_DIRS}")
endif()
#-------------------------------------------------------------------------------
# Python Configuration
#-------------------------------------------------------------------------------
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set(NPCOMP_PYTHON_BINDINGS_VERSION_LOCKED 1 CACHE BOOL
"Links to specific python libraries, resolving all symbols.")
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# TODO(laurenzo): Rationalize with how this is done elsewhere
find_package(PythonInterp REQUIRED)
find_package(PythonLibs)
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message(STATUS "Found python include dirs: ${PYTHON_INCLUDE_DIRS}")
message(STATUS "Found ppython libraries: ${PYTHON_LIBRARIES}")
find_package(pybind11 CONFIG REQUIRED)
message(STATUS "Found pybind11 v${pybind11_VERSION}: ${pybind11_INCLUDE_DIRS}")
Add pytorch interface to ATen Dialect (#30) This patch adds a pytorch interface to npcomp. This interface is modeled after pytorch_xla and exposes the MLIR-based flow as a virtual device (similar to a gpu device or the xla backend). Usage is intended to be something like: dev = torch_mlir.mlir_device() t0 = torch.randn((4,4), device=dev) t1 = torch.randn((4,4), device=dev) t2 = t0 + t1 t2_mlir = torch_mlir.get_mlir( t2 ) t2_cpu = t2.to('cpu') In this case t2_cpu would contain the result of the computation, and t2_mlir contains the mlir description of the computation. Note that this also properly returns backward paths synthesized by pytorch. There are several parts of this: 1) A tensor type (implemented by tensor.* and tensor_impl.*) 2) The device modeling (aten_mlir_bridge.*, aten_mlir_device.*, aten_mlir_type*) 3) a temporary IR (implemented by ir.cpp) There is also a reference lowering directly from the ATen dialect to C function calls consisting of two parts: 1) The driver that uses the IR to generate MLIR, run Passes and compile the result using mlir::ExecutionEngine (implemented by jit.cpp and mlir_gen.cpp) 2) A runtime library implemented by lib/aten_ops.cpp. Most of the operations are implemented by callbacks into the torch C++ libraries. Some aspects of this are known to be less than optimal, in particular: 1) There's some function definitions that don't live in the file corresponding to their declaration. 2) More aspects of this (e.g. the IR) seem like they should be automatically generated. 3) It's unclear to me how much of the 'IR' is actually necessary, or whether MLIR could be created on the fly. Note that this code is licensed in a way similar to pytorch, with the intention that eventually (when npcomp reaches some maturity) it should be pushed there. (see frontends/pytorch/LICENSE) The code is also structured much closer to the pytorch coding style than the LLVM coding style.
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#-------------------------------------------------------------------------------
# Pytorch Configuration
#-------------------------------------------------------------------------------
find_package(Torch)
#-------------------------------------------------------------------------------
# Directory setup
#-------------------------------------------------------------------------------
set(MLIR_NPCOMP_SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR})
set(MLIR_NPCOMP_BINARY_DIR ${CMAKE_CURRENT_BINARY_DIR})
add_custom_target(check-npcomp)
Add pytorch interface to ATen Dialect (#30) This patch adds a pytorch interface to npcomp. This interface is modeled after pytorch_xla and exposes the MLIR-based flow as a virtual device (similar to a gpu device or the xla backend). Usage is intended to be something like: dev = torch_mlir.mlir_device() t0 = torch.randn((4,4), device=dev) t1 = torch.randn((4,4), device=dev) t2 = t0 + t1 t2_mlir = torch_mlir.get_mlir( t2 ) t2_cpu = t2.to('cpu') In this case t2_cpu would contain the result of the computation, and t2_mlir contains the mlir description of the computation. Note that this also properly returns backward paths synthesized by pytorch. There are several parts of this: 1) A tensor type (implemented by tensor.* and tensor_impl.*) 2) The device modeling (aten_mlir_bridge.*, aten_mlir_device.*, aten_mlir_type*) 3) a temporary IR (implemented by ir.cpp) There is also a reference lowering directly from the ATen dialect to C function calls consisting of two parts: 1) The driver that uses the IR to generate MLIR, run Passes and compile the result using mlir::ExecutionEngine (implemented by jit.cpp and mlir_gen.cpp) 2) A runtime library implemented by lib/aten_ops.cpp. Most of the operations are implemented by callbacks into the torch C++ libraries. Some aspects of this are known to be less than optimal, in particular: 1) There's some function definitions that don't live in the file corresponding to their declaration. 2) More aspects of this (e.g. the IR) seem like they should be automatically generated. 3) It's unclear to me how much of the 'IR' is actually necessary, or whether MLIR could be created on the fly. Note that this code is licensed in a way similar to pytorch, with the intention that eventually (when npcomp reaches some maturity) it should be pushed there. (see frontends/pytorch/LICENSE) The code is also structured much closer to the pytorch coding style than the LLVM coding style.
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add_custom_target(check-all)
add_dependencies(check-all check-npcomp)
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add_subdirectory(include/npcomp)
add_subdirectory(lib)
add_subdirectory(python)
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add_subdirectory(test)
Add pytorch interface to ATen Dialect (#30) This patch adds a pytorch interface to npcomp. This interface is modeled after pytorch_xla and exposes the MLIR-based flow as a virtual device (similar to a gpu device or the xla backend). Usage is intended to be something like: dev = torch_mlir.mlir_device() t0 = torch.randn((4,4), device=dev) t1 = torch.randn((4,4), device=dev) t2 = t0 + t1 t2_mlir = torch_mlir.get_mlir( t2 ) t2_cpu = t2.to('cpu') In this case t2_cpu would contain the result of the computation, and t2_mlir contains the mlir description of the computation. Note that this also properly returns backward paths synthesized by pytorch. There are several parts of this: 1) A tensor type (implemented by tensor.* and tensor_impl.*) 2) The device modeling (aten_mlir_bridge.*, aten_mlir_device.*, aten_mlir_type*) 3) a temporary IR (implemented by ir.cpp) There is also a reference lowering directly from the ATen dialect to C function calls consisting of two parts: 1) The driver that uses the IR to generate MLIR, run Passes and compile the result using mlir::ExecutionEngine (implemented by jit.cpp and mlir_gen.cpp) 2) A runtime library implemented by lib/aten_ops.cpp. Most of the operations are implemented by callbacks into the torch C++ libraries. Some aspects of this are known to be less than optimal, in particular: 1) There's some function definitions that don't live in the file corresponding to their declaration. 2) More aspects of this (e.g. the IR) seem like they should be automatically generated. 3) It's unclear to me how much of the 'IR' is actually necessary, or whether MLIR could be created on the fly. Note that this code is licensed in a way similar to pytorch, with the intention that eventually (when npcomp reaches some maturity) it should be pushed there. (see frontends/pytorch/LICENSE) The code is also structured much closer to the pytorch coding style than the LLVM coding style.
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add_subdirectory(frontends)
# Tools needs to come late to ensure that NPCOMP_ALL_LIBS is populated.
# Generally things after this point may depend on NPCOMP_ALL_LIBS or libNPCOMP.so.
add_subdirectory(tools)