torch-mlir/frontends/pytorch/csrc/CMakeLists.txt

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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.
2020-08-22 02:22:47 +08:00
include_directories(
${TORCH_INCLUDE_DIRS}
${TORCH_INSTALL_PREFIX}/include/TH
${TORCH_INSTALL_PREFIX}/include/THC/opt/pytorch/pytorch
${CMAKE_CURRENT_SOURCE_DIR}
${CMAKE_CURRENT_BINARY_DIR}
${PYTHON_INCLUDE_DIRS}
)
link_directories("${TORCH_INSTALL_PREFIX}/lib")
add_library(_torch_mlir SHARED
aten_mlir_bridge.cpp
aten_mlir_type.cpp
aten_mlir_type_default.cpp
device.cpp
init_python_bindings.cpp
ir.cpp
jit.cpp
mlir_gen.cpp
tensor.cpp
tensor_impl.cpp
torch_util.cpp
)
set_target_properties(_torch_mlir PROPERTIES PREFIX "")
get_property(mlir_libs GLOBAL PROPERTY MLIR_ALL_LIBS)
target_link_libraries(_torch_mlir
NPCOMPATenDialect
${TORCH_LIBRARIES}
${mlir_libs}
${PYTHON_LIBRARIES}
torch_python
)