torch-mlir/lib/Dialect/TMTensor/IR/CMakeLists.txt

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CMake
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add_mlir_library(TorchMLIRTMTensorDialect
TMTensorDialect.cpp
TMTensorInterfaces.cpp
TMTensorOps.cpp
ScalarLoopOpInterface.cpp
ADDITIONAL_HEADER_DIRS
${TORCH_MLIR_DIALECTS_SOURCE_DIR}/include
DEPENDS
TorchMLIRTMTensorOpsIncGen
LINK_LIBS PUBLIC
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MLIRAffineDialect
MLIRDialectUtils
MLIRIR
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MLIRLinalgDialect
MLIRMathDialect
MLIRMemRefDialect
MLIRPass
MLIRSideEffectInterfaces
MLIRSupport
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MLIRSCFDialect
MLIRFuncDialect
MLIRTensorDialect
MLIRViewLikeInterface
)
Re-organize project structure to separate PyTorch dependencies from core project. (#2542) This is a first step towards the structure we discussed here: https://gist.github.com/stellaraccident/931b068aaf7fa56f34069426740ebf20 There are two primary goals: 1. Separate the core project (C++ dialects and conversions) from the hard PyTorch dependencies. We move all such things into projects/pt1 as a starting point since they are presently entangled with PT1-era APIs. Additional work can be done to disentangle components from that (specifically LTC is identified as likely ultimately living in a `projects/ltc`). 2. Create space for native PyTorch2 Dynamo-based infra to be upstreamed without needing to co-exist with the original TorchScript path. Very little changes in this path with respect to build layering or options. These can be updated in a followup without commingling directory structure changes. This also takes steps toward a couple of other layering enhancements: * Removes the llvm-external-projects/torch-mlir-dialects sub-project, collapsing it into the main tree. * Audits and fixes up the core C++ build to account for issues found while moving things. This is just an opportunistic pass through but roughly ~halves the number of build actions for the project from the high 4000's to the low 2000's. It deviates from the discussed plan by having a `projects/` tree instead of `compat/`. As I was thinking about it, this will better accommodate the follow-on code movement. Once things are roughly in place and the CI passing, followups will focus on more in-situ fixes and cleanups.
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torch_mlir_target_includes(TorchMLIRTMTensorDialect)