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llvm-project/mlir/tools/mlir-opt/CMakeLists.txt

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Parsing support for Range, View and Slice operations This CL implements the previously unsupported parsing for Range, View and Slice operations. A pass is introduced to lower to the LLVM. Tests are moved out of C++ land and into mlir/test/Examples. This allows better fitting within standard developer workflows. -- PiperOrigin-RevId: 245796600
2019-04-30 03:11:58 +08:00
set(LLVM_OPTIONAL_SOURCES
null.cpp
)
set(LIB_LIBS
MLIRAnalysis
MLIRLLVMIR
MLIRParser
MLIRPass
MLIRTransforms
MLIRSupport
)
add_llvm_library(MLIRMlirOptLib
mlir-opt.cpp
)
target_link_libraries(MLIRMlirOptLib ${LIB_LIBS})
Add build files and update README. * Add initial version of build files; * Update README with instructions to download and build MLIR from github; -- PiperOrigin-RevId: 241102092
2019-03-30 13:10:12 +08:00
set(LIBS
Move Linalg Transforms that are actually Conversions - NFC PiperOrigin-RevId: 281844602
2019-11-22 07:19:52 +08:00
MLIRAnalysis
Add build files and update README. * Add initial version of build files; * Update README with instructions to download and build MLIR from github; -- PiperOrigin-RevId: 241102092
2019-03-30 13:10:12 +08:00
MLIRAffineOps
Move Affine to Standard conversion to lib/Conversion This is essentially a dialect conversion and conceptually belongs to conversions. PiperOrigin-RevId: 280460034
2019-11-15 02:34:46 +08:00
MLIRAffineToStandard
Extend AffineToGPU to support Linalg loops Extend the utility that converts affine loop nests to support other types of loops by abstracting away common behavior through templates. This also slightly simplifies the existing Affine to GPU conversion by always passing in the loop step as an additional kernel argument even though it is a known constant. If it is used, it will be propagated into the loop body by the existing canonicalization pattern and can be further constant-folded, otherwise it will be dropped by canonicalization. This prepares for the common loop abstraction that will be used for converting to GPU kernels, which is conceptually close to Linalg loops, while maintaining the existing conversion operational. PiperOrigin-RevId: 257172216
2019-07-09 20:26:18 +08:00
MLIRLoopsToGPU
Move Linalg Transforms that are actually Conversions - NFC PiperOrigin-RevId: 281844602
2019-11-22 07:19:52 +08:00
MLIRLinalgToLLVM
Refactor dependencies to expose Vector transformations as patterns - NFC This CL refactors some of the MLIR vector dependencies to allow decoupling VectorOps, vector analysis, vector transformations and vector conversions from each other. This makes the system more modular and allows extracting VectorToVector into VectorTransforms that do not depend on vector conversions. This refactoring exhibited a bunch of cyclic library dependencies that have been cleaned up. PiperOrigin-RevId: 283660308
2019-12-04 09:51:34 +08:00
NFC: rename Conversion/ControlFlowToCFG to Conversion/LoopToStandard This makes the name of the conversion pass more consistent with the naming scheme, since it actually converts from the Loop dialect to the Standard dialect rather than working with arbitrary control flow operations. PiperOrigin-RevId: 272612112
2019-10-03 16:34:16 +08:00
MLIRLoopToStandard
Add build files and update README. * Add initial version of build files; * Update README with instructions to download and build MLIR from github; -- PiperOrigin-RevId: 241102092
2019-03-30 13:10:12 +08:00
MLIREDSC
Add CMakeLists.txt for Quantization and FxpMathOps dialects (and misc fixes to build with OSS setup). Tested: cmake -G Ninja ../llvm/ -DLLVM_BUILD_EXAMPLES=ON ninja check-mlir -- PiperOrigin-RevId: 241841296
2019-04-04 08:43:00 +08:00
MLIRFxpMathOps
Fixup MLIRGPU to mlir-opt in cmake. -- PiperOrigin-RevId: 245820077
2019-04-30 05:10:08 +08:00
MLIRGPU
Add lowering pass from GPU dialect operations to LLVM/NVVM intrinsics. PiperOrigin-RevId: 253551452
2019-06-17 18:56:20 +08:00
MLIRGPUtoNVVMTransforms
[ROCm] Adding pass to lower GPU Dialect to ROCDL Dialect. This is a follow-up to the PRtensorflow/mlir#146 which introduced the ROCDL Dialect. This PR introduces a pass to lower GPU Dialect to the ROCDL Dialect. As with the previous PR, this one builds on the work done by @whchung, and addresses most of the review comments in the original PR. Closes tensorflow/mlir#154 COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/154 from deven-amd:deven-lower-gpu-to-rocdl 809893e08236da5ab6a38e3459692fa04247773d PiperOrigin-RevId: 272390729
2019-10-02 16:50:03 +08:00
MLIRGPUtoROCDLTransforms
Link in MLIRGPUtoSPIRVTransforms with mlir-opt Add a missed library that needs to be linked with mlir-opt. This results in a test failure in the MLIR due to the pass `-convert-gpu-to-spirv` not being found. PiperOrigin-RevId: 260773067
2019-07-31 03:39:09 +08:00
MLIRGPUtoSPIRVTransforms
Start a Linalg dialect This CL starts implementing a Linalg dialect with the objective of supporting optimizing compilation of loops and library calls for a subset of common linear algebra operations. This CL starts by simply adding a linalg.range type and an operation with the proper roundtripping test. -- PiperOrigin-RevId: 244189468
2019-04-18 23:25:54 +08:00
MLIRLinalg
Add build files and update README. * Add initial version of build files; * Update README with instructions to download and build MLIR from github; -- PiperOrigin-RevId: 241102092
2019-03-30 13:10:12 +08:00
MLIRLLVMIR
Extract std.for std.if and std.terminator in their own dialect These ops should not belong to the std dialect. This CL extracts them in their own dialect and updates the corresponding conversions and tests. PiperOrigin-RevId: 258123853
2019-07-15 17:50:09 +08:00
MLIRLoopOps
Fix opt build failure. -- PiperOrigin-RevId: 245844236
2019-04-30 07:12:15 +08:00
MLIRNVVMIR
Split out mlir-opt main into separate file. Enable reusing the real mlir-opt main from unit tests and in case where additional initialization needs to happen before main is invoked (e.g., when using different command line flag libraries). PiperOrigin-RevId: 254764575
2019-06-24 23:41:52 +08:00
MLIROptMain
Add build files and update README. * Add initial version of build files; * Update README with instructions to download and build MLIR from github; -- PiperOrigin-RevId: 241102092
2019-03-30 13:10:12 +08:00
MLIRParser
MLIRPass
Upstream the Quantizer tool (part 4). This adds the basic passes needed and ties them into mlir-opt. Also adds two specific unit tests that exercise them. Next step is a standalone quantizer tool and additional cleanup. Tested: ninja check-mlir -- PiperOrigin-RevId: 249167690
2019-05-21 09:27:38 +08:00
MLIRQuantizerTransforms
Move Quantization -> Dialect/QuantOps, FxpMathOps -> Dialect/FxpMathOps. Adding the additional layer of directory was discussed offline and matches the Target/ tree. The names match the defacto convention we seem to be following where the C++ namespace is ^(.+)Ops/$ matched against the directory name. This is in preparation for patching the Quantizer into this tree, which would have been confusing without moving the Quantization dialect to its more proper home. It is left to others to move other dialects if desired. Tested: ninja check-mlir -- PiperOrigin-RevId: 248171982
2019-05-15 02:03:55 +08:00
MLIRQuantOps
[ROCm] Adding ROCDL Dialect. This commit introduces the ROCDL Dialect (i.e. the ROCDL ops + the code to lower those ROCDL ops to LLWM intrinsics/functions). Think of ROCDL Dialect as analogous to the NVVM Dialect, but for AMD GPUs. This patch contains just the essentials needed to get a simple example up and running. We expect to make further additions to the ROCDL Dialect. This is the first of 3 commits, the follow-up will be: * add a pass that lowers GPU Dialect to ROCDL Dialect * add a "mlir-rocm-runner" utility Closes tensorflow/mlir#146 COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/146 from deven-amd:deven-rocdl-dialect e78e8005c75a78912631116c78dc844fcc4b0de9 PiperOrigin-RevId: 271511259
2019-09-27 14:49:51 +08:00
MLIRROCDLIR
[spirv] Start the SPIR-V dialect This CL sets up the basic structure for a SPIR-V dialect: operation definition specification, dialect registration, testing, etc. A single op, FMul, is defined and tested to showcase. The SPIR-V dialect aims to be a simple proxy for the SPIR-V binary format to enable straightforward and lightweight conversion from/to the binary format. Ops in this dialect should stay as the same semantic level and try to be a mechanical mapping to the corresponding SPIR-V instructions; but they can deviate representationally to allow using MLIR mechanisms. -- PiperOrigin-RevId: 250040830
2019-05-26 20:43:20 +08:00
MLIRSPIRV
NFC: Refactor Dialect Conversion targeting SPIR-V. Refactoring the conversion from StandardOps/GPU dialect to SPIR-V dialect: 1) Move the SPIRVTypeConversion and SPIRVOpLowering class into SPIR-V dialect. 2) Add header files that expose functions to add patterns for the dialects to SPIR-V lowering, as well as a pass that does the dialect to SPIR-V lowering. 3) Make SPIRVOpLowering derive from OpLowering class. PiperOrigin-RevId: 280486871
2019-11-15 04:31:32 +08:00
MLIRStandardToSPIRVTransforms
[spirv] Add a pass to decorate the composite types with layout info. Add a pass to decorate the composite types used by composite objects in the StorageBuffer, PhysicalStorageBuffer, Uniform, and PushConstant storage classes with layout information. Closes tensorflow/mlir#156 COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/156 from denis0x0D:sandbox/layout_info_decoration 7c50840fd38ca169a2da7ce9886b52b50c868b84 PiperOrigin-RevId: 273634140
2019-10-09 07:42:38 +08:00
MLIRSPIRVTransforms
Add build files and update README. * Add initial version of build files; * Update README with instructions to download and build MLIR from github; -- PiperOrigin-RevId: 241102092
2019-03-30 13:10:12 +08:00
MLIRStandardOps
Start moving conversions to {lib,include/mlir}/Conversion Conversions from dialect A to dialect B depend on both A and B. Therefore, it is reasonable for them to live in a separate library that depends on both DialectA and DialectB library, and does not forces dependees of DialectA or DialectB to also link in the conversion. Create the directory layout for the conversions and move the Standard to LLVM dialect conversion as the first example. PiperOrigin-RevId: 253312252
2019-06-15 06:54:21 +08:00
MLIRStandardToLLVM
Add build files and update README. * Add initial version of build files; * Update README with instructions to download and build MLIR from github; -- PiperOrigin-RevId: 241102092
2019-03-30 13:10:12 +08:00
MLIRTransforms
Split test-specific passes out of mlir-opt Instead put their impl in test/lib and link them into mlir-test-opt PiperOrigin-RevId: 254837439
2019-06-25 05:35:21 +08:00
MLIRTestDialect
Add support for walking the uses of a symbol. MLIR uses symbol references to model references to many global entities, such as functions/variables/etc. Before this change, there is no way to actually reason about the uses of such entities. This change provides a walker for symbol references(via SymbolTable::walkSymbolUses), as well as 'use_empty' support(via SymbolTable::symbol_use_empty). It also resolves some deficiencies in the LangRef definition of SymbolRefAttr, namely the restrictions on where a SymbolRefAttr can be stored, ArrayAttr and DictionaryAttr, and the relationship with operations containing the SymbolTable trait. PiperOrigin-RevId: 273549331
2019-10-09 01:21:26 +08:00
MLIRTestIR
Refactor PassTiming to support nested pipelines. This is done via a new set of instrumentation hooks runBeforePipeline/runAfterPipeline, that signal the lifetime of a pass pipeline on a specific operation type. These hooks also provide the parent thread of the pipeline, allowing for accurate merging of timers running on different threads. PiperOrigin-RevId: 267909193
2019-09-09 10:57:25 +08:00
MLIRTestPass
Split test-specific passes out of mlir-opt Instead put their impl in test/lib and link them into mlir-test-opt PiperOrigin-RevId: 254837439
2019-06-25 05:35:21 +08:00
MLIRTestTransforms
Add build files and update README. * Add initial version of build files; * Update README with instructions to download and build MLIR from github; -- PiperOrigin-RevId: 241102092
2019-03-30 13:10:12 +08:00
MLIRSupport
MLIRVectorOps
Refactor dependencies to expose Vector transformations as patterns - NFC This CL refactors some of the MLIR vector dependencies to allow decoupling VectorOps, vector analysis, vector transformations and vector conversions from each other. This makes the system more modular and allows extracting VectorToVector into VectorTransforms that do not depend on vector conversions. This refactoring exhibited a bunch of cyclic library dependencies that have been cleaned up. PiperOrigin-RevId: 283660308
2019-12-04 09:51:34 +08:00
MLIRVectorToLLVM
MLIRVectorToLoops
Add build files and update README. * Add initial version of build files; * Update README with instructions to download and build MLIR from github; -- PiperOrigin-RevId: 241102092
2019-03-30 13:10:12 +08:00
)
Make GPU to CUDA transformations independent of CUDA runtime. The actual transformation from PTX source to a CUDA binary is now factored out, enabling compiling and testing the transformations independently of a CUDA runtime. MLIR has still to be built with NVPTX target support for the conversions to be built and tested. PiperOrigin-RevId: 255167139
2019-06-26 20:16:11 +08:00
if(MLIR_CUDA_CONVERSIONS_ENABLED)
list(APPEND LIBS
MLIRGPUtoCUDATransforms
)
endif()
Add some CMake rules for installing headers, mlir-tblgen, and mlir-opt Closes tensorflow/mlir#246 PiperOrigin-RevId: 281442685
2019-11-20 13:04:45 +08:00
add_llvm_tool(mlir-opt
Parsing support for Range, View and Slice operations This CL implements the previously unsupported parsing for Range, View and Slice operations. A pass is introduced to lower to the LLVM. Tests are moved out of C++ land and into mlir/test/Examples. This allows better fitting within standard developer workflows. -- PiperOrigin-RevId: 245796600
2019-04-30 03:11:58 +08:00
mlir-opt.cpp
Add build files and update README. * Add initial version of build files; * Update README with instructions to download and build MLIR from github; -- PiperOrigin-RevId: 241102092
2019-03-30 13:10:12 +08:00
)
Fix MacOS build This is making up for some differences in standard library and linker flags. It also get rid of the requirement to build with RTTI. -- PiperOrigin-RevId: 241348845
2019-04-02 01:17:45 +08:00
llvm_update_compile_flags(mlir-opt)
Add build files and update README. * Add initial version of build files; * Update README with instructions to download and build MLIR from github; -- PiperOrigin-RevId: 241102092
2019-03-30 13:10:12 +08:00
whole_archive_link(mlir-opt ${LIBS})
Add keywords in target_link_libraries post add_llvm_executable. -- PiperOrigin-RevId: 250704528
2019-05-31 00:42:17 +08:00
target_link_libraries(mlir-opt PRIVATE MLIRIR MLIRMlirOptLib ${LIBS} LLVMSupport)