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Add support for math.ctlz in convert-math-to-funcs This change adds a software implementation of the math.ctlz operation and includes it in --convert-math-to-funcs. This is my first change to MLIR, so please bear with me as I'm still learning the idioms of the codebase. The context for this change is that I have some larger scale project in which I'd like to lower from a mix of MLIR dialects to CIRCT, but many of the CIRCT passes don't support the math dialect. I noticed the content of convert-math-to-funcs was limited entirely to the pow functions, but otherwise provided the needed structure to implement this feature with minimal changes. Highlight of the changes: - Add a dependence on the SCF dialect for this lower. I could have lowered directly to cf, following the pow lowerings in the same pass, but I felt it was not necessary given the existing support for lowering scf to cf. - Generalize the DenseMap storing op implementations: modify the callback function hashmap to be keyed by both OperationType (for me this effectively means the name of the op being implemented in software) and the type signature of the resulting function. - Implement the ctlz function as a loop. I had researched a variety of implementations that claimed to be more efficient (such as those based on a de Bruijn sequence), but it seems to me that the simplest approach would make it easier for later compiler optimizations to do a better (platform-aware) job optimizing this than I could do by hand. Questions I had for the reviewer: - [edit: found mlir-cpu-runner and added two tests] What would I add to the filecheck invocation to actually run the resulting MLIR on a value and assert the output is correct? I have done this manually with the C implementation but I'm not confident my port to MLIR is correct. - Should I add a test for a vectorized version of this lowering? I followed suit with the VecOpToScalarOp but I admit I don't fully understand what it's doing. Reviewed By: vzakhari Differential Revision: https://reviews.llvm.org/D146261 | 3 年前 | |
[mlir] Move casting calls from methods to function calls The MLIR classes Type/Attribute/Operation/Op/Value support cast/dyn_cast/isa/dyn_cast_or_null functionality through llvm's doCast functionality in addition to defining methods with the same name. This change begins the migration of uses of the method to the corresponding function call as has been decided as more consistent. Note that there still exist classes that only define methods directly, such as AffineExpr, and this does not include work currently to support a functional cast/isa call. Caveats include: - This clang-tidy script probably has more problems. - This only touches C++ code, so nothing that is being generated. Context: - https://mlir.llvm.org/deprecation/ at "Use the free function variants for dyn_cast/cast/isa/…" - Original discussion at https://discourse.llvm.org/t/preferred-casting-style-going-forward/68443 Implementation: This first patch was created with the following steps. The intention is to only do automated changes at first, so I waste less time if it's reverted, and so the first mass change is more clear as an example to other teams that will need to follow similar steps. Steps are described per line, as comments are removed by git: 0. Retrieve the change from the following to build clang-tidy with an additional check: https://github.com/llvm/llvm-project/compare/main...tpopp:llvm-project:tidy-cast-check 1. Build clang-tidy 2. Run clang-tidy over your entire codebase while disabling all checks and enabling the one relevant one. Run on all header files also. 3. Delete .inc files that were also modified, so the next build rebuilds them to a pure state. 4. Some changes have been deleted for the following reasons: - Some files had a variable also named cast - Some files had not included a header file that defines the cast functions - Some files are definitions of the classes that have the casting methods, so the code still refers to the method instead of the function without adding a prefix or removing the method declaration at the same time. ninja -C $BUILD_DIR clang-tidy run-clang-tidy -clang-tidy-binary=$BUILD_DIR/bin/clang-tidy -checks='-*,misc-cast-functions'\ -header-filter=mlir/ mlir/* -fix rm -rf $BUILD_DIR/tools/mlir/**/*.inc git restore mlir/lib/IR mlir/lib/Dialect/DLTI/DLTI.cpp\ mlir/lib/Dialect/Complex/IR/ComplexDialect.cpp\ mlir/lib/**/IR/\ mlir/lib/Dialect/SparseTensor/Transforms/SparseVectorization.cpp\ mlir/lib/Dialect/Vector/Transforms/LowerVectorMultiReduction.cpp\ mlir/test/lib/Dialect/Test/TestTypes.cpp\ mlir/test/lib/Dialect/Transform/TestTransformDialectExtension.cpp\ mlir/test/lib/Dialect/Test/TestAttributes.cpp\ mlir/unittests/TableGen/EnumsGenTest.cpp\ mlir/test/python/lib/PythonTestCAPI.cpp\ mlir/include/mlir/IR/ Differential Revision: https://reviews.llvm.org/D150123 | 3 年前 |
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| 3 年前 |