MMarkus Böck[mlir] Improve syntax of distinct[n]<unit>
| 文件 | 最后提交记录 | 最后更新时间 |
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[mlir][affineexpr] Changing AsmParser::parseAffineExpr API to use ArrayRef The methods added by D154177 don't require the symbolSet parameter to be mutable nor to have the SmallVectorImpl type, so this commit changes them to accept ArrayRef instead: both for generality, and to make the non-mutation an explicit part of the API. Reviewed By: aartbik, Peiming Differential Revision: https://reviews.llvm.org/D154751 | 2 年前 | |
[mlir][affineexpr] Changing AsmParser::parseAffineExpr API to use ArrayRef The methods added by D154177 don't require the symbolSet parameter to be mutable nor to have the SmallVectorImpl type, so this commit changes them to accept ArrayRef instead: both for generality, and to make the non-mutation an explicit part of the API. Reviewed By: aartbik, Peiming Differential Revision: https://reviews.llvm.org/D154751 | 2 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 | |
[mlir] Improve syntax of distinct[n]<unit> In cases where memory is of less of a concern (e.g. small attributes where all instances have to be distinct by definition), using DistinctAttr with a unit attribute is a useful and conscious way of generating deterministic unique IDs. The syntax as is however, makes them less useful to use, as it 1) always prints <unit> at the back and 2) always aliases them leading to not very useful #distinct = distinct[n]<unit> lines in the printer output. This patch fixes that by special casing UnitAttr to simply elide the unit attribute in the back and not printing it as alias in that case. Differential Revision: https://reviews.llvm.org/D155162 | 2 年前 | |
[mlir] Refactor the Parser library in preparation for an MLIR binary format The current Parser library is solely focused on providing API for the textual MLIR format, but MLIR will soon also provide a binary format. This commit renames the current Parser library to AsmParser to better correspond to what the library is actually intended for. A new Parser library is added which will act as a unified parser interface between both text and binary formats. Most parser clients are unaffected, given that the unified interface is essentially the same as the current interface. Only clients that rely on utilizing the AsmParserState, or those that want to parse Attributes/Types need to be updated to point to the AsmParser library. Differential Revision: https://reviews.llvm.org/D129605 | 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 | |
[mlir] LLVM_FALLTHROUGH => [[fallthrough]]. NFC | 3 年前 | |
[mlir] Refactor the Parser library in preparation for an MLIR binary format The current Parser library is solely focused on providing API for the textual MLIR format, but MLIR will soon also provide a binary format. This commit renames the current Parser library to AsmParser to better correspond to what the library is actually intended for. A new Parser library is added which will act as a unified parser interface between both text and binary formats. Most parser clients are unaffected, given that the unified interface is essentially the same as the current interface. Only clients that rely on utilizing the AsmParserState, or those that want to parse Attributes/Types need to be updated to point to the AsmParser library. Differential Revision: https://reviews.llvm.org/D129605 | 3 年前 | |
[mlir] llvm::Optional::value => operator*/operator-> std::optional::value() has undesired exception checking semantics and is unavailable in older Xcode (see _LIBCPP_AVAILABILITY_BAD_OPTIONAL_ACCESS). The call sites block std::optional migration. | 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. 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 patch updates all remaining uses of the deprecated functionality in mlir/. This was done with clang-tidy as described below and further modifications to GPUBase.td and OpenMPOpsInterfaces.td. 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: 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. 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 Differential Revision: https://reviews.llvm.org/D151542 | 3 年前 | |
[mlir] Add a builtin distinct attribute A distinct attribute associates a referenced attribute with a unique identifier. Every call to its create function allocates a new distinct attribute instance. The address of the attribute instance temporarily serves as its unique identifier. Similar to the names of SSA values, the final unique identifiers are generated during pretty printing. Examples: #distinct = distinct[0]<42.0 : f32> #distinct1 = distinct[1]<42.0 : f32> #distinct2 = distinct[2]<array<i32: 10, 42>> This mechanism is meant to generate attributes with a unique identifier, which can be used to mark groups of operations that share a common properties such as if they are aliasing. The design of the distinct attribute ensures minimal memory footprint per distinct attribute since it only contains a reference to another attribute. All distinct attributes are stored outside of the storage uniquer in a thread local store that is part of the context. It uses one bump pointer allocator per thread to ensure distinct attributes can be created in-parallel. Reviewed By: rriddle, Dinistro, zero9178 Differential Revision: https://reviews.llvm.org/D153360 | 2 年前 | |
[mlir] Add a builtin distinct attribute A distinct attribute associates a referenced attribute with a unique identifier. Every call to its create function allocates a new distinct attribute instance. The address of the attribute instance temporarily serves as its unique identifier. Similar to the names of SSA values, the final unique identifiers are generated during pretty printing. Examples: #distinct = distinct[0]<42.0 : f32> #distinct1 = distinct[1]<42.0 : f32> #distinct2 = distinct[2]<array<i32: 10, 42>> This mechanism is meant to generate attributes with a unique identifier, which can be used to mark groups of operations that share a common properties such as if they are aliasing. The design of the distinct attribute ensures minimal memory footprint per distinct attribute since it only contains a reference to another attribute. All distinct attributes are stored outside of the storage uniquer in a thread local store that is part of the context. It uses one bump pointer allocator per thread to ensure distinct attributes can be created in-parallel. Reviewed By: rriddle, Dinistro, zero9178 Differential Revision: https://reviews.llvm.org/D153360 | 2 年前 | |
[mlir] Use std::optional instead of llvm::Optional (NFC) This patch replaces (llvm::|)Optional< with std::optional<. I'll post a separate patch to remove #include "llvm/ADT/Optional.h". This is part of an effort to migrate from llvm::Optional to std::optional: https://discourse.llvm.org/t/deprecating-llvm-optional-x-hasvalue-getvalue-getvalueor/63716 | 3 年前 | |
[mlir] Remove remaining uses of llvm::Optional (NFC) This patch removes one "using" declaration and #include "llvm/ADT/Optional.h". It keeps several "using" declarations in headers for downstream users. This is part of an effort to migrate from llvm::Optional to std::optional: https://discourse.llvm.org/t/deprecating-llvm-optional-x-hasvalue-getvalue-getvalueor/63716 | 3 年前 | |
[mlir] Add a builtin distinct attribute A distinct attribute associates a referenced attribute with a unique identifier. Every call to its create function allocates a new distinct attribute instance. The address of the attribute instance temporarily serves as its unique identifier. Similar to the names of SSA values, the final unique identifiers are generated during pretty printing. Examples: #distinct = distinct[0]<42.0 : f32> #distinct1 = distinct[1]<42.0 : f32> #distinct2 = distinct[2]<array<i32: 10, 42>> This mechanism is meant to generate attributes with a unique identifier, which can be used to mark groups of operations that share a common properties such as if they are aliasing. The design of the distinct attribute ensures minimal memory footprint per distinct attribute since it only contains a reference to another attribute. All distinct attributes are stored outside of the storage uniquer in a thread local store that is part of the context. It uses one bump pointer allocator per thread to ensure distinct attributes can be created in-parallel. Reviewed By: rriddle, Dinistro, zero9178 Differential Revision: https://reviews.llvm.org/D153360 | 2 年前 | |
[mlir] Add support for TF32 as a Builtin FloatType This diff adds support for TF32 as a Builtin floating point type. This supplements the recent addition of the TF32 semantic to the LLVM APFloat class by extending usage to MLIR. https://reviews.llvm.org/D151923 More information on the TF32 type can be found here: https://blogs.nvidia.com/blog/2020/05/14/tensorfloat-32-precision-format/ Reviewed By: jpienaar Differential Revision: https://reviews.llvm.org/D153705 | 2 年前 |