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path: root/mlir/lib/Bytecode/Writer/IRNumbering.cpp
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2025-06-09[mlir] Use *Map::try_emplace (NFC) (#143341)Kazu Hirata
- try_emplace(Key) is shorter than insert({Key, nullptr}). - try_emplace performs value initialization without value parameters. - We overwrite values on successful insertion anyway.
2024-07-15[mlir] Remove bytecode reader & writer header from interface. (#98920)Jacques Pienaar
Flagged some additional headers missing in process. Inspired by #98676
2024-04-10[mlir] Slightly optimize bytecode op numbering (#88310)Jeff Niu
If the bytecode encoding supports properties, then the dictionary attribute is always the raw dictionary attribute of the operation, regardless of what it contains. Otherwise, get the dictionary attribute from the op: if the op does not have properties, then it returns the raw dictionary, otherwise it returns the combined inherent and discardable attributes.
2024-01-04[mlir] don't use magic numbers in IRNumbering.cppAlex Zinenko
Bytecode versions have named constants that should be used instead of magic numbers.
2024-01-04[mlir] fix bytecode writer after c1eab57673ef3eb28Alex Zinenko
The change in c1eab57 fixed the behavior of `getDiscardableAttrDictionary` for ops that are not using properties to only return discardable attributes. Bytecode writer was relying on the wrong behavior and would assume all attributes are discardable, without appropriate testing. Fix that and add a test.
2023-10-31[mlir][bytecode] Implements back deployment capability for MLIR dialects ↵Matteo Franciolini
(#70724) When emitting bytecode, clients can specify a target dialect version to emit in `BytecodeWriterConfig`. This exposes a target dialect version to the DialectBytecodeWriter, which can be queried by name and used to back-deploy attributes, types, and properties.
2023-10-21Apply clang-tidy fixes for llvm-qualified-auto in IRNumbering.cpp (NFC)Mehdi Amini
2023-07-28Expose callbacks for encoding of types/attributesMatteo Franciolini
[mlir] Expose a mechanism to provide a callback for encoding types and attributes in MLIR bytecode. Two callbacks are exposed, respectively, to the BytecodeWriterConfig and to the ParserConfig. At bytecode parsing/printing, clients have the ability to specify a callback to be used to optionally read/write the encoding. On failure, fallback path will execute the default parsers and printers for the dialect. Testing shows how to leverage this functionality to support back-deployment and backward-compatibility usecases when roundtripping to bytecode a client dialect with type/attributes dependencies on upstream. Reviewed By: rriddle Differential Revision: https://reviews.llvm.org/D153383
2023-07-28Revert "Expose callbacks for encoding of types/attributes"Mehdi Amini
This reverts commit b299ec16661f653df66cdaf161cdc5441bc9803c. The authorship informations were incorrect.
2023-07-28Expose callbacks for encoding of types/attributesMehdi Amini
[mlir] Expose a mechanism to provide a callback for encoding types and attributes in MLIR bytecode. Two callbacks are exposed, respectively, to the BytecodeWriterConfig and to the ParserConfig. At bytecode parsing/printing, clients have the ability to specify a callback to be used to optionally read/write the encoding. On failure, fallback path will execute the default parsers and printers for the dialect. Testing shows how to leverage this functionality to support back-deployment and backward-compatibility usecases when roundtripping to bytecode a client dialect with type/attributes dependencies on upstream. Reviewed By: rriddle Differential Revision: https://reviews.llvm.org/D153383
2023-07-25[mlir:bytecode] Only visit the all regions path if the op has regionsRiver Riddle
Zero region operations return true for both isBeforeAllRegions and isAfterAllRegions when using WalkStage. The bytecode walk only expects region holding operations in the after regions path, so guard against that.
2023-07-25[mlir:bytecode] Support lazy loading dynamically isolated regionsRiver Riddle
We currently only support lazy loading for regions that statically implement the IsolatedFromAbove trait, but that limits the amount of operations that can be lazily loaded. This review lifts that restriction by computing which operations have isolated regions when numbering, allowing any operation to be lazily loaded as long as it doesn't use values defined above. Differential Revision: https://reviews.llvm.org/D156199
2023-07-24Update ODS variadic segments "magic" attributes to use native PropertiesMehdi Amini
The operand_segment_sizes and result_segment_sizes Attributes are now inlined in the operation as native propertie. We continue to support building an Attribute on the fly for `getAttr("operand_segment_sizes")` and setting the property from an attribute with `setAttr("operand_segment_sizes", attr)`. A new bytecode version is introduced to support backward compatibility and backdeployments. Differential Revision: https://reviews.llvm.org/D155919
2023-07-24Revert "Update ODS variadic segments "magic" attributes to use native ↵Mehdi Amini
Properties" This reverts commit 20b93abca6516bbb23689c3777536fea04e46e14. One python test is broken, WIP.
2023-07-24Update ODS variadic segments "magic" attributes to use native PropertiesMehdi Amini
The operand_segment_sizes and result_segment_sizes Attributes are now inlined in the operation as native propertie. We continue to support building an Attribute on the fly for `getAttr("operand_segment_sizes")` and setting the property from an attribute with `setAttr("operand_segment_sizes", attr)`. A new bytecode version is introduced to support backward compatibility and backdeployments. Differential Revision: https://reviews.llvm.org/D155919
2023-06-27[mlir][VectorType] Allow arbitrary dimensions to be scalableAndrzej Warzynski
At the moment, only the trailing dimensions in the vector type can be scalable, i.e. this is supported: vector<2x[4]xf32> and this is not allowed: vector<[2]x4xf32> This patch extends the vector type so that arbitrary dimensions can be scalable. To this end, an array of bool values is added to every vector type to denote whether the corresponding dimensions are scalable or not. For example, for this vector: vector<[2]x[3]x4xf32> the following array would be created: {true, true, false}. Additionally, the current syntax: vector<[2x3]x4xf32> is replaced with: vector<[2]x[3]x4xf32> This is primarily to simplify parsing (this way, the parser can easily process one dimension at a time rather than e.g. tracking whether "scalable block" has been entered/left). NOTE: The `isScalableDim` parameter of `VectorType` (introduced in this patch) makes `numScalableDims` redundant. For the time being, `numScalableDims` is preserved to facilitate the transition between the two parameters. `numScalableDims` will be removed in one of the subsequent patches. This change is a part of a larger effort to enable scalable vectorisation in Linalg. See this RFC for more context: * https://discourse.llvm.org/t/rfc-scalable-vectorisation-in-linalg/ Differential Revision: https://reviews.llvm.org/D153372
2023-05-26[MLIR] Add native Bytecode support for propertiesMehdi Amini
This is adding a new interface (`BytecodeOpInterface`) to allow operations to opt-in skipping conversion to attribute and serializing properties to native bytecode. The scheme relies on a new section where properties are stored in sequence { size, serialize_properties }, ... The operations are storing the index of a properties, a table of offset is built when loading the properties section the first time. This is a re-commit of 837d1ce0dc which conflicted with another patch upgrading the bytecode and the collision wasn't properly resolved before. Differential Revision: https://reviews.llvm.org/D151065
2023-05-25Revert "[MLIR] Add native Bytecode support for properties"Mehdi Amini
This reverts commit ca5a12fd69d4acf70c08f797cbffd714dd548348 and follow-up fixes: df34c288c428eb4b867c8075def48b3d1727d60b 07dc906883af660780cf6d0cc1044f7e74dab83e ab80ad0095083fda062c23ac90df84c40b4332c8 837d1ce0dc8eec5b17255291b3462e6296cb369b The first commit was incomplete and broken, I'll prepare a new version later, in the meantime pull this work out of tree.
2023-05-25[MLIR] Add native Bytecode support for propertiesMehdi Amini
This is adding a new interface (`BytecodeOpInterface`) to allow operations to opt-in skipping conversion to attribute and serializing properties to native bytecode. The scheme relies on a new section where properties are stored in sequence { size, serialize_properties }, ... The operations are storing the index of a properties, a table of offset is built when loading the properties section the first time. Back-deployment to version prior to 4 are relying on getAttrDictionnary() which we intend to deprecate and remove: that is putting a de-factor end-of-support horizon for supporting deployments to version older than 4. Differential Revision: https://reviews.llvm.org/D151065
2023-05-21Preserve use-list orders in mlir bytecodeMatteo Franciolini
This patch implements a mechanism to read/write use-list orders from/to the mlir bytecode format. When producing bytecode, use-list orders are appended to each value of the IR. When reading bytecode, use-lists orders are loaded in memory and used at the end of parsing to sort the existing use-list chains. Reviewed By: mehdi_amini Differential Revision: https://reviews.llvm.org/D149755
2023-05-12[mlir] Move casting calls from methods to function callsTres Popp
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
2023-04-29[mlir][bytecode] Allow client to specify a desired version.Jacques Pienaar
Add method to set a desired bytecode file format to generate. Change write method to be able to return status including the minimum bytecode version needed by reader. This enables generating an older version of the bytecode (not dialect ops, attributes or types). But this does not guarantee that an older version can always be generated, e.g., if a dialect uses a new encoding only available at later bytecode version. This clamps setting to at most current version. Differential Revision: https://reviews.llvm.org/D146555
2023-03-15[ADT][mlir][NFCI] Do not use non-const lvalue-refs with enumerateJakub Kuderski
Replace references to enumerate results with either result_pairs (reference wrapper type) or structured bindings. I did not use structured bindings everywhere as it wasn't clear to me it would improve readability. This is in preparation to the switch to zip semantics which won't support non-const lvalue reference to elements: https://reviews.llvm.org/D144503. I chose to use values instead of const lvalue-refs because MLIR is biased towards avoiding `const` local variables. This won't degrade performance because currently `result_pair` is cheap to copy (size_t + iterator), and in the future, the enumerator iterator dereference will return temporaries anyway. Reviewed By: dblaikie Differential Revision: https://reviews.llvm.org/D146006
2023-02-07[mlir][IRNumbering] Fix the dialect comparator to be strictRiver Riddle
Check if rhs is the dialect to be ordered first, ensuring that we don't inadvertantly order something before it by falling back to pure number comparison. This only shows up depending on the implementation of stable_sort. This was hit in a build of MSVC that was checking for strict ordering.
2023-01-16[llvm][ADT] Replace uses of `makeMutableArrayRef` with deduction guidesJoe Loser
Similar to how `makeArrayRef` is deprecated in favor of deduction guides, do the same for `makeMutableArrayRef`. Once all of the places in-tree are using the deduction guides for `MutableArrayRef`, we can mark `makeMutableArrayRef` as deprecated. Differential Revision: https://reviews.llvm.org/D141814
2022-09-13[mlir] Add bytecode encodings for the builtin ElementsAttr attributesRiver Riddle
This adds bytecode support for DenseArrayAttr, DenseIntOrFpElementsAttr, DenseStringElementsAttr, and SparseElementsAttr. Differential Revision: https://reviews.llvm.org/D133744
2022-09-13[mlir:Bytecode] Add support for encoding resourcesRiver Riddle
Resources are encoded in two separate sections similarly to attributes/types, one for the actual data and one for the data offsets. Unlike other sections, the resource sections are optional given that in many cases they won't be present. For testing, bytecode serialization is added for DenseResourceElementsAttr. Differential Revision: https://reviews.llvm.org/D132729
2022-08-26[mlir:Bytecode] Add encoding support for a majority of the builtin attributesRiver Riddle
This adds support for the non-location, non-elements, non-affine builtin attributes. Differential Revision: https://reviews.llvm.org/D132539
2022-08-23[mlir:Bytecode] Add initial support for dialect defined attribute/type encodingsRiver Riddle
Dialects can opt-in to providing custom encodings by implementing the `BytecodeDialectInterface`. This interface provides hooks, namely `readAttribute`/`readType` and `writeAttribute`/`writeType`, that will be used by the bytecode reader and writer. These hooks are provided a reader and writer implementation that can be used to encode various constructs in the underlying bytecode format. A unique feature of this interface is that dialects may choose to only encode a subset of their attributes and types in a custom bytecode format, which can simplify adding new or experimental components that aren't fully baked. Differential Revision: https://reviews.llvm.org/D132498
2022-08-22[mlir] Fix bots after bytecode support was added in D131747River Riddle
* Fix ambiguous Twine constructor call * Ensure shift is 64-bit (for MSVC) * Disable bytecode tests on s390x (we don't support big endian right now)
2022-08-22[mlir] Add initial support for a binary serialization formatRiver Riddle
This commit adds a new bytecode serialization format for MLIR. The actual serialization of MLIR to binary is relatively straightforward, given the very very general structure of MLIR. The underlying basis for this format is a variable-length encoding for integers, which gets heavily used for nearly all aspects of the encoding (given that most of the encoding is just indexing into lists). The format currently does not provide support for custom attribute/type serialization, and thus always uses an assembly format fallback. It also doesn't provide support for resources. These will be added in followups, the intention for this patch is to provide something that supports the basic cases, and can be built on top of. https://discourse.llvm.org/t/rfc-a-binary-serialization-format-for-mlir/63518 Differential Revision: https://reviews.llvm.org/D131747